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All Journal Jurnal Teliska Jurnal Desiminasi Teknologi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Applied Smart Electrical Network and System (JASENS) Jurnal Techno Bahari J-SAKTI (Jurnal Sains Komputer dan Informatika)
Hendra Marta Yudha
Uiversitas Tridinanti Palembang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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PREDIKSI PEMANENAN ENERGI PADA ARRAY PIEZOELEKTRIK KONFIGURASI SERI-PARALEL BERBASIS RANDOM FOREST REGRESSION Fairuz Attalah; Yurni Oktarina; Pola Risma; Assyifa Mourlina Faraquinsha; Hendra Marta Yudha
JURNAL TELISKA Vol 19 No I (2026): TELISKA Maret 2026
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/teliska.v19iI.11829

Abstract

This study analyses the electrical characteristics and predicts the power output of a piezoelectric smart carpet system through a comparative analysis of series and parallel circuit configurations using the Random Forest Regression (RFR) algorithm. The system was designed using 16 piezoelectric ceramic elements per block integrated into a 100 cm carpet structure. Experimental data were collected from five subjects with body masses ranging from 54–98 kg through walking and running activities, yielding 100 observational samples. Results show the series configuration produced an average power output of 1649.3 mW, outperforming the parallel configuration by 104.8%, which yielded only 805.1 mW, with peak voltage reaching 80 V during running. The RFR model optimized using GridSearchCV with 5-fold cross-validation achieved a coefficient of determination (R²) of 88.25%, a Mean Absolute Error (MAE) of 145.06 mW, and a Root Mean Squared Error (RMSE) of 179.15 mW. Feature importance analysis revealed that circuit configuration (47.59%) and step frequency (47.18%) are the dominant predictive factors, while body mass contributed only 5.22% due to mechanical saturation in the carpet structure. This study confirms that RFR is effective as a predictive model for optimizing biomechanical energy harvesting systems in public infrastructure.
Co-Authors Andika Wijaya, Andika Assyifa Mourlina Faraquinsha Citra Anggraini Damayanti, Nita Rosa Dewi Permata Sari Edi Surya Negara Fairuz Attalah Fajarino, Aldo Hibrizi, Dzaky Rafif Islami, Miftahu Khoiri Oktarina, Yurni Pola Risma Rahman, Raihan Aldiaz Renny Maulidda, Renny Sonny Syahputra Tresna Dewi Tresna Dewi Yesi Novaria Kunang Yurni Oktarina Yurni Oktarina