@thesis{thesis,
		author={Pratama Putera  Rizki and Romy Wicaksono  Irham},
		title ={PREDIKSI DAYA REGENERATIF PADA PENGEREMAN REGENERATIF KENDARAAN LISTRIK MENGGUNAKAN MACHINE LEARNING},
		year={2022},
		url={http://156.67.221.169/5338/},
		abstract={This study conducts a simulation to forecast regenerative power of a regenerative braking using a model
that has regenerative braking system. This study proposes analyzing forecasted results of regenerative
power using training data obtained by the model and doing validation using test data. Power forecasting
simulation conducted by using regression; furthermore, regressions used for power forecasting are SVR
(Support Vector Regression) and linear regression. Although this study uses machine learning for power
forecasting, the forecasted data have difference results compared to experimental data based on regression
metrics. According to power forecasting simulation using SVR, the metrics obtained for ultracapacitor
installed in parallel and single ultracapacitor, respectively, are: 1) Coefficient of determination scores 0.69
and 0.85; 2) MAE of 3.68 and 2.09; and 3) MSE of 23.16 and 11.75; In the other hand, according to power
forecasting simulation using linear regression, the metrics obtained for ultracapacitor installed in parallel
and single ultracapacitor, respectively, are: 1) Coefficient of determination of 0.71 and 0.91; 2) MAE of
3.49 and 1.75; and 3) MSE of 21.83 and 7.28.}
		}