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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Ragam Pengabdian
Muaya, Valentino Goerge
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Environmental Science Social Sciences Other

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Studi Performa Algoritma Random Forest untuk Deteksi Dini Kerusakan pada Sistem Konversi Energi Piezoelektrik Siwy, Viggo Faraday; Muaya, Valentino Goerge; Pattimahu, Kevin Yonathan; Wauran, Alfrets Septy; Ramschie, Ali Akbar
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus, Sustainable Development Goals (SDGs): Multidisciplinary Perspectiv
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/k65z9114

Abstract

Piezoelectric energy conversion systems are essential renewable energy harvesting solutions for autonomously powering Internet of Things devices. Piezoelectric ceramic elements are highly susceptible to functional degradation due to cyclic mechanical stress exposure that triggers material fatigue. This study proposes a smart computational predictive maintenance approach to detect system anomalies in real time. The experiment acquires transient electrical parameters namely voltage and current, as well as spatial mechanical vibrations from a piezoelectric matrix prototype. Given the high cost of physical destructive testing, a dataset of five thousand samples was synthesized using an empirical data augmentation approach based on hardware ground truth. The Random Forest Classifier algorithm was implemented for binary classification of normal and anomaly system conditions. Independent testing results demonstrated that the model achieved an accuracy of 94.6 percent, precision of 92.49 percent, sensitivity of 94.71 percent, and an F1 score of 93.59 percent. The predictive error margin of approximately seven percent proves the model robustness in accommodating highly realistic physical ambiguous zones. The model is algorithmically consistent with material physics laws by prioritizing voltage features and vertical acceleration, while ignoring microampere current fluctuations. This performance solidifies the viability of ensemble architecture as a structural health monitoring system in edge computing networks.  
STUDI KARAKTERISTIK ELEKTRIK MODUL PIEZOELEKTRIK TERHADAP VARIASI BEBAN RESISTIF SEBAGAI SUMBER ENERGI ALTERNATIF TERBARUKAN Rondonuwu, Marvil Rafael; Muaya, Valentino Goerge; Loegimin, Maruto Swatara; Kasenda, Sonny R.
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 02 (2026): Volume 11 Nomor 02, Juni 2026 Published
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i02.48760

Abstract

Mechanical energy harvesting from human footsteps is a potential solution for supplying micro-power to autonomous electronic devices. This study aims to investigate the electrical characteristics of PZT (Lead Zirconate Titanate) piezoelectric modules arranged in a parallel configuration of 20 units to respond to a mechanical load of 100 kg. The main focus of this research is to analyze the effect of low resistive load variations from 10 Ω to 110 Ω on power transfer efficiency. The experimental method was carried out by applying a static load of 100 kg to the piezoelectric array connected to a full-bridge rectifier circuit using 1N4007 diodes and a 47 µF filter capacitor. The output voltage was then measured across six variations of resistive loads using a digital multimeter, with three repetitions at each measurement point to ensure data validity. The results show that increasing the load resistance is directly proportional to the increase in output voltage, but inversely proportional to the generated current. The Maximum Power Point (MPP) was identified at a resistive load of 90 Ω with a power output of 33.64 mW. This phenomenon demonstrates that the parallel configuration of the piezoelectric array effectively reduces the internal impedance of the system, allowing optimal energy transfer within a low-load range.
Co-Authors Kasenda, Sonny R. Loegimin, Maruto Swatara Pattimahu, Kevin Yonathan Ramschie, Ali Akbar Rondonuwu, Marvil Rafael Siwy, Viggo Faraday Wauran, Alfrets Septy