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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Nyatte, Steyve
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Materials Science & Nanotechnology

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AI-Powered Holter for Affordable and Accurate Arrhythmia Detection Nyatte, Steyve; Leatitia, Guiadem; steve, Perabi; Essiane, Ndjakomo
Jurnal Teknokes Vol. 18 No. 2 (2025): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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Abstract

Cardiac arrhythmias pose significant health risks, and current detection systems often suffer from high costs and limited accessibility, particularly in resource-constrained settings. This research aimed to develop a portable, cost-effective Holter monitoring device for accurate arrhythmia detection using machine learning. By combining an inexpensive ESP32 microcontroller with an AD8232 ECG sensor, a data acquisition system was built. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP) models were trained and evaluated for arrhythmia classification. The SVM model achieved the highest accuracy (78.53%) using a linear kernel and features selected by a random forest algorithm. While KNN and MLP also showed promise, the results emphasized the importance of hyperparameter tuning and feature selection. This research demonstrated the feasibility of creating an affordable and intelligent Holter device capable of effective arrhythmia detection, potentially increasing access to cardiac monitoring and enabling early diagnosis in resource-limited environments.
Comparison of Pressure Sensor in Flow Analyzer Design for Peep Measurement on Ventilators Wakidi, Levana Forra; Amrinsani, Farid; Zeha, Alfi Nur; Dewiningrum, Riqqah; Nyatte, Steyve
Jurnal Teknokes Vol. 16 No. 4 (2023): December
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

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Abstract

Flow Analyzer allows measurement of flow, pressure, volume, and oxygen concentration delivered to the patient, with PEEP (Positive End Expiratory Pressure) being a crucial parameter in mechanical ventilation. Incorrect PEEP values can elevate the risk of patient mortality. The recommended PEEP range is 5-24 cmH2O, and administration is determined by the patient's clinical condition. This research aims to identify stable and highly accurate pressure sensors by comparing the MPX2010DP and MPX5010DP sensors with pressure readings from a Digital Pressure Meter (DPM). The study involves 5 repetitions of a lung test, each with 11 pressure reading points, within a pressure measurement range of 0-30 cmH2O. The DPM has a resolution of 1 cmH2O, while both pressure sensors have a resolution of 0.01 cmH2O. Results indicated that the MPX2010DP sensor has the smallest error percentage, specifically 0.00%, at a pressure increase of 5 cmH2O and 20 cmH2O. Conversely, the MPX2010DP sensor shows the largest error percentage, 5.16%, when the pressure decreases by 5 cmH2O. The highest standard deviation of 0.52 is observed in the MPX5010DP sensor at a 20 cmH2O pressure increase, while the maximum correction value of 0.54 is found in the MPX5010DP sensor at a 25 cmH2O pressure increase. According to the ANOVA test, there is no significant difference in pressure produced between the MPX2010DP sensor, MPX5010DP sensor, and DPM. The sensors are well-calibrated and provide accurate readings according to calibration tool standards. Therefore, the MPX2010DP and MPX5010DP sensors are deemed accurate for measuring PEEP parameters in ventilators. Based on the obtained data, it can be concluded that the MPX2010DP sensor is more accurate and stable.
Analysis of the Accuracy of Thermocouple Sensors at the Incubator Calibrator Laboratory Equipped with Internet of Thing-Based Data Storage Prastyadi, Candra; Irianto, Bambang Guruh; Ariswati, Her Gumiwang; Titisari, Dyah; Nyatte, Steyve; Misra, Shubhrojit
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 4 (2022): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i4.158

Abstract

Laboratory incubator is a tool used to incubate a breed. This incubator provides optimum temperature conditions for microorganisms to grow. It has a temperature regulator so that the temperature can be adjusted according to the breed incarnated. In this case, incubator worked like the hot-dry system of ovens. The purpose of this study was to conduct testing and analysis on the accuracy of thermocouple sensors using incubator media in laboratory incubator calibrator tools. The contribution of the research was to know the level of accuracy of the right sensor for sensing the temperature in the laboratory incubator. The main designed tool consisted of 8 MAX6675 standards, 8 K thermocouple, Arduino-Mega, and SD Card Standards. The temperature of the incubator device, in this case, was measured by the K thermocouple sensor. The sensor system had 8 channels that serve to measure the temperature at each incubator point. The temperature data were further stored in the SD card to analyze the data and the data can be processed into the form of a graphic. Benchmarking was done using a data logger temperature tool. This was done to make the designed tool results under the standards tool. After comparing between the tool designed and the standard tool obtained the largest error value of 3.98% in channel T6 at the temperature of 35°C with ordinary incubator media, while the smallest error in ordinary incubator media was at the point T6 at temperature of 37°C by 0.06%. Meanwhile, in the fan incubator, at the temperature of 35oC, had the largest error of 2.98%, while the smallest error was 0.86%. The conclusion of this study is that the tool designed could work well in measuring the temperature of the incubator, as well as the system for storing the data reading using the SD card.
Real-Time Vibration Fault Detection in Rotating Machines Using Transformers to Minimize Production Losses in Industry 5.0: VIBT TOUKAP NONO, FERNAND JOSEPH; TOKOUE NGATCHA , DIANORE; OFFOLE, Florence; Nyatte, Steyve; MOUZONG PEMI, Marcelin
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.566

Abstract

Quickly identifying anomalies in rotating machinery is crucial for safety and profitability in contemporary industry (Industry 5.0). Unidentified failures can cause costly malfunctions and production interruptions. This research proposes an innovative strategy based on Transformer for the analysis of multidimensional vibration events (VIBT), with a view to early and accurate detection of anomalies in rotating machinery. The goal is to minimize production interruptions in Industry 5.0. The study highlights the limitations of conventional vibration analysis approaches and traditional deep learning techniques, emphasizing the need for innovative solutions. VIBT incorporates transformers and a filter bank convolution (FBC) module for effective denoising, as well as an adaptive wavelet transformation (WTA) mechanism for dynamic feature fusion at various scales, thereby addressing the challenges posed by non-stationary and noisy signals. Extensive testing on the Mafaulda dataset reveals that VIBT achieves 98.1% precision and 98.8% accuracy, significantly outperforming existing standard models. The results suggest that VIBT not only improves fault detection capabilities but also optimizes maintenance strategies in industrial applications, paving the way for future research on semi-supervised learning based on transformers and the integration of intermodal data.
Co-Authors Amrinsani, Farid Ariswati, Her Gumiwang Bambang Guruh Irianto Dewiningrum, Riqqah Dyah Titisari, Dyah Essiane, Ndjakomo Leatitia, Guiadem Misra, Shubhrojit MOUZONG PEMI, Marcelin OFFOLE, Florence Prastyadi, Candra steve, Perabi TOKOUE NGATCHA , DIANORE TOUKAP NONO, FERNAND JOSEPH Wakidi, Levana Forra Zeha, Alfi Nur