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All Journal Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Recent in Engineering Science and Technology
Sonki Prasetya
Department of Mechanical Engineering, Politeknik Negeri Jakarta, Depok, Indonesia
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TRAINING ON THE CONVERSION TECHNIQUES OF GASOLINE MOTORCYCLES TO ELECTRIC FOR AUTOMOTIVE COMMUNITIES Fuad Zainuri; Muhammad Todaro; Sonki Prasetya; Muhammad Hidayat Tullah; Rahmat Noval; Muhammad Ridwan
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 5 No. 3 (2025): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v5i3.3007

Abstract

Vehicles powered by gasoline still dominate the roads in Jakarta and its surrounding areas. This has led to uncontrollable levels of pollution in Jakarta. Electric vehicles are one solution to reduce the pollution levels in the city. Since it is not feasible to eliminate all gasoline-powered vehicles to switch to electric vehicles, converting these vehicles into electric ones is a viable solution to address this issue. One of the most widely used gasoline-powered vehicles is the motorcycle. The large number of gasoline motorcycles in Jakarta presents an opportunity for automotive communities, such as conventional vehicle workshops, to participate in the transition to electric vehicles. However, the automotive community must be skilled in converting gasoline motorcycles to electric ones. Training on converting gasoline motorcycles to electric has been conducted for the automotive community in Jakarta. Training participants come from various educational backgrounds, ranging from high school/vocational school to university graduates. As a result, the training participants have been able to increase their knowledge and skills in converting gasoline motorcycles to electric ones by up to 20.54%.
Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries Idrus Assagaf; Agus Sukandi; Parulian Jannus; Sonki Prasetya; Asep Apriana; Ega Edistria; Abdul Azis Abdillah
Recent in Engineering Science and Technology Vol. 3 No. 4 (2025): RiESTech Vol. 3 No. 4 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i04.127

Abstract

Accurately forecasting the Remaining Useful Life (RUL) of lithium-ion batteries is critical for optimizing battery management and ensuring operational reliability. This study compares the performance of two deep learning architectures—a Multilayer Perceptron (MLP) and a one-dimensional Convolutional Neural Network (1D-CNN)—in predicting RUL using datasets from CALCE batteries B35, B36, and B37. Data preprocessing involved outlier removal, missing value handling, and feature normalization, with key features extracted including Resistance, Constant Voltage Charging Time (CVCT), and Constant Current Charging Time (CCCT). Correlation analyses confirmed strong relationships between these features and RUL. Both models were trained and validated on preprocessed data, and their predictive accuracies were assessed using Root Mean Square Error (RMSE) and coefficient of determination (R2). Results indicated that while both architectures effectively captured battery degradation patterns, the MLP consistently outperformed the 1D-CNN, achieving on average 5% lower RMSE and 1.5% higher R2 across all tested batteries. These findings suggest that simpler fully connected networks may suffice for this forecasting task under the given feature set and preprocessing conditions. This work provides valuable insights into neural network model selection for battery health prognostics, guiding the development of efficient and accurate predictive maintenance strategies.
Co-Authors Abdul Azis Abdillah Agus Sukandi Asep Apriana Ega Edistria Fuad Zainuri Idrus Assagaf Muhammad Hidayat Tullah Muhammad Ridwan Muhammad Todaro Parulian Jannus Rahmat Noval