Jurnal Teknokes
Aims JURNAL TEKNOKES aims to become a forum for publicizing ideas and thoughts on health science and engineering in the form of research and review articles from academics, analysts, practitioners, and those interested in providing literature on biomedical engineering in all aspects. Scope: 1. Medical Electronics Technology and Biomedical Engineering: Biomedical Signal Processing and Control, Artificial intelligence in biomedical imaging, Machine learning, and Pattern Recognition in a biomedical signal, Medical Diagnostic Instrumentation, Laboratorium Instrumentation, Medical Calibrator Design, Intelligent Systems, Neural Networks, Machine Learning, Fuzzy Systems, Digital Signal Processing, Image Processing, prosthetics, orthotics, rehabilitation sciences, Mobility Assistive Technology (MAT), Internet of Things (IoT), and Artificial Intelligence (AI) in the prosthetics and orthotics field, Breast Imaging, Cardiovascular Imaging, Chest Radiology, Computed Tomography, Diagnostic Imaging, Gastrointestinal Imaging, Genitourinary, Radiology, Head & Neck, Imaging Sciences, Magnetic Resonance Imaging, Musculoskeletal Radiology, Neuroimaging and Head & Neck, Neuro-Radiology, Nuclear Medicine, Pediatric Imaging, Positron Emission Tomography, Radiation Oncology, Ultrasound, X-ray Radiography, etc. 2. Medical Laboratory Technology: Hematology and clinical chemistry departments, microbiology section of the laboratory, parasitology, bacteriology, virology, hematology, clinical chemistry, toxicology, food and beverage chemistry. 3. Environmental Health Science, Engineering and Technology: Papers focus on design, development of engineering methods, management, governmental policies, and societal impacts of wastewater collection and treatment; the fate and transport of contaminants on watersheds, in surface waters, in groundwater, in soil, and in the atmosphere; environmental biology, microbiology, chemistry, fluid mechanics, and physical processes that control natural concentrations and dispersion of wastes in air, water, and soil; nonpoint-source pollution on watersheds, in streams, in groundwater, in lakes, and in estuaries and coastal areas; treatment, management, and control of hazardous wastes; control and monitoring of air pollution and acid deposition; airshed management; and design and management of solid waste facilities, detection of micropollutants, nanoparticles and microplastic, antimicrobial resistance, greenhouse gas mitigation technologies, novel disinfection methods, zero or minimal liquid discharge technologies, biofuel production, advanced water analytics 4. Health Information System and Technology The journal presents and discusses hot subjects including but not limited to patient safety, patient empowerment, disease surveillance and management, e-health and issues concerning data security, privacy, reliability and management, data mining and knowledge exchange as well as health prevention. The journal also addresses the medical, financial, social, educational, and safety aspects of health technologies as well as health technology assessment and management, including issues such as security, efficacy, the cost in comparison to the benefit, as well as social, legal, and ethical implications. This journal also discussed Intelligent Biomedical Informatics, Computer-aided medical decision support systems using a heuristic, Educational computer-based programs pertaining to medical informatics.
Articles
96 Documents
<![CDATA[Performance Evaluation of a Smart Aeration System for Tilapia Farming Based on IoT and Environmental Sensing ]]>
<![CDATA[Nursuwars, Firmansyah maulana sugiartana]]>;
<![CDATA[Shofa, Rahmi]]>;
<![CDATA[hiron, Nurul]]>;
<![CDATA[Swamardika, Ida Bagus Alit]]>;
<![CDATA[sambas, aceng]]>
<![CDATA[ Jurnal Teknokes Vol. 18 No. 4 (2025): Desember ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v18i4.129
<![CDATA[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.]]>
<![CDATA[Food Detection to Estimate Calories Using Detection Transformer ]]>
<![CDATA[Kristanto, Joshua Putra Fesha]]>;
<![CDATA[Prabowo, Dedy Agung]]>;
<![CDATA[Yohani Setiya Rafika Nur]]>
<![CDATA[ Jurnal Teknokes Vol. 18 No. 4 (2025): Desember ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v18i4.132
<![CDATA[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 absolute residual of 8.7. These findings suggest that bounding box area is a reliable estimator of serving size. This study serves as a proof of concept for monitoring individual food intake and provides a foundation for further improvement in sub-item recognition, three-dimensional volume estimation, and the inclusion of broader food classes.]]>
<![CDATA[Evaluation of the GPS Neo Ublox M8N and Four-Sided Ultrasonic Sensor for Smart Navigation: A Case Study of a Miniature Unmanned Ground Vehicle ]]>
<![CDATA[Andiani, Linahtadiya]]>;
<![CDATA[Saputra, Casmika]]>;
<![CDATA[H, Noviana]]>;
<![CDATA[I, Fauziah]]>;
<![CDATA[K, Muhammad Fahrul]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 1 (2026): March ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i1.137
<![CDATA[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 sensor fusion and autonomous control strategies.]]>
<![CDATA[Low-Cost Level Deduplication: Design and Inter-Node Consistency Evaluation in Indoor Industrial Environments ]]>
<![CDATA[Purnomo, Agus]]>;
<![CDATA[Andang, Asep]]>;
<![CDATA[Badriah, Siti]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 1 (2026): March ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i1.131
<![CDATA[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 to longer horizons, drift characterization, and integration with adaptive, event-driven analytics and calibration pipelines for robust industrial rollouts.]]>
<![CDATA[Comparative Analysis of Hybrid Wavelet Transformation and Filter Bank for Efficient Arrhythmia Detection in ECG Signals ]]>
<![CDATA[Nurul Maulida, Amalia]]>;
<![CDATA[Humairani, Annisa]]>;
<![CDATA[Waluyo Purboyo, Tito]]>;
<![CDATA[Naufal, Dziban]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 1 (2026): March ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i1.154
<![CDATA[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.]]>
<![CDATA[Development of Lactation Mobile Applications in Indonesia: A Systematic Literature Review ]]>
<![CDATA[Rusmilawaty , Rusmilawaty]]>;
<![CDATA[Sofia , Norlaila]]>;
<![CDATA[Hapisah , Hapisah]]>;
<![CDATA[Tunggal, Tri]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 1 (2026): March ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i1.156
<![CDATA[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 platform. In conclusion, lactation application development in Indonesia remains fragmented and lacks a system-integrated approach. The proposed SiCubit conceptual model provides a decision-support framework for integrated breastfeeding monitoring and digital lactation support.]]>
