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
92 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.]]>
