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Abstract
Baterai Lithium Polymer (Li-Po) Karena beratnya yang ringan dan kepadatan energinya yang tinggi, banyak digunakan pada berbagai perangkat elektronik. Penelitian ini bertujuan membandingkan kinerja algoritma Decision Tree dan XGBoost dalam memprediksi Time to Full Charge (TTF) baterai Li-Po. Data diperoleh dari sistem monitoring berbasis ESP32 yang mengakuisisi parameter tegangan, arus, suhu, dan State of Charge (SoC) selama proses pengisian baterai. Evaluasi model dilakukan menggunakan metrik Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), dan koefisien determinasi (R²). Hasil pengujian menunjukkan bahwa Decision Tree menghasilkan MAE 44,5892 detik, RMSE 62,08 detik, MAPE 10,82%, dan R² 0,9905. Sementara itu, XGBoost menghasilkan MAE 47,1935 detik, RMSE 63,657 detik, MAPE 7,796%, dan R² 0,9906. Dengan nilai R2 di atas 0,99, kedua algoritma dapat digunakan untuk mengestimasi waktu pengisian baterai Li-Po.
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Copyright (c) 2026 Dio Lizwardi, Sabilal Rasyad, Tegar Prasetyo
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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H. Hilal and P. Saha, “Forecasting Lithium-Ion Battery Longevity with Limited Data Availability: Benchmarking Different Machine Learning Algorithms,” Dec. 2023, [Online]. Available: http://arxiv.org/abs/2312.05717
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