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[Experimental Study on Thickness-Dependent X-ray Radiation Protection of a Flexible and Lightweight Silicone Rubber–PbO Composite Apron ]]>
<![CDATA[Nugraha, Fathur Rahman]]>;
<![CDATA[Wibowo, Kusnanto Mukti]]>;
<![CDATA[Rahardian, Arga Pratama]]>;
<![CDATA[Susanto, Fani]]>;
<![CDATA[Supriyadi, Supriyadi]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 2 (2026): June ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i2.147
<![CDATA[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.]]>
<![CDATA[Development of an IoT-Based Anthropometric System Employing K-Means Clustering for Stunting Detection and Spatial Mapping in Toddlers ]]>
<![CDATA[Syaifudin, Syaifudin]]>;
<![CDATA[Yulianto, Endro]]>;
<![CDATA[Huda Wildany, Miftakhul]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 2 (2026): June ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i2.149
<![CDATA[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.]]>
<![CDATA[Classification of Autism Spectrum Disorder (ASD) in Children Using the VGG19 CNN Model Based on Facial Landmarks of the Eye and Forehead Areas ]]>
<![CDATA[yunidar]]>;
<![CDATA[Suyanda, Arya]]>;
<![CDATA[Melinda, Melinda]]>;
<![CDATA[Zakaria, Lailatul Qadri]]>;
<![CDATA[Rusdiana, Siti]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 2 (2026): June ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i2.158
<![CDATA[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.]]>
