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All Journal Prosiding Seminar Nasional Teknik Mesin Jurnal Mekanik Terapan Mitra Akademia: Jurnal Pengabdian Masyarakat Recent in Engineering Science and Technology
Asep Apriana, Asep
Jurusan Teknik Mesin, Politeknik Negeri Jakarta, Jl. Prof G E Siwabesi Kampus Baru UI Depok, 16425
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Comparative Analysis of Regression Methods for Estimation of Remaining Useful Life of Lithium Ion Battery Assagaf, Idrus; Abdillah, Abdul Azis; Edistria, Ega; Sukandi, Agus; Prasetya, Sonki; Apriana, Asep; Nugroho; Kamil, Raihan
Recent in Engineering Science and Technology Vol. 3 No. 01 (2025): RiESTech Volume 03 No. 01 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i01.93

Abstract

Lithium batteries play a critical role in modern technological applications, including electric vehicles and portable electronic devices. Ensuring accurate estimation of their remaining useful life is essential to improve system efficiency and reliability. This study focuses on predicting the remaining useful life of lithium batteries using advanced regression methods. Data were collected from lithium battery charge-discharge cycles, encompassing key operational parameters such as voltage, current, and temperature. The analysis employed several regression models, including linear regression, lasso regression, and Ridge regression, to identify relationships between these parameters and battery life. The models were evaluated based on estimation accuracy, with Root Mean Square Error (RMSE) as the primary performance metric. The findings demonstrate that regression methods can effectively capture non-linear relationships between input variables and the remaining useful life, with lasso and Ridge regression showing superior performance in reducing prediction errors. These results underscore the potential of regression-based approaches in providing robust and reliable estimations of battery life. The conclusions highlight the importance of these models for developing predictive battery management systems, which can optimize battery performance and extend their operational lifespan across various applications. This research establishes a solid foundation for future studies on intelligent battery health monitoring and management.
Pengaruh Lebar Pita Terhadap Tegangan Listrik yang Dihasilkan oleh Generator Windbelt Mulyana, Fajar; Gamayel, Adhes; Zaenudin, Mohamad; Apriana, Asep
Jurnal Mekanik Terapan Vol 4 No 3 (2023): Desember 2023
Publisher : Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/jmt.v4i3.6031

Abstract

Salah satu alat pemanen energi alternatif adalah generator Winbelt. Winbelt adalah alat konversi energi yang mengubah gerakan angin menjadi energi listrik. Dalam generator Winbelt terdapat suatu alat induksi eletromagnetik yang terdiri dari magnet dan kumparan. Karena gerakan angin yang mengenai belt, sehingga menggerakan magnet secara bolak balik terhadap kumparan, maka terjadilah induksi elektromangnetis yang dapat  menghasilkan listrik. Penelitian ini membahas pengaruh lebar Pita Winbelt terhadap energi listrik yang dihasilkan dengan variasi kecepatan angin yang diberikan. Metodologi penelitian melibatkan pembuatan beberapa prototipe Windbelt dengan panjang pita yang sama namun berbeda dalam hal tingkat kelebaran. Selanjutnya, prototipe tersebut ditempatkan dalam lingkungan yang dapat mensimulasikan angin atau aliran udara yang cukup untuk menghasilkan getaran pada pita. Tegangan listrik yang dihasilkan oleh setiap prototipe diamati dan diukur menggunakan perangkat pengukur yang sesuai. Setelah dilakukan pengukuran, didapatkan  nilai tengangan listrik tertinggi dihasilkan pada variasi lebar Pita 18 mm dan dengan kecepatan angin 3.0 m/s  sebesar 2,62 volt.
Pelatihan Dasar Preventif Maintenance 1 Untuk Masyarakat Kecamatan Cipayung, Depok Rahayu, Minto; Apriana, Asep; Abdillah, Aziz
Mitra Akademia: Jurnal Pengabdian Masyarakat Vol 2 No 1 (2019): Mitra Akademia: Jurnal Pengabdian Masyarakat
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/mapnj.v2i1.1978

Abstract

Pemda Depok mempunyai Tempat Pengolahan Sampah (TPS) di Kecamatan Cipayung. Selama ini masyarakat Cipayung hanya menjadi penonton dan menerima dampak negatif dari keberadaan TPS, tanpa dapat berkontribusi dan mendapatkan keuntungan keekonomian. Progran Studi Alat Berat mempunyai tanggung jawab terhadap pemberdayaan masyarakat sehingga bermaksud melaksanakan pengabdian kepada masyarakat sesuai dengan kompetensi, yaitu Pelatihan dasar Preventive Mantenance 1 (PM 1) pada masyarakat Cipayung, Depok. Hasilnya masyarakat mempunyai keterampilan preventive maintenance, khususnya pada mesin pengolah sampah sehingga masyarakat dapat  menjadi  mitra Pemda Depok dalam hal pelayanan perawatan berkala terhadap unit alat berat.
Comparing MLP and 1D-CNN Architectures for Accurate RUL Forecasting in Lithium Batteries Assagaf, Idrus; Sukandi, Agus; Jannus, Parulian; Prasetya, Sonki; Apriana, Asep; Edistria, Ega; Abdillah, Abdul Azis
Recent in Engineering Science and Technology Vol. 3 No. 04 (2025): RiESTech Volume 03 No. 04 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 Abdillah, Abdul Azis Abdillah, Aziz Adhes Gamayel Agus Sukandi Assagaf, Idrus Azis, Muhammad Abdul Chen, Ching-Cheng Edistria, Ega Eko, Nugroho Ernando, Arnol Wibi Fajar Mulyana Fitri Wijayanti Gumilang, Muhammad Gagas Hidayatulloh, Nur Jannus, Parulian Kamil, Raihan Lumbantobing, Fransiscus J. Makhluf, Muhammad Nadim MINTO RAHAYU Mohamad Zaenudin Nabildan, Marwan Nugraha, Fajar Wahid Nugroho Rahmiati, Tia Saad, Siti Asmahani Simanungkalit, Jachob Radonov Sonki Prasetya Umami, Febri Yusril Wahyudi, Ahmad Nur Sahid Wijaya, Fitri Yuliono, Agung Hidayat Dharma