Jurnal Teknokes
type: channel
publisher: Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia
language: en
publication name: Jurnal Teknokes
issn: 1907-7904
copyright: Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
items: Effect of phantom orientation angle on antenna sensor response in agar-based homogeneous phantom for abnormal breast tissue detection
items: Analysis of the Effect of Abnormal Tissue Size on the S11 Response of a Monopole Antenna Using a Realistic Heterogeneous Breast Phantom
items: Classification of Autism Spectrum Disorder (ASD) in Children Using the VGG19 CNN Model Based on Facial Landmarks of the Eye and Forehead Areas
items: Development of Lactation Mobile Applications in Indonesia: A Systematic Literature Review
items: Comparative Analysis of Hybrid Wavelet Transformation and Filter Bank for Efficient Arrhythmia Detection in ECG Signals
items: Robust Real-Time SpO₂ Signal Enhancement Using Optimized IIR Filtering in a Web-Based Vital Sign Monitoring System
items: Development of an IoT-Based Anthropometric System Employing K-Means Clustering for Stunting Detection and Spatial Mapping in Toddlers
items: Beta-Band Electroencephalography Classification for Autism Spectrum Disorder Using Wavelet Features and Least-Squares Support Vector Machine
items: Experimental Study on Thickness-Dependent X-ray Radiation Protection of a Flexible and Lightweight Silicone Rubber–PbO Composite Apron
items: Evaluation of the GPS Neo Ublox M8N and Four-Sided Ultrasonic Sensor for Smart Navigation: A Case Study of a Miniature Unmanned Ground Vehicle
items: Embedded Machine Learning on ESP32 for Upper-Limb Exoskeletons Based on EMG
items: Food Detection to Estimate Calories Using Detection Transformer
items: Low-Cost Level Deduplication: Design and Inter-Node Consistency Evaluation in Indoor Industrial Environments
items: Development of an Android-based Stroke Prevention Application
items: Performance Evaluation of a Smart Aeration System for Tilapia Farming Based on IoT and Environmental Sensing
items: Child Growth and Development Monitoring in the Digital Era: A Systematic Review of Mobile and Digital Health Applications in Indonesia
items: Health Information Technology Management Model to Improve User Performance and Satisfaction
items: Development and Usability Evaluation of the "Si BINTANG" Audiovisual Web Application for Integrated Child Growth and Development Monitoring
items: Vision Transformer Enhanced by Contrastive Learning: A Self-Supervised Strategy for Pulmonary Tuberculosis Diagnosis
items: Sistemic Review and Meta-Analysis: PCR For Early Detection Of Infection Intrauterine And Prediction Obstetric Complications In High-Risk Pregnancy
items: The Effect Of Time Variation and Sodium Chloride (NaCl) Concentration In The Sedimentation Method On Soil-Transmitted Helmints (STH) Examination
items: Analyzing the Sociodemographic and Psychological Factors Influencing the Intention to Consume Single-Use Plastics Among University Students: A Cross-Sectional Quantitative Study
items: Isolation, Quantification, and Plaque Morphology Analysis of Lytic Bacteriophages from River Water Targeting Clinical MDR Klebsiella pneumoniae Using the Double-Layer Agar Method
items: Quasi-experimental study on Aloe Vera Gel As A Phytotherapy to Accelerate Perineal Wound Regeneration In Postpartum Mothers At Bpm Pera
items: Smart Screening Technology for Diabetes Risk: FFQ and FINDRISC Integration in a Digital Platform
items: SERAYA: A Mobile App for Mental Health with Personalized Self-Healing Recommendations Based on Psychological Assessment
items: Effectiveness of Using Analog Grid and Virtual Grid in Thoracic Radiography Examination
items: Early Detection of Biliary Atresia through Quantitative Ultrasound Imaging: A Retrospective Analysis of Key Diagnostic Features
items: Comparative in Vitro Antibacterial Activity of Red Ginger (Zingiber Officinale Var. Rubrum) Extract and Conventional Antibiotics Against Pathogenic Escherichia Coli
items: Monitoring the Occurrence of Alarms in Internet of Things-Based HFNC With Analysis of Signal Increases Before Blockages Error

Effect of phantom orientation angle on antenna sensor response in agar-based homogeneous phantom for abnormal breast tissue detection
type: item
description: Breast cancer remains a major global health concern; therefore, early detection plays a crucial role in reducing its mortality rates. Conventional imaging techniques, such as mammography, have limitations including ionizing radiation exposure and reduced sensitivity in dense breast tissues. Microwave-based sensing has emerged as a promising alternative due to its non-ionizing nature, relatively low cost, and sensitivity to dielectric property contrasts between normal and malignant tissues. This study aims to investigate the effect of phantom orientation angle on the response of an antenna-based microwave sensor for abnormal breast tissue detection. An agar-based homogeneous breast phantom is utilized, with tumor inclusion modeled as regions of different dielectric properties. Measurements are conducted using a Vector Network Analyzer (VNA) over a frequency range of 2–6 GHz, with four orientation angles (0°, 90°, 180°, and 270°). The reflection coefficient (S11) is analyzed to observe variations in electromagnetic response under different conditions. The results indicate that tumor presence causes measurable shifts in resonance frequency and variations in the reflection coefficient (S11). Larger tumors produce greater frequency shifts due to higher dielectric contrast. Furthermore, the orientation angle significantly affects detection sensitivity, which is attributed to the antenna radiation pattern and spatial interaction between electromagnetic waves and the phantom. In conclusion, this study demonstrates that both tumor size and phantom orientation influence antenna sensor response. The novelty of this work lies in the incorporation of orientation-based analysis, providing new insight into spatial electromagnetic interactions and enhancing the potential of microwave sensing systems for accurate and non-invasive breast cancer detection.
subject: Articles
  topic: section
subject: Microwave sensing
  topic: keywords
subject: Breast phantom
  topic: keywords
subject: antenna sensor
  topic: keywords
subject: Resonance frequency shift
  topic: keywords
subject: Reflection coefficient (S11)
  topic: keywords
creator: Rachma Cherlly Pramata
creator: Irmayatul Hikmah
creator: Muntaqo Alfin Amanaf
creator: Nur Afifah Zen
rights: Copyright (c) 2026 Rachma Cherlly Pramata, Irmayatul Hikmah , Nur Afifah Zen, Muntaqo Alfin Amanaf (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-12
publication date: 2026-06-12
starting page: 110
ending page: 122
doi: 10.35882/jteknokes.v19i2.160

Analysis of the Effect of Abnormal Tissue Size on the S11 Response of a Monopole Antenna Using a Realistic Heterogeneous Breast Phantom
type: item
description: Breast cancer remains one of the leading causes of death among women. Although conventional diagnostic methods are available, they are limited by radiation exposure risks, high operational costs, and limited accessibility. Alternatively, microwave technology offers the advantage of using non-ionizing radiation and leveraging differences in dielectric properties between healthy and abnormal tissues. The development of this technology, however, requires experimental validation using phantoms capable of realistically and stably representing the dielectric characteristics of biological tissues. In this study, a heterogeneous breast phantom was developed to evaluate antenna sensitivity in detecting variations in the size of abnormal tissue through S11 parameter analysis. The contribution of this study is the fabrication of a four-layer phantom (skin, fat, glandular, and abnormal tissue) using agar–gelatin materials. Sodium benzoate was added as a preservative to prevent microbial growth, and NaCl was incorporated to adjust the conductivity, enabling dielectric properties closer to those of real breast tissue. In the testing phase, two variations of abnormal tissue with diameters of 4 cm and 6 cm were inserted into the glandular layer to simulate different pathological conditions. The S11 response was measured using a vector network analyzer (VNA) over the 2–6 GHz frequency range with a monopole antenna placed 1 cm from the phantom. The results showed that the antenna sensor could detect differences between normal phantoms and phantoms with anomalies, particularly in the 2.5–3 GHz range. The normal phantom showed the lowest resonant frequency at 2.51 GHz with a return loss of −36.35 dB. In contrast, phantoms with 4 cm and 6 cm abnormal tissues showed shifts to 2.52 GHz (−29.71 dB) and 2.53 GHz (−28.69 dB), respectively. A maximum return loss difference of approximately 7.66 dB was observed, indicating high sensitivity to internal structural changes. These significant diff…
subject: Articles
  topic: section
subject: breast phantom
  topic: keywords
subject: microwave-based detection
  topic: keywords
subject: reflection coefficient (S11)
  topic: keywords
subject: abnormal tissue detection
  topic: keywords
subject: monopole antenna
  topic: keywords
creator: Isma Hanifah
creator: Irmayatul Hikmah
creator: Nur Afifah Zen
creator: Muntaqo Alfin Amanaf
rights: Copyright (c) 2026 Isma Hanifah, Irmayatul Hikmah, S.Si., M.Si, Nur Afifah Zen, S.Si., M.Si (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-04-12
publication date: 2026-04-12
starting page: 072
ending page: 083
doi: 10.35882/jteknokes.v19i2.159

Classification of Autism Spectrum Disorder (ASD) in Children Using the VGG19 CNN Model Based on Facial Landmarks of the Eye and Forehead Areas
type: item
description: Early detection of Autism Spectrum Disorder (ASD) is a crucial challenge in child development interventions because conventional screening methods are often subjective and prone to assessor bias. This study proposes an objective solution in the form of a deep learning approach for automatic ASD classification using facial landmark representations that focus exclusively on the eye and forehead areas. The selection of these areas is based on the eye avoidance hypothesis, which states that these regions contain very rich diagnostic information and behavioral biomarkers related to the ASD phenotype. The pre-processing stage involves isolating the eye and forehead areas using Dlib 68-landmark detection to eliminate background visual noise, followed by detailed topological visualization using MediaPipe Face Mesh with 478 landmark points as the model input. The Convolutional Neural Network (CNN) architecture used is the VGG19 model modified with transfer learning techniques and the addition of Dropout layers to improve efficiency and prevent overfitting. The model was trained on a primary dataset of 1,238 images collected under controlled conditions from children in Banda Aceh. The test results showed very promising performance with an overall accuracy of 94.35%. Specifically, the model achieved a recall (sensitivity) of 95.24%, a precision of 93.75%, and an AUC score of 0.9831. This high sensitivity is crucial in a medical context to minimize the risk of misdetection of positive cases. These results demonstrate that landmark visualization in the eye and forehead areas with the VGG19 model is a highly effective, accurate, and practical method for serving as an economical early screening tool for ASD.
subject: Articles
  topic: section
subject: Autism Spectrum Disorder
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: VGG19
  topic: keywords
subject: Facial Landmarks
  topic: keywords
subject: Eye-avoidance Hypothesis
  topic: keywords
subject: MediaPipe Face Mesh
  topic: keywords
subject: Early Screening
  topic: keywords
creator: yunidar
creator: Arya Suyanda
creator: Melinda Melinda
creator: Lailatul Qadri Zakaria
creator: Siti Rusdiana
rights: Copyright (c) 2026 yunidar, Arya Suyanda, Melinda Melinda, Lailatul Qadri Zakaria, Siti Rusdiana (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-13
publication date: 2026-06-13
starting page: 061
ending page: 071
doi: 10.35882/jteknokes.v19i2.158

Development of Lactation Mobile Applications in Indonesia: A Systematic Literature Review
type: item
description: Exclusive breastfeeding for six months is a critical public health intervention to reduce infant mortality in Indonesia. However, its implementation continues to face significant barriers, including low maternal health literacy, limited psychosocial support, and challenges in lactation management. Although digital health technologies have expanded rapidly, existing lactation applications in Indonesia remain fragmented and predominantly focus on one-way educational functions, without integrating maternal health data or clinical infant monitoring. This study aims to systematically analyze the characteristics and development trends of lactation mobile applications in Indonesia and to identify feature gaps, which will serve as the foundation for designing an integrated digital model. The main contribution of this study is the development of a conceptual framework for an integrated lactation technology model that combines breastfeeding education, family involvement, maternal factor documentation, and breast milk adequacy monitoring using infant physiological indicators, all within a single digital ecosystem. A Systematic Literature Review (SLR) was conducted following PRISMA guidelines. Literature searches were performed using Publish or Perish software on the Google Scholar database for studies published between 2017 and February 2026 using structured keywords. Five eligible studies were analyzed thematically and appraised using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. The review found that all identified applications were Android-based (100%, n=5). Four applications (80%) focused on maternal education and demonstrated improvements in knowledge and reduced anxiety; one (20%) emphasized family support through co-parenting; and one (20%) addressed lactation management via milk stock calculation features. No application integrates maternal history, early detection of lactation problems, and infant-based physiological monitoring within a unified platf…
subject: Articles
  topic: section
subject: Exclusive breastfeeding
  topic: keywords
subject: mHealth
  topic: keywords
subject: Digital health integration
  topic: keywords
subject: Decision-support system
  topic: keywords
subject: Lactation mobile application
  topic: keywords
creator: Rusmilawaty Rusmilawaty
creator: Norlaila Sofia
creator: Hapisah Hapisah
creator: Tri Tunggal
rights: Copyright (c) 2026 Rusmilawaty Rusmilawaty , Norlaila Sofia , Hapisah Hapisah , Tri Tunggal (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-19
publication date: 2026-03-19
starting page: 048
ending page: 060
doi: 10.35882/jteknokes.v19i1.156

Comparative Analysis of Hybrid Wavelet Transformation and Filter Bank for Efficient Arrhythmia Detection in ECG Signals
type: item
description: Cardiovascular disease (CVD) is still the leading cause of death worldwide, and arrhythmia is one of its most serious forms because it can trigger sudden cardiac arrest. Given the life-threatening nature of arrhythmias, reliable automated methods for arrhythmia detection are increasingly important in both clinical and remote monitoring settings. While the electrocardiogram (ECG) is the standard tool for arrhythmia detection, its accuracy is often reduced by noise and waveform distortion, which may lead to misclassification. To address this challenge, this study proposes an arrhythmia classification framework that integrates wavelet-based feature extraction with filter bank enhancement. ECG signals from the MIT-BIH Arrhythmia Database were preprocessed and segmented from two leads (MLII and V1), followed by wavelet decomposition using Daubechies (db6), Symlet (sym7), and Biorthogonal (bior4.4) families. Three complementary feature enhancement schemes, Discrete Cosine Transform (DCT), Complex Discrete Wavelet Transform (CDWT), and Orthogonal filter bank, were applied prior to classification with Support Vector Machine (SVM) and Random Forest (RF). The experimental results further highlight that the selection of wavelet, filter bank, and classifier combinations significantly influences arrhythmia detection performance. In particular, the pairing of the bior4.4 wavelet with the orthogonal filter bank and RF classifier achieved the highest accuracy of 94.76%, outperforming other setups, including CDWT-based schemes. This outcome suggests that the linear phase property of bior4.4 yields a more stable feature representation that aligns well with the ensemble mechanism of RF. These insights reinforce the importance of considering both the mathematical properties of wavelets and classifier design when developing ECG-based diagnostic systems. Future work will extend this approach to deep learning models and larger datasets to strengthen its clinical applicability.
subject: Articles
  topic: section
subject: Arrhythmia
  topic: keywords
subject: Electrocardiogram
  topic: keywords
subject: Wavelet Transform
  topic: keywords
subject: Filter Bank
  topic: keywords
creator: Amalia Nurul Maulida
creator: Annisa Humairani
creator: Tito Waluyo Purboyo
creator: Dziban Naufal
rights: Copyright (c) 2026 Amalia Nurul Maulida, Annisa Humairani, S.T., M.T., Tito Waluyo Purboyo, Dziban Naufal (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-18
publication date: 2026-03-18
starting page: 034
ending page: 047
doi: 10.35882/jteknokes.v19i1.154

Robust Real-Time SpO₂ Signal Enhancement Using Optimized IIR Filtering in a Web-Based Vital Sign Monitoring System
type: item
description: SpO₂ is a critical physiological parameter in vital sign monitoring, particularly within Internet of Things (IoT) based healthcare systems that enable continuous and remote patient observation. However, the accuracy of SpO₂ measurements is often compromised by motion artifacts in photoplethysmograph (PPG) signals, which introduce amplitude distortion and frequency spreading that degrade oxygen saturation estimation. Although various signal processing approaches have been proposed, limited studies have evaluated digital filtering performance on real PPG signals using real-time embedded platforms with direct comparison of filter characteristics. This study aims to analyze the effectiveness of two Infinite Impulse Response (IIR) digital filters, Butterworth and Elliptic, in suppressing motion artifacts in SpO₂ signals processed on a microcontroller. Data were collected from ten healthy participants under three conditions: baseline (no movement), induced finger motion, and filtered signals. Signal quality improvement was assessed using Fast Fourier Transform (FFT), Power Spectral Density (PSD), and Signal-to-Noise Ratio (SNR) analyses. Results indicate that motion artifacts increase high-frequency components above 3 Hz and disrupt the morphological integrity of the PPG waveform. Applying a low-pass IIR filter with a 3 Hz cutoff frequency successfully restored the principal periodic components. The Butterworth filter produced a smoother spectral response with minimal phase distortion, while the Elliptic filter achieved a sharper roll-off with slight passband ripple. Quantitative evaluation demonstrated average SNR improvements of +0.905 dB (Butterworth) and +0.899 dB (Elliptic) using FFT , and +0.98 dB and +0.66 dB, respectively, using PSD. These findings demonstrate that computationally efficient IIR filtering can be reliably implemented in resource-constrained embedded platforms without compromising signal integrity. This approach enhances signal stability, reduces fal…
subject: Articles
  topic: section
subject: SpO2; Motion Artifact; IIR Filter; Butterworth; Elliptic; FFT; PSD; SNR.
  topic: keywords
creator: Indah Firdausi Nuzula
creator: Priyambada Cahya Nugraha
creator: Moch Prastawa Assalim Tetra Putra
creator: I Dewa Gede Hari Wisana
creator: Riqqah Dewiningrum
creator: Alfi Nur Zeha
creator: Syaifullah Yusuf
rights: Copyright (c) 2026 Indah Firdausi Nuzula, Priyambada Cahya Nugraha, Moch Prastawa Assalim Tetra Putra, I Dewa Gede Hari Wisana, Riqqah Dewiningrum, Alfi Nur Zeha, Syaifullah Yusuf (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-18
publication date: 2026-06-18
starting page: 101
ending page: 109
doi: 10.35882/jteknokes.v19i2.152

Development of an IoT-Based Anthropometric System Employing K-Means Clustering for Stunting Detection and Spatial Mapping in Toddlers
type: item
description: Stunting remains a major public health challenge in Indonesia, affecting children’s physical growth and cognitive development due to inaccurate and delayed monitoring in community health centers. This study aims to develop an Internet of Things (IoT)-based anthropometric measurement and regional stunting mapping system that provides real-time, automated, and spatially contextualized data analysis. The novelty of this research lies in integrating IoT sensor networks with machine learning–based K-Means clustering and statistical validation through the Sum of Squared Error (SSE) method, supported by an automated email alert for high-risk areas. Unlike previous studies that focus solely on anthropometric measurement or standalone IoT monitoring, this study integrates real-time IoT-based data acquisition with K-Means clustering for regional stunting mapping and automated alert generation. The system employs an HC-SR04 ultrasonic sensor, an MPU-6050 gyroscope, and an ESP32 microcontroller for data acquisition and transmission, followed by clustering analysis to categorize stunting prevalence into five levels. Experimental results show high measurement accuracy (mean error of 1.24%) and optimal clustering compactness (SSE = 1.72 × 10³ at k = 5), effectively identifying regions with very high prevalence and visualizing them through a web-based dashboard. Although the study is limited by the use of secondary datasets and pilot- scale validation, the findings demonstrate that the proposed IoT-based framework can enhance data-driven public health decision-making. This innovation aligns with Indonesia’s national stunting reduction strategy and supports Sustainable Development Goal (SDG) 3 Good Health and Well-being, contributing to the digital transformation of early childhood health monitoring.
subject: Articles
  topic: section
subject: Stunting; IoT; Anthropometry; Regional Mapping; K-Means;
  topic: keywords
creator: Syaifudin Syaifudin
creator: Endro Yulianto
creator: Miftakhul Huda Wildany
rights: Copyright (c) 2026 Syaifudin, Endro Yulianto, Miftakhul Huda Wildany (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-19
publication date: 2026-06-19
starting page: 93
ending page: 100
doi: 10.35882/jteknokes.v19i2.149

Beta-Band Electroencephalography Classification for Autism Spectrum Disorder Using Wavelet Features and Least-Squares Support Vector Machine
type: item
description: Autism spectrum disorder requires accessible and objective neurophysiological biomarkers to complement behavioral assessment, particularly for early screening in resource-limited settings. This study explores a computationally efficient framework for distinguishing children with autism spectrum disorder from neurotypical controls using beta-band electroencephalography activity (12–30 Hz), which has been associated with atypical sensorimotor and cognitive processing in autism. Beta-band oscillations are theoretically relevant for their roles in attention, cognitive control, and inhibitory processes, domains frequently disrupted in autism spectrum disorder. Data were obtained from the public King Abdulaziz University dataset comprising 16 male participants (8 with autism, 8 controls; aged 6–14 years). Following independent component analysis-based artifact removal and bandpass filtering, recordings were segmented into 2-s epochs with 50% overlap. Discrete wavelet transform (Daubechies-4, four levels) was applied to extract statistical features (mean, standard deviation, skewness, kurtosis) from wavelet coefficients across 16 EEG channels, yielding a 320-dimensional feature vector per epoch. Classification was performed using least-squares support vector machines with a polynomial kernel (degree d=3), with hyperparameters optimized via 5-fold cross-validation on the training set, and evaluated via a stratified 70/30 train–test split at the segment level. The polynomial-kernel model achieved 98.49% segment-level accuracy, outperforming the linear kernel (95.07%) and a relative beta-power baseline. However, these results should be interpreted with caution due to the small sample size (n=16), a male-only cohort, and segment-level evaluation, which may inflate performance through intra-subject data leakage. The lightweight computational design supports potential implementation on portable devices. This proof-of-concept demonstrates the feasibility of wavelet-based beta-ban…
subject: Articles
  topic: section
subject: Autism Spectrum Disorder
  topic: keywords
subject: Electroencephalography
  topic: keywords
subject: Beta rhythm
  topic: keywords
subject: Discrete Wavelet Transform
  topic: keywords
subject: Least Squares Support Vector Machine
  topic: keywords
creator: melinda melinda
creator: Muhammad Irhamsyah
creator: Sri Rahayu Ade
creator: Saifullah Nur Muhammad
creator: Yunidar Yunidar
creator: Nurlida Basir
rights: Copyright (c) 2026 melinda melinda, Muhammad Irhamsyah, Sri Rahayu Ade, Saifullah Nur Muhammad, Yunidar Yunidar, Nurlida Basir (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-22
publication date: 2026-06-22
starting page: 110
ending page: 119
doi: 10.35882/jteknokes.v19i2.148

Experimental Study on Thickness-Dependent X-ray Radiation Protection of a Flexible and Lightweight Silicone Rubber–PbO Composite Apron
type: item
description: The use of X-rays in medical imaging provides substantial diagnostic benefits but also poses risks associated with ionizing radiation exposure. Conventional lead-based protective aprons are effective but have major limitations, including excessive weight, rigidity, and potential toxicity. This study addresses a specific research gap by systematically evaluating the relationship between material thickness, radiation attenuation effectiveness, and the Half-Value Layer (HVL) of silicone rubber-based aprons filled with lead(II) oxide (PbO) at clinically relevant low-to-medium X-ray energies. An experimental method was employed by fabricating silicone–PbO composite apron prototypes with three thickness variations (2.5 mm, 3.0 mm, and 3.5 mm). Radiation attenuation tests were conducted at X-ray tube voltages of 60, 65, and 70 kV by measuring radiation intensity before and after transmission through the samples using a radiation detector, followed by calculating protection effectiveness and HVL values. The results demonstrate that apron thickness significantly influences radiation protection performance, with the highest attenuation of 85.11% achieved at a thickness of 3.5 mm. A moderate-to-strong positive correlation between thickness and protection effectiveness is observed at all voltage levels, with the highest coefficient of determination (R² = 0.916) at 65 kV. HVL values increase with thickness, indicating the need for thicker materials to achieve a 50% reduction in radiation intensity at higher attenuation levels. These findings highlight the novelty of quantitatively correlating thickness, attenuation effectiveness, and HVL within a single experimental framework and demonstrate that silicone rubber–PbO composite aprons have strong potential as a lightweight and flexible alternative to conventional lead aprons for clinical radiation protection at low-to-medium diagnostic X-ray energies.
subject: Articles
  topic: section
subject: Effectiveness
  topic: keywords
subject: Lead(II) Oxide
  topic: keywords
subject: Protective apron
  topic: keywords
subject: Rubber Silicon
  topic: keywords
subject: X-ray
  topic: keywords
creator: Fathur Rahman Nugraha
creator: Kusnanto Mukti Wibowo
creator: Arga Pratama Rahardian
creator: Fani Susanto
creator: Supriyadi Supriyadi
rights: Copyright (c) 2026 Fathur Rahman Nugraha, Kusnanto Mukti Wibowo, Arga Pratama Rahardian, Fani Susanto, Supriyadi Supriyadi (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-06-17
publication date: 2026-06-17
starting page: 084
ending page: 092
doi: 10.35882/jteknokes.v19i2.147

Evaluation of the GPS Neo Ublox M8N and Four-Sided Ultrasonic Sensor for Smart Navigation: A Case Study of a Miniature Unmanned Ground Vehicle
type: item
description: The rapid advancement of autonomous systems has driven the development of intelligent navigation technologies across various fields, including transportation, robotics, and environmental monitoring. However, many autonomous ground vehicle platforms rely on high-cost sensors and complex system architectures, limiting their accessibility for research and education purposes. To address this challenge, this study proposes a cost-effective miniature Unmanned Ground Vehicle (UGV) integrating a Neo Ublox M8N GPS module with a four-sided ultrasonic sensing system to support real-time navigation and local obstacle awareness. The proposed system combines global positioning data with multi-directional short-range distance detection, processed through a Raspberry Pi and visualized via a web-based platform for real-time monitoring. Experimental testing was conducted under controlled outdoor and indoor conditions to evaluate GPS positioning accuracy, ultrasonic detection performance, and overall system responsiveness. The Neo Ublox M8N module achieved an average positional error of 4.35 m, corresponding to an accuracy of 97.4%, representing an improvement over previous studies using low-cost GPS receivers without algorithmic enhancement. Meanwhile, the ultrasonic sensors demonstrated reliable obstacle detection within a range of 5–70 cm, with an error of less than 1% and stable readings across all four sides of the UGV. The integration of these two sensing modalities demonstrated effective coordination between global and local navigation tasks, enabling real-time path visualization and obstacle awareness. Overall, the findings indicate that the proposed miniature UGV provides a scalable, low-cost platform suitable for research, prototyping, and education applications in autonomous navigation. This work also contributes practical insights for developing intelligent sensing architectures in small-scale robotic systems and highlights opportunities for further enhancements through se…
subject: Articles
  topic: section
subject: Autonomous Navigation
  topic: keywords
subject: UGV
  topic: keywords
subject: GPS Module
  topic: keywords
subject: Four-Sided Ultrasonic Sensor
  topic: keywords
creator: Linahtadiya Andiani
creator: Casmika Saputra
creator: Noviana H
creator: Fauziah I
creator: Muhammad Fahrul K
rights: Copyright (c) 2026 Linahtadiya Andiani, Casmika Saputra, Noviana H, Fauziah I, Muhammad Fahrul K (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-02-02
publication date: 2026-02-02
doi: 10.35882/jteknokes.v19i1.137

Embedded Machine Learning on ESP32 for Upper-Limb Exoskeletons Based on EMG
type: item
description: Stroke remains one of the primary causes of long-term disability worldwide and frequently results in persistent impairment of upper limb motor function. To support more effective and intensive rehabilitation, there is a need for wearable devices that can interpret muscle activity and autonomously assist limb movement without relying on an external computer. This study aims to design and implement an upper-limb rehabilitation exoskeleton that is driven by electromyography (EMG) signal classification using machine learning and by real-time elbow angle monitoring, with all models deployed directly on an ESP32 microcontroller. The proposed exoskeleton is built from lightweight, ergonomic 3D-printed components and operates in both unilateral and bilateral modes. Its main contributions include: (1) embedding real-time EMG classification models on the ESP32 so that the device can function independently, (2) integrating EMG-based motor control with elbow angle feedback from an MPU6050 inertial measurement unit, and (3) incorporating a load cell to estimate biceps force during training. EMG signals from the forearm flexor muscles are processed to extract statistical features such as variance (VAR), waveform length (WL), integrated EMG (IEMG), and root mean square (RMS). These features are used to train Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost classifiers. The trained models are converted to C code using the micromlgen library for execution on the ESP32. System evaluation involved thirty male participants aged 20–25 years with body weights between 50–85 kg. All tested models achieved 100% accuracy in distinguishing relaxed versus grasping muscle contractions, while the correlation of elbow angles between unilateral and bilateral ESP32 systems reached 0.9469, indicating highly consistent motion detection. The Decision Tree model was selected for deployment due to its superior memory efficiency on the microcontroller. These results demonstrate tha…
subject: Articles
  topic: section
subject: Exoskeleton; Machine Learning; EMG; ESP32; Stroke Rehabilitation
  topic: keywords
creator: Triwiyanto Triwiyanto
creator: Anita Miftahul Maghfiroh
creator: Levana Forra Wakidi
creator: Syevana Dita Musvika
creator: Bedjo Utomo
creator: Sumber Sumber
creator: Priyambada Cahya Nugraha
creator: Wahyu Caesarendra
rights: Copyright (c) 2025 Triwiyanto Triwiyanto, Anita Miftahul Maghfiroh, Levana Forra Wakidi, Syevana Dita Musvika (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-07
publication date: 2025-12-07
starting page: 303
ending page: 310
doi: 10.35882/jteknokes.v18i4.134

Food Detection to Estimate Calories Using Detection Transformer
type: item
description: Accurately estimating calorie intake remains a common challenge, as many individuals have limited understanding of portion sizes and the caloric content of foods. This lack of nutritional knowledge is a major cause of both over- and under-calorie consumption and contributes to significant public health problems, including obesity, cardiovascular disease, and chronic metabolic disorders. Although computer vision–based approaches for dietary assessment have advanced, many methods still rely on handcrafted features, anchor-based CNN detectors, or controlled geometric assumptions. This indicates a practical gap in developing a fully functional system that operates on basic RGB images captured under everyday conditions. This study aims to develop an end-to-end food detection and calorie estimation system using the Detection Transformer (DETR) to predict calorie values directly from food images. The main contributions of this study include: (1) employing DETR to address non-maximum suppression limitations and improve the stability of multi-food recognition; (2) using a bounding box area-to-weight ratio as a low-complexity alternative to segmentation-based food portion estimation; and (3) developing a user-friendly interface for output visualization that displays detected food items and their estimated calorie values in real-world scenarios involving irregular food shapes and varying focal lengths. A DETR-based detector was trained using 2,228 COCO-formatted images across six distinct food classes. Calorie values were estimated by predicting food weight based on bounding box measurements, followed by calorie calculation using standardized reference weights. The method assessed robustness by evaluation on both controlled and real-life food images. Experimental results demonstrated moderate performance, with 0.617 mean Average Precision (mAP) and 0.656 mean Average Recall (mAR). The weight prediction module served as the primary estimation component, achieving a mean absolut…
subject: Articles
  topic: section
subject: Computer Vision
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Food Detection
  topic: keywords
subject: Detection Transformer
  topic: keywords
subject: Calories
  topic: keywords
creator: Joshua Putra Fesha Kristanto
creator: Dedy Agung Prabowo
creator: Yohani Setiya Rafika Nur
rights: Copyright (c) 2025 Joshua Putra Fesha Kristanto, Dedy Agung Prabowo, Yohani Setiya Rafika Nur (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-05
publication date: 2026-01-05
doi: 10.35882/jteknokes.v18i4.132

Low-Cost Level Deduplication: Design and Inter-Node Consistency Evaluation in Indoor Industrial Environments
type: item
description: Indoor air quality (IAQ) monitoring in industrial spaces is vital to protect workers from particulate matter exposure (PM₁, PM₂.₅, PM₁₀). Yet, many low-cost IoT systems prioritize outdoor, wide-area deployments and rarely confront two issues that matter indoors: inter-node measurement consistency when multiple identical sensors are co-located, and firmware-level transmission efficiency for Wi-Fi nodes operating under energy and bandwidth constraints. This work addresses both by presenting a reproducible, low-cost IAQ node built on an ESP32 (S3) and a PMS7003, coupled with a lightweight, on-device data-deduplication routine that suppresses redundant packets before they reach the network stack. The node integrates temperature–humidity sensing, RTC-GNSS for stable timestamps, local SD logging, a compact display for in-situ readouts, and standard Wi-Fi for infrastructure-friendly connectivity, enabling autonomous operation with optional MQTT back-end integration. We evaluate the design via a 24-hour co-location test of four identical nodes in a controlled indoor room (5-minute sampling). Minute-aligned time series are analyzed using one-way ANOVA to quantify inter-node agreement. Results indicate no statistically significant differences among nodes for PM₁, PM₂.₅, and PM₁₀ (p > 0.05), confirming internal consistency suitable for simultaneous multi-point monitoring. The deduplication routine reduces transmissions by ≈3.2% without information loss, modest per device, but compounding across dense deployments to lower network load and energy use. Together, these outcomes validate (i) a practical hardware–firmware stack for low-cost IAQ sensing in indoor factories, (ii) a deployable firmware strategy for network-efficient reporting, and (iii) an empirical inter-node consistency assessment using co-location and ANOVA. The approach facilitates scalable, accurate, and efficient IAQ surveillance for occupational safety programs and compliance workflows. Future work will extend t…
subject: Articles
  topic: section
subject: Low-Cost Air Sensor
  topic: keywords
subject: Indoor Air Quality
  topic: keywords
subject: Data Deduplication
  topic: keywords
subject: Node Consistency
  topic: keywords
subject: ANOVA
  topic: keywords
creator: Agus Purnomo
creator: Asep Andang
creator: Siti Badriah
rights: Copyright (c) 2026 Agus Purnomo, Asep Andang, Siti Badriah (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-17
publication date: 2026-03-17
starting page: 23
ending page: 33
doi: 10.35882/jteknokes.v19i1.131

Development of an Android-based Stroke Prevention Application
type: item
description: Stroke remains a significant public health problem in Indonesia, with a prevalence of 8.3 per 1,000 population and a significant contribution to disability and mortality. Limited community health literacy and underutilization of digital health media indicate the need for accessible technology-based stroke prevention interventions. This study aimed to develop and validate a community-oriented Android-based application for stroke prevention using an ADDIE-based Research and Development framework. This study contributes a validated mobile health intervention for community stroke prevention and a replicable development model adaptable to other non-communicable disease prevention programs. The development process included application design, feature and database construction, internal testing, expert validation, and small-group usability testing. experts. Media validation involved three instructional technology experts. A small-group usability test was conducted with 15 adult community members in Banjar Regency, selected through convenience sampling. Data were analyzed using descriptive statistics and feasibility classification. The application obtained a mean score of 4.7 from material experts and 4.9 from media experts, indicating a “Highly Feasible” classification. Usability testing showed an overall mean score of 16.8 out of 20, reflecting strong user acceptance. This study is limited by its small sample size and short-term evaluation without behavioral or clinical outcome assessment. Nevertheless, the application shows potential as a digital health education tool to improve stroke prevention literacy and support community-based prevention programs and national digital health strategies.
subject: Articles
  topic: section
subject: Android based mobile application
  topic: keywords
subject: Stroke prevention
  topic: keywords
subject: Mobile health education
  topic: keywords
creator: Khairir Rizani
creator: Norlaila Sofia
creator: Ferry Fadli Fratama
creator: M. Rizki Imanuddin
rights: Copyright (c) 2025 Khairir Rizani, Norlaila Sofia, Ferry Fadli Fratama, M. Rizki Imanuddin (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-08
publication date: 2025-12-08
starting page: 282
ending page: 290
doi: 10.35882/jteknokes.v18i4.130

Performance Evaluation of a Smart Aeration System for Tilapia Farming Based on IoT and Environmental Sensing
type: item
description: Fluctuations in dissolved oxygen (DO) levels in high-density biofloc-based tilapia aquaculture pose a critical challenge that directly affects fish growth, survival rate, and feed conversion efficiency. Traditional aeration systems that operate continuously are energy inefficient and unable to adapt dynamically to real-time environmental variations. This study aims to improve DO stability and energy efficiency in biofloc-based tilapia aquaculture through adaptive aeration control. This study designs and evaluates an Internet of Things (IoT)-based smart aeration system that automatically regulates aeration intensity based on real-time DO sensing and threshold-based control logic. The system is built on an ESP32 microcontroller integrated with a digital DO sensor, a water temperature sensor, and relay actuators for blower control, with data transmission via the MQTT protocol and real-time monitoring through a web-based dashboard. Experimental testing was conducted for seven days in a biofloc pond containing 200 tilapia, with a comparative analysis between manual and automated control modes. The results demonstrate that the smart aeration system effectively maintained DO within the optimal range of 5.1–6.8 mg/L while reducing blower energy consumption by 26.7%. Communication reliability was validated with an average transmission delay of 740 ms and a packet loss rate of 1.8%, both of which are acceptable for real-time IoT applications. Data analysis showed consistent improvements in DO stability and energy efficiency throughout the experimental stage. In addition, the system’s modular architecture enables scalability for integration with additional sensors or renewable energy sources, such as solar panels, to support off-grid operations. The findings affirm that the proposed system offers a practical, low-cost, and sustainable solution for data-driven aquaculture management and contribute to the advancement of smart, environmentally responsive aquaculture systems.
subject: Articles
  topic: section
subject: Biofloc
  topic: keywords
subject: Dissolved Oxygen (DO)
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: MQTT
  topic: keywords
subject: Smart Aeration
  topic: keywords
subject: ESP32
  topic: keywords
creator: Firmansyah maulana sugiartana Nursuwars
creator: Rahmi Shofa
creator: Nurul hiron
creator: Ida Bagus Alit Swamardika
creator: aceng sambas
rights: Copyright (c) 2025 Firmansyah maulana sugiartana Nursuwars, Rahmi Shofa, Nurul hiron, Ida Bagus Alit Swamardika, aceng sambas (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-01-05
publication date: 2026-01-05
doi: 10.35882/jteknokes.v18i4.129

Child Growth and Development Monitoring in the Digital Era: A Systematic Review of Mobile and Digital Health Applications in Indonesia
type: item
description: Monitoring child growth and development is essential for improving early childhood health outcomes and preventing stunting; however, monitoring systems in Indonesia remain fragmented and largely focused on anthropometric data. This study synthesizes evidence on digital applications developed between 2020 and 2025 to support child growth and developmental monitoring in Indonesia. Following PRISMA guidelines, 18 studies were identified from national and international databases and appraised using the JBI checklist. The review examined application characteristics, monitoring functions, design quality, user experience, and their contributions to parenting and developmental stimulation. Findings show that most applications prioritize physical growth tracking, while only a minority integrate assessments of cognitive, motor, and socio-emotional development. Thematic synthesis identifies three major gaps: limited incorporation of holistic developmental frameworks, suboptimal interface and interaction quality, and insufficient evidence-based educational content to support sustained caregiver engagement. Although most systems demonstrate high functional performance and methodological quality, interoperability with regional health information networks remains weak, and long-term user engagement is inconsistently documented. Contextual factors influencing these patterns include variability in digital literacy, infrastructural disparities across regions, and the technical challenges of digitizing developmental screening tools. The increasing use of Agile and RAD development approaches indicates potential to improve adaptability, usability, and iterative system refinement. This review underscores the need for integrated, family-centered digital platforms supported by national policy, capacity-building initiatives, and standardized developmental assessment protocols. The synthesized insights provide a foundation for guiding future system development, with interoperability, user-ce…
subject: Articles
  topic: section
subject: Child growth monitoring
  topic: keywords
subject: Child development assesment;
  topic: keywords
subject: Digital health app
  topic: keywords
subject: Mobile health;
  topic: keywords
subject: Stunting prevention
  topic: keywords
creator: Norlaila Sofia
creator: Tien Zubaidah
creator: Hapisah
creator: Noor Adha Aprilea
creator: Rusmilawaty
rights: Copyright (c) 2026 Norlaila Sofia, Tien Zubaidah, Hapisah, Noor Adha Aprilea, Rusmilawaty (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2026-03-17
publication date: 2026-03-17
starting page: 1
ending page: 13
doi: 10.35882/jteknokes.v19i1.128

Health Information Technology Management Model to Improve User Performance and Satisfaction
type: item
description: The rapid development of digital technology has reshaped the way healthcare institutions manage information, deliver services, and support clinical decisions. Despite these advances, many hospitals still struggle with inefficiencies resulting from weak Health Information Technology (HIT) governance and limited user skills. Most existing approaches prioritize technical deployment while paying less attention to managerial, organizational, and behavioral factors that are essential for sustainable success. To overcome these limitations, this study introduces and empirically evaluates a comprehensive Health Information Technology Management (HITM) model that combines strategic IT governance, system quality, and user dimensions to improve satisfaction and performance among healthcare professionals. The research examines how governance mechanisms, system quality, and user capabilities affect satisfaction and performance. The specific objectives are to identify the key drivers of system quality, evaluate the relationship between system quality and user satisfaction, and examine how satisfaction impacts user performance. The study contributes theoretically by presenting a more integrated framework that unites concepts from IT governance, Information Systems Success Theory, and Technology Acceptance Theory. It also offers empirical evidence of the importance of managerial structures in driving successful digital transformation in healthcare settings. A survey involving healthcare personnel from three public hospitals in Indonesia was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results demonstrate strong model validity, accounting for 65% of the variance in user satisfaction and 59% of the variance in performance, with predictive relevance (Q²) values of 0.47 and 0.52, respectively. These outcomes demonstrate that mature governance, leadership support, cross-unit collaboration, and systematic user training enhance system quality, satisfaction, …
subject: Articles
  topic: section
subject: Health Information Technology Management; User Satisfaction; User Performance; System Quality;
  topic: keywords
creator: Nining yuniati
creator: Agung Yuliyanto Nugroho
rights: Copyright (c) 2025 Nining yuniati (Author); Nugroho (Translator) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-04
publication date: 2025-12-04
starting page: 238
ending page: 246
doi: 10.35882/jteknokes.v18i4.123

Development and Usability Evaluation of the "Si BINTANG" Audiovisual Web Application for Integrated Child Growth and Development Monitoring
type: item
description: Indonesia’s child growth monitoring system, essential for national stunting prevention, still relies on paper-based tools such as the Child Health Card (KMS), which often face challenges of low parental engagement, fragmented data management, and limited health literacy. Existing digital applications primarily function as data logbooks, lacking interactive audiovisual content that can effectively support parents with diverse literacy levels and learning needs. This gap highlights the urgency for innovative health communication strategies that bridge conventional and digital media to enhance participatory learning and improve community-based child monitoring practices. This study developed and evaluated Si BINTANG (Interactive Barcode-Based Growth and Development Information System). This hybrid audiovisual web platform connects a printed child monitoring book with digital content through QR code technology. The application was designed using a user-centered and health communication approach to enhance parental understanding, motivation, and engagement in monitoring their child's growth and development. A preliminary usability evaluation involving experts and mothers of children under five indicated high system quality, usability, and user acceptance, demonstrating positive responses across functionality, accessibility, and user satisfaction. These results suggest that hybrid digital–print integration can enhance health education by making it more interactive, inclusive, and accessible for families, particularly in low-literacy or resource-limited settings. While the findings show encouraging potential, this study acknowledges several limitations, including the small and localized sample size. Future research will focus on large-scale implementation and impact assessment on parental knowledge, behavioral change, and child health outcomes. The Si BINTANG platform represents a promising direction for strengthening family-based stunting prevention through hybrid digital…
subject: Articles
  topic: section
subject: Child growth monitoring; Digital health intervention; Stunting prevention; Hybrid health education; QR code technology
  topic: keywords
creator: Norlaila Sofia
creator: Hapisah
creator: Noor Adha Aprilea
creator: Rusmilawaty
creator: Yuniarti
creator: Zakiah
rights: Copyright (c) 2025 Norlaila Sofia, Hapisah, Noor Adha Aprilea, Rusmilawaty, Yuniarti, Zakiah (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-04
publication date: 2025-12-04
starting page: 217
ending page: 226
doi: 10.35882/jteknokes.v18i4.127

Vision Transformer Enhanced by Contrastive Learning: A Self-Supervised Strategy for Pulmonary Tuberculosis Diagnosis
type: item
description: Tuberculosis (TB) diagnosis from Chest X-ray (CXR) images poses a significant challenge in radiology due to the inherent data imbalance and subtle lesion heterogeneity. These factors cause traditional deep learning models, like standard CNNs and conventional Vision Transformers (ViT), to exhibit poor generalization and inadequate sensitivity (recall) for the minority TB class. We address this critical research gap by introducing a novel methodology, an enhanced ViT architecture that leverages Self-Supervised Learning (SSL) via the SimCLR framework, subsequently optimized with an Adaptive Weighted Focal Loss. Our primary objective was to develop a generalizable model that minimizes false negatives without sacrificing overall precision, thereby establishing a new performance benchmark for automated TB detection. The methodology conceptually separates feature learning from SSL pre-training on unlabeled data to generate robust and domain-invariant features, distinct from classification optimization. Adaptive Weighted Focal Loss is employed during fine-tuning to counter majority class gradient dominance mechanistically. We validated this approach using K-Fold Cross-Validation. The final ViT SSL Weighted model achieved a peak internal accuracy of 0.9861 and an AUPRC of 0.9781. Crucially, it maintained generalization stability when externally tested on the TBX11K dataset, securing an AUPRC of 0.9795 and a high recall of 0.9527. This minimal variance strongly confirms the reproducibility and robustness of our features against institutional variation. The resulting high recall directly translates to enhanced diagnostic decision-making, significantly lowering the clinical risk associated with a missed TB diagnosis. This study establishes an effective, stable, and generalizable SSL-based ViT framework, offering a scalable solution for public health efforts in resource-constrained settings.
subject: Articles
  topic: section
subject: Vision Trasnformer
  topic: keywords
subject: Self-Supervised Learning
  topic: keywords
subject: ViT
  topic: keywords
subject: SimCLR
  topic: keywords
subject: Tuberculosis
  topic: keywords
creator: Widia Marlina
creator: Umar Zaky
rights: Copyright (c) 2025 Widia Marlina, Umar Zaky (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-04
publication date: 2025-12-04
starting page: 247
ending page: 259
doi: 10.35882/jteknokes.v18i4.126

Sistemic Review and Meta-Analysis: PCR For Early Detection Of Infection Intrauterine And Prediction Obstetric Complications In High-Risk Pregnancy
type: item
description: Intrauterine infection is a major cause obstetric complications, including preterm birth, premature rupture of membranes (PROM), and low birth weight (LBW). Polymerase Chain Reaction (PCR) testing offers rapid and sensitive molecular detection compared to conventional diagnostic methods. This study is a systematic review and meta analysis conducted following the PRISMA 2020 guidelines, aimed at evaluating the effectiveness of PCR in detecting intrauterine infection and predicting performed using PubMed, Scopus, ScienceDirect, Wiley and SpringerLink (2020 - 2025). The pooled analysis demonstrated a sensitivity of 0.90, specificity of 0,93 and an Area Under the Curve (AUC) of 0,95 with a Diagnostic Odds Ratio (DOR) of approximately 120. Postive PCR results were significantly associated with an increased risk of preterm birth (OR 3,4), LBW (OR 2,8) and PROM (OR 2,2). PCR demonstrated high diagnostic accuracy and strong prognostic value, supporting its integration into risk based antenatal screening to reduce perinatal morbidity and mortality.
subject: Articles
  topic: section
subject: Polymerase chain reaction (PCR)
  topic: keywords
subject: Intrauterine infection
  topic: keywords
subject: Preterm birth
  topic: keywords
creator: Evi Irianti
creator: Ice Ratnalela Siregar
rights: Copyright (c) 2025 Evi Irianti, Ice (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-28
publication date: 2025-12-28
doi: 10.35882/jteknokes.v18i4.120

The Effect Of Time Variation and Sodium Chloride (NaCl) Concentration In The Sedimentation Method On Soil-Transmitted Helmints (STH) Examination
type: item
description: Soil-transmitted helminths (STH) remain a significant public health concern, particularly in regions with inadequate sanitation. While the sedimentation method is routinely employed for STH diagnosis, the effects of procedural variables such as incubation time and sodium chloride (NaCl) concentration on recovery efficiency remain inadequately explored. This study aimed to evaluate the influence of variations in sedimentation time and NaCl concentration on the detection and morphological integrity of STH eggs in stool samples. This contributes to more reliable diagnostic results in laboratory settings, supports epidemiological surveillance, and enhances the accuracy of STH monitoring in endemic areas. A controlled experimental design was implemented, utilizing 48 treatment combinations that varied in NaCl concentration (2%, 4%, 6%, and 8%) and sedimentation duration (5, 10, 15, and 20 minutes). Stool samples naturally infected with Ascaris lumbricoides, Trichuris trichiura, and hookworm were processed and examined microscopically for egg count, morphology, and sedimentation quality. Data normality was assessed using the Shapiro–Wilk test, and differences among treatment groups were analyzed with the Kruskal–Wallis test followed by post-hoc pairwise comparisons. Results demonstrated that both NaCl concentration and sedimentation time significantly influenced egg recovery (p < 0.05), with 2% NaCl and 30 minutes yielding the highest recovery rate and optimal morphological preservation. Morphological assessment revealed that prolonged sedimentation (>40 minutes) resulted in noticeable egg deformation, potentially compromising identification accuracy. These findings highlight the critical importance of optimizing procedural parameters in routine STH diagnostics. Adjusting NaCl concentration and sedimentation time can enhance both quantitative recovery and qualitative morphological assessment, thereby improving diagnostic reliability in clinical and epidemiological setting…
subject: Articles
  topic: section
subject: Soil transmittedHelminths; Stool examination; Sedimentation method; Sodium chloride; Centrifugation time.
  topic: keywords
creator: Acivrida Mega Charisma
creator: Arina Wahyuningrum
rights: Copyright (c) 2025 Acivrida Mega Charisma, Arina Wahyuningrum (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-05
publication date: 2025-12-05
starting page: 207
ending page: 216
doi: 10.35882/jteknokes.v18i4.119

Analyzing the Sociodemographic and Psychological Factors Influencing the Intention to Consume Single-Use Plastics Among University Students: A Cross-Sectional Quantitative Study
type: item
description: The rising use of disposable plastics among Indonesian university students contributes to the growing problem of plastic waste, making it essential to understand the factors shaping their intentions to use single-use plastics. This research aims to determine the impact of sociodemographic factors (age, education level, and economic status) and psychological factors (subjective norms, emotional motivation, and beliefs) on the intention to use disposable plastic among university students. The study contributes by integrating the Theory of Planned Behaviour (TPB) and the Value-Belief-Norm (VBN) Theory to explain the intention to consume single-use plastics in the Indonesian context, identifying the belief variable as the most dominant factor influencing behavioral intention. The research employed a quantitative descriptive-analytical approach with a cross-sectional design, conducted from April to June 2024. A total of 125 respondents were selected using purposive sampling. Data were collected through questionnaires developed based on the TPB and VBN frameworks, then analyzed using descriptive statistical tests and multivariate logistic regression to identify significant predictors of behavioral intention. The results show that sociodemographic factors, age (p = 0.002), education level (p = 0.000), and economic status (p = 0.000), significantly influence the intention to consume disposable plastics. Likewise, psychological factors, subjective norms (p = 0.001), emotional motivation (p = 0.000), and beliefs (p = 0.000), also have a significant effect, with beliefs emerging as the most dominant factor (Exp(B) = 10.234). The study implies that efforts to reduce single-use plastic consumption among university students should focus on strengthening environmental education and transforming social norms to foster sustainable behaviors. Furthermore, future research with longitudinal designs and broader populations is recommended to enhance the generalizability of these findings…
subject: Articles
  topic: section
subject: Sociodemographi
  topic: keywords
subject: Psychology
  topic: keywords
subject: Theory of Planned Behavior (TPB
  topic: keywords
subject: Value Belief Norm (VBN).
  topic: keywords
creator: Alprida Harahap
creator: Adi Antoni
creator: Haslinah Ahmad
creator: Anton J. Hadi
creator: Nanda Masraini Daulay
rights: Copyright (c) 2025 Alprida Harahap, Adi Antoni, Haslinah Ahmad , Anton J. Hadi, Nanda Masraini Daulay (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-04
publication date: 2025-12-04
starting page: 227
ending page: 237
doi: 10.35882/jteknokes.v18i4.116

Isolation, Quantification, and Plaque Morphology Analysis of Lytic Bacteriophages from River Water Targeting Clinical MDR Klebsiella pneumoniae Using the Double-Layer Agar Method
type: item
description: Antimicrobial resistance is a growing global health threat and is projected to cause up to 10 million deaths per year by 2050. Klebsiella pneumoniae is a priority pathogen due to its multidrug resistance (MDR) mechanisms, such as extended-spectrum β-lactamases and carbapenemases, which significantly limit therapeutic options and increase the need for antimicrobial alternatives. This study aimed to isolate and quantify active lytic bacteriophages capable of infecting clinical MDR K. pneumoniae from river water samples. Water samples were processed by centrifugation and membrane filtration to remove debris and bacterial cells, then incubated with MDR K. pneumoniae in Luria broth at 37°C to enhance phage adsorption and amplification. Phage detection and enumeration were performed using the double-layer agar method. Plaque morphology was observed to confirm lytic activity, while serial dilutions were used to determine phage titer. Several lytic bacteriophages were successfully isolated from river water samples. The plaques formed were clear, spherical, and well-defined, with some exhibiting halos indicative of possible depolymerase activity. Phage titers ranged from 1.28 × 10³ to 2.00 × 10⁶ PFU/mL, indicating efficient replication against MDR K. pneumoniae without repeated enrichment processes. River water is a potential source of lytic bacteriophages capable of infecting MDR K. pneumoniae. These findings emphasize the role of aquatic environments as natural reservoirs of phages with potential use in the development of future antimicrobial or biocontrol strategies and support the need for further studies on the host range, stability, and therapeutic applications of the isolated phages.
subject: Articles
  topic: section
subject: Lytic bacteriophages;
  topic: keywords
subject: Multidrug-resistant (MDR) Klebsiella pneumoniae;
  topic: keywords
subject: River water;
  topic: keywords
subject: Double-layer agar method;
  topic: keywords
subject: Plaque morphology;
  topic: keywords
subject: Antimicrobial alternatives.
  topic: keywords
creator: Aminah Aminah
creator: Citra Trisna
creator: Fitri Alina Sugianto
creator: Gadis Amalia
creator: M Atik Martiningsih
creator: Zulfikar Husni Faruq
rights: Copyright (c) 2025 Aminah Aminah, dr. Citra, Fitri Alina Sugianto, Gadis Amalia, M Atik Martiningsih, Zulfikar Husni Faruq (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-08
publication date: 2025-12-08
starting page: 272
ending page: 281
doi: 10.35882/jteknokes.v18i4.113

Quasi-experimental study on Aloe Vera Gel As A Phytotherapy to Accelerate Perineal Wound Regeneration In Postpartum Mothers At Bpm Pera
type: item
description: Perineal wounds are one of the most common complications in postpartum women, causing pain, increasing the risk of infection, and delaying recovery. The lack of effective, safe, affordable, and easily accessible therapies highlights a gap in perineal wound care practices, particularly in primary care. Aloe vera, which contains anti-inflammatory compounds and regenerative bioactives, has the potential to serve as an alternative phytotherapy, but clinical evidence of its effectiveness in perineal wounds remains limited. This study aimed to evaluate the effectiveness of aloe vera gel in accelerating perineal wound healing in postpartum women. The contribution of this research is to strengthen the practice of phytotherapy-based wound care that is safe, affordable, and applicable in primary health care facilities. The study used a quasi-experimental pre-post study design with a control group. Thirty postpartum women with grade I–II perineal wounds were divided into a treatment group (Aloe vera) and a control group (standard care). Healing was evaluated using the REEDA scale, and pain intensity was assessed using a Visual Analogue Scale (VAS) over seven days. Analysis was performed using an independent t-test with a significance level of 0.05. The results showed a significant difference between the two groups. The average healing time for the Aloe vera group was 4.20 days (SD 0.95), faster than the control group, which took an average of 7.00 days (SD 1.20). Statistical tests showed a p-value = 0.001, indicating significant effectiveness of Aloe vera application in accelerating tissue healing. In addition, REEDA and VAS scores on the seventh day were consistently lower in the treatment group, indicating a faster reduction in inflammation and pain. Implicitly, these findings provide a clinical contribution to strengthening safe and affordable phytotherapy-based wound care practices for postpartum mothers, particularly in primary healthcare facilities. This study concluded …
subject: Articles
  topic: section
subject: Perineal Wounds, Phytotherapy, Postpartum Mothers, Aloe Vera, Regeneration.
  topic: keywords
creator: Basaria Manurung
creator: Retno Wahyuni
creator: Hariati Eliana Purba
creator: Isyos Sari Sembiring
creator: Eva Ratna Dewi
creator: Nopalina Suyanti Damanik
creator: Titis Jernih Wati Gea
rights: Copyright (c) 2025 basaria, Retno Wahyuni, Hariati Eliana Purba, Isyos Sari Sembiring, Eva Ratna Dewi, Nopalina Suyanti Damanik, Titis Jernih Wati Gea (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-12-08
publication date: 2025-12-08
starting page: 260
ending page: 271
doi: 10.35882/jteknokes.v18i4.110

Smart Screening Technology for Diabetes Risk: FFQ and FINDRISC Integration in a Digital Platform
type: item
description: Diabetes mellitus (DM) is a growing metabolic and autoimmune-related disease whose early onset is increasingly observed among young adults, including the university students in Indonesia. The existing screening models are either costly, invasive, or fail to integrate lifestyle data, leaving a gap for practical yet scalable solutions in this population. This study introduces a smart screening technology that combines the Food Frequency Questionnaire (FFQ) and the Finnish Diabetes Risk Score (FINDRISC) within a digital platform to capture both dietary patterns and individual risk factors. A cross-sectional design was applied to 110 undergraduates, chosen to reflect young adults most vulnerable to lifestyle-related DM risks. Data were collected entirely online to ensure feasibility and low-cost scalability in campus and public health programs. Multiple linear regression revealed that both individual factors (age, gender, BMI, physical activity, family history) and dietary patterns were significant predictors of DM risk (β = 0.312; β = 0.389; p < 0.001), explaining 37.4% of the variance. Compared to prior studies that relied solely on clinical or genetic markers, this integration highlights the added predictive value of dietary data in digital risk screening. With 70.9% of respondents at moderate and 25.5% at high risk, the findings underscore the urgent need for early intervention among Indonesian students. The proposed model offers practical applications through university health centers, mobile apps for student lifestyle monitoring, and peer-based preventive education. Future work should extend to biomarker validation and adaptive algorithms to enhance predictive accuracy and applicability across diverse populations.
subject: Articles
  topic: section
subject: Diabetes Mellitus
  topic: keywords
subject: Smart Screening
  topic: keywords
subject: Individual Factors
  topic: keywords
subject: Dietary Pattern
  topic: keywords
subject: Early Detection
  topic: keywords
subject: FINDRISC
  topic: keywords
subject: FFQ
  topic: keywords
creator: Yuswanto Setyawan
rights: Copyright (c) 2025 Yuswanto Setyawan (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 198
ending page: 206

SERAYA: A Mobile App for Mental Health with Personalized Self-Healing Recommendations Based on Psychological Assessment
type: item
description: Mental health disorders are a crucial public health issue in Indonesia, as reflected in the 2023 National Health Survey (SKI), which reported that 2.0% of the population aged ≥15 years old were diagnosed with mental health problems, and approximately 20% of the 250 million people experiencing mental health problems do not yet have access to adequate services. Although many previous studies have developed digital applications, such as “Serenity” and “CERDAS” in Indonesia for psychological assessment using instruments such as the DASS-21, these applications only provide general recommendations and do not provide personalized self-healing guidance. To address this gap, this study developed and tested “SERAYA”, a mobile application designed not only to assess mental health levels but also to provide self-healing recommendations. This application integrates two standard instruments, the DASS-21 and the PSS-10, to measure depression, anxiety, stress, and perceived stress. A rule-based expert system using forward chaining processes the assessment scores; for example, “IF DASS-21 depression score ≥ 28 THEN recommendation = ‘CBT Therapy’”. Based on this score, the system generates specific recommendations or direct referrals to mental health professionals for severe cases. SERAYA's functionality was verified through successful black-box testing. Initial usability assessments using the System Usability Scale (SUS) with 11 respondents yielded an average score of 80.68, indicating good usability and ease of learning for early users. While these initial results are encouraging, they are derived from a limited, non-clinical sample and cannot be generalized to the entire Indonesian population. Overall, this study demonstrates that “SERAYA” serves as a viable proof-of-concept for providing personalized early mental health support and illustrates the potential of rule-based systems in digital health applications. Future research should focus on larger-scale validation, clinical inte…
subject: Articles
  topic: section
subject: Mental health
  topic: keywords
subject: PSS-10
  topic: keywords
subject: DASS-21
  topic: keywords
subject: Mobile App
  topic: keywords
subject: Rule-based System
  topic: keywords
creator: Enggi Wira Praja Putri Taufani
creator: Umar Zaky
rights: Copyright (c) 2025 Enggi Wira Praja Putri Taufani (Author), Umar Zaky https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 182
ending page: 197
doi: 10.35882/jteknokes.v18i3.108

Effectiveness of Using Analog Grid and Virtual Grid in Thoracic Radiography Examination
type: item
description: Chest radiography is essential for diagnosing thoracic abnormalities, but scattered radiation often reduces image contrast and diagnostic accuracy. Conventional analog grids mitigate scatter yet increase patient dose, while virtual grids offer digital correction with potentially lower radiation exposure. This study aimed to compare image quality and radiation efficiency between analog and virtual grids in thoracic radiography, contributing empirical evidence on the feasibility of adopting virtual grids as safer and more efficient alternatives. Three imaging protocols (Analog Grid, Virtual Grid 1, and Virtual Grid 2) were compared using identical exposure parameters on a phantom. Image quality was evaluated both objectively and subjectively. Results showed that Virtual Grid 2 achieved the highest score (4.47), slightly outperforming the Analog Grid (4.20) despite using lower radiation, while Virtual Grid 1 scored the lowest. ANOVA confirmed significant differences among the three methods, though the t-test between Analog Grid and Virtual Grid 2 showed no significant difference. A moderate negative correlation indicated that a reduced dose does not always compromise image quality when supported by advanced processing. In conclusion, Virtual Grid 2 demonstrates strong potential as a reliable alternative to analog grids, enabling excellent image quality with minimal radiation and supporting safer radiographic practices.
subject: Articles
  topic: section
subject: Analog Grid;
  topic: keywords
subject: Virtual Grid;
  topic: keywords
subject: Radiography;
  topic: keywords
subject: Thorax;
  topic: keywords
creator: Ayu Mahanani
creator: Anshor Nugroho
creator: Arnefia Mei Yusnida
rights: Copyright (c) 2025 Ayu Mahanani, Anshor Nugroho, Arnefia Mei Yusnida (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 157
ending page: 165

Early Detection of Biliary Atresia through Quantitative Ultrasound Imaging: A Retrospective Analysis of Key Diagnostic Features
type: item
description: Biliary atresia (BA) is a progressive fibroinflammatory obstruction of the intra- and extrahepatic bile ducts, accounting for approximately 25–30% of neonatal cholestasis cases worldwide. Delayed diagnosis significantly increases the risk of liver failure and the need for transplantation. This study aimed to evaluate the diagnostic value of specific ultrasonographic parameters triangular cord sign, hepatic artery to portal vein (HAD/PV) ratio, and hepatic subcapsular flow (HSF) in the early detection of BA. A retrospective observational analytic study was conducted at Dr. Soetomo General Academic Hospital, involving 82 neonates with cholestasis between January 2019 and December 2023. Ultrasound images acquired using the GE LOGIQ Series E9 XDClear 2.0 were reviewed by blinded pediatric radiology consultants via the HOROS DICOM viewer. Statistical analysis revealed that both the triangular cord sign (p = 0.024; OR = 3.158) and hepatic subcapsular flow (p = 0.003; OR = 5.635) showed significant association with BA, while the HAD/PV ratio did not reach statistical significance (p = 0.087). Triangular cord sign and hepatic subcapsular flow are reliable sonographic markers that support early diagnosis of biliary atresia, whereas the HAD/PV ratio showed limited diagnostic relevance in this cohort. These findings reinforce the utility of non-invasive ultrasound imaging in differentiating BA from other causes of neonatal cholestasis. Incorporating these parameters into routine neonatal screening protocols may facilitate earlier surgical intervention and improve long-term outcomes
subject: Articles
  topic: section
subject: Ultrasonography
  topic: keywords
subject: Radiology Technology
  topic: keywords
subject: Triangular Cord Sign
  topic: keywords
subject: Hepatic Subcapsular Flow
  topic: keywords
subject: Biliary Atresia
  topic: keywords
creator: Lenny Violetta
creator: Dian Nurhayati
creator: Ryan Elmanar
rights: Copyright (c) 2025 Lenny Violetta, Dian Nurhayati, Ryan Elmanar (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 166
ending page: 173

Comparative in Vitro Antibacterial Activity of Red Ginger (Zingiber Officinale Var. Rubrum) Extract and Conventional Antibiotics Against Pathogenic Escherichia Coli
type: item
description: The increasing incidence of antibiotic resistance in Escherichia coli poses a significant challenge to public health, as the effectiveness of conventional antibiotics such as ciprofloxacin continues to decline due to the emergence of resistant E. coli strains. This study aims to evaluate red ginger extract's in vitro antibacterial activity against Escherichia coli and determine the optimal concentration for bacterial inhibition. This research aims to determine the effectiveness of red ginger extract with concentrations of 60%, 80% and 100% on the growth of Escherichia coli. Research method using a laboratory-based experimental study was conducted using red ginger extract prepared via ethanol maceration at concentrations of 60%, 80%, and 100%. The extract's antibacterial activity against E. coli was evaluated using the disc diffusion method following CLSI guidelines, with ciprofloxacin (500 mg) as a positive control and 95% ethanol as a negative control. Each treatment was replicated five times. Inhibition zones were measured after 24 hours of incubation at 37°C. The results of the study show that red ginger extract demonstrated significant antibacterial activity against E. coli, with mean inhibition zones of 6.08 ± 0.98 mm, 6.48 ± 1.21 mm, and 5.20 ± 1.89 mm for 60%, 80%, and 100% concentrations, respectively. The 80% concentration showed optimal inhibition activity, while the higher concentration (100%) demonstrated reduced effectiveness. Statistical analysis confirmed significant treatment differences (p < .001, η² = 0.967). The positive control produced significantly larger inhibition zones (23.16 ± 1.67 mm, p < .05). This study concludes that red ginger extract exhibits moderate antibacterial activity against E. coli in vitro, with 80% concentration showing optimal efficacy. These findings suggest potential applications in developing natural antimicrobial agents, although further research is needed to elucidate mechanisms of action and evaluate clinical applicat…
subject: Articles
  topic: section
subject: Antibacterial activity
  topic: keywords
subject: Escherichia coli
  topic: keywords
subject: Red ginger
  topic: keywords
subject: Zingiber officinale var. rubrum
  topic: keywords
subject: Natural products
  topic: keywords
subject: Antimicrobial resistance
  topic: keywords
creator: Isramilda Isramilda
creator: Andi Ipaljri
creator: Kinanthi Amalia Putri
rights: Copyright (c) 2025 Isramilda Isramilda, Andi Ipaljri, Kinanthi Amalia Putri (Author) https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-25
publication date: 2025-09-25
starting page: 174
ending page: 181

Monitoring the Occurrence of Alarms in Internet of Things-Based HFNC With Analysis of Signal Increases Before Blockages Error
type: item
description: HFNC must be closely monitored by nurses to increase the flow rate when the patient's condition worsens. The monitoring occurs every hour to assess the results of HFNC administration. If the patient's condition persists, the flow given to HFNC will be increased. Thus, careful monitoring is essential during oxygen therapy with HFNC to ensure the patient's oxygen dose remains consistent. Despite debates about aerosol side effects, HFNC has proven effective in increasing blood oxygen levels and improving lung performance. This study aims to analyze HFNC errors leading to decreased flow. The research employs a temperature setting of 34ºC and a flow setting of 30-60 LPM. Under a flow setting of 30 LPM, the average pressure value before the error is 0 in process 2, and after the error, it remains 0, indicating a Blockage error as notified by IoT. Similarly, under the same flow setting, the average pressure value before the error is 0 in process 1, and after the error, it remains 0, indicating a leaking error based on the IoT notification and tool condition. The study reveals an average error of 3.8 for Blockage and 1.5 for Leaking conditions. Future research could explore developments such as employing a more sensitive pressure sensor, like the MPX5010DP, and incorporating batteries to enhance the module's portability without the need for a voltage source.
subject: Articles
  topic: section
subject: HNFC, Pressure Error, IoT
  topic: keywords
creator: Dwi Widyaningtyas
creator: Muhammad Ridha Mak’ruf
creator: Andjar Pudji
rights: Copyright (c) 2023 Dwi Widyaningtyas, Muhammad Ridha Mak’ruf, Andjar Pudji https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-16
publication date: 2025-07-16
starting page: 66
ending page: 72