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.
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<![CDATA[Child Growth and Development Monitoring in the Digital Era: A Systematic Review of Mobile and Digital Health Applications in Indonesia ]]>
<![CDATA[Sofia, Norlaila]]>;
<![CDATA[Zubaidah, Tien]]>;
<![CDATA[Hapisah]]>;
<![CDATA[Noor Adha Aprilea]]>;
<![CDATA[Rusmilawaty]]>
<![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.v19i1.128
<![CDATA[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-centered design, and equitable access as core principles for strengthening Indonesia’s digital child-health monitoring ecosystem.]]>
<![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.]]>
