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
104 Documents
<![CDATA[Effect of phantom orientation angle on antenna sensor response in agar-based homogeneous phantom for abnormal breast tissue detection ]]>
<![CDATA[Rachma Cherlly Pramata]]>;
<![CDATA[Irmayatul Hikmah]]>;
<![CDATA[Muntaqo Alfin Amanaf]]>;
<![CDATA[Nur Afifah Zen]]>
<![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.v19i2.160
<![CDATA[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.]]>
<![CDATA[Beta-Band Electroencephalography Classification for Autism Spectrum Disorder Using Wavelet Features and Least-Squares Support Vector Machine ]]>
<![CDATA[Muhammad Irhamsyah]]>;
<![CDATA[melinda melinda]]>;
<![CDATA[Sri Rahayu Ade]]>;
<![CDATA[Saifullah Nur Muhammad]]>;
<![CDATA[Yunidar Yunidar]]>;
<![CDATA[Nurlida Basir]]>
<![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.v19i2.148
<![CDATA[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-band analysis for autism classification, but rigorous validation using larger, balanced cohorts with subject-wise cross-validation is essential before clinical translation can be considered.]]>
<![CDATA[Analysis of the Effect of Abnormal Tissue Size on the S11 Response of a Monopole Antenna Using a Realistic Heterogeneous Breast Phantom ]]>
<![CDATA[Isma Hanifah]]>;
<![CDATA[Irmayatul Hikmah]]>;
<![CDATA[Nur Afifah Zen]]>;
<![CDATA[Muntaqo Alfin Amanaf]]>
<![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.v19i2.159
<![CDATA[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 differences in return loss values indicate that the developed system is highly sensitive to changes in internal structure. This combination of a heterogeneous phantom and an antenna sensor has the potential to serve as a simple experimental platform to support breast cancer detection technology.]]>
<![CDATA[Predicting Stress Levels in Special Needs School Teachers Using DASS-21 and Gradient Boosting Machine ]]>
<![CDATA[Alfita Khairah]]>;
<![CDATA[melinda melinda]]>;
<![CDATA[Iskandar Hasanuddin]]>;
<![CDATA[Rizka Miftahujjannah]]>;
<![CDATA[Rosminazuin Ab Rahim]]>;
<![CDATA[Siti Rusdiana]]>
<![CDATA[ Jurnal Teknokes Vol. 19 No. 3 (2026): September ]]>
Publisher : <![CDATA[Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia ]]>
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DOI: 10.35882/jteknokes.v19i3.164
<![CDATA[The stress experienced by teachers in Special Needs Schools presents considerable threats to both mental health and job performance, making the need for precise and scalable detection techniques essential. This research presents a multimodal framework for stress classification that combines physiological signals from a wearable IoT device with psychological assessment using the DASS-21. Data were gathered from 48 educators, including heart rate (BPM) and body temperature (°C) as physiological indicators and DASS-21 stress subscale scores as psychological indicators. The target variable was categorized as a binary class (stressed vs. non-stressed) utilizing standardized DASS-21 cut-off scores, with physiological thresholds to improve label dependability. The class distribution was examined to reduce class imbalance bias. The dataset was subjected to preprocessing, which involved normalization and feature selection, and was then divided into a 60:40 train-test split. The evaluation of model generalizability was conducted through 5-fold cross-validation. GBM model was utilized to identify non-linear relationships and interactions between features. Performance assessment involved accuracy, precision, recall, F1-score, specificity, and AUC. The suggested model reached accuracy of 94.12%, 87.5%, and 85.71% for the elementary, junior high, and high school groups, respectively, demonstrating consistently high precision and AUC metrics, reflecting strong discriminative ability. These findings indicate that a clearly defined labeling approach, harmonious feature integration, and strict validation procedure facilitate dependable and consistent stress detection. The suggested framework offers a scalable solution for real-time monitoring of mental health in educational environments. Furthermore, the results explicitly confirm that integrating IoT-based wearable systems with the DASS-21 and the GBM algorithm provides a reliable and scalable approach for stress detection among teachers. This study highlights the practical significance of implementing real-time monitoring systems in educational environments, enabling early identification and intervention for stress management. Such a framework not only improves teachers’ mental well-being but also supports institutional decision-making in developing preventive strategies and sustainable mental health programs.]]>
