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All Journal Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Jurnal Sisfotek Global Al-Khidmah Jurnal Pengabdian Masyarakat
Yani Koerniawan
Akademi Komunitas Toyota Indonesia
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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IMPLEMENTASI JIT PADA TAHAP SERAH DAN TERIMA KOMPONEN RING PISTON ENGINE DI PT. TMMI Yani Koerniawan; Suhermanto Suhermanto; Anwar Hilman
Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Vol 22, No 2 (2022): JURNAL SAINS DAN TEKNOLOGI
Publisher : Sekolah Tinggi Teknologi Industri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36275/stsp.v22i2.540

Abstract

Through the background handover stage and receive the piston engine ring component conducted by PT. Toyota Manufacturing Motor Indonesia (TMMI) there are wasteful activities or do not have added value. Then the part of the way to eliminate waste in the handover activity and accept the component is by just in time (JIT), then later there will be eliminated stages. Through this research has the aim to analyze the implementation of Just In Time and the influence of the implementation itself on these components. The method used in this study is Root Cause Analysis (RCA), with the aim of eliminating wasteful activities into just in time activities. Then the result of this study is the identification of the existence of 3 roots that cause the problem of the wasteful activity. Just in time implementation can eliminate activities which initially had 9 process activities for handover and accept it into 4 process activities. The inspection section does not require back to carry out handover activities and accept the user of the goods. So that time will be more efficient than 33 minutes to 16 minutes by one time the handover activity and receive the component. Conclusion from handover and receive directly from the supplier of the production line (place of the user). Then causes the component does not require back to be stored in a transit warehouse, in the end the transit warehouse will disappear.
Multi-Sensor Based Remaining Useful Life Prediction of Bearing Motors: A Comparative Study of LSTM and CNN Models Yani Koerniawan; Indrawan Indrawan; Raynaldi Yudha Prasetya; Wingky Kurniawan
JURNAL SISFOTEK GLOBAL Vol 16, No 1 (2026): JURNAL SISFOTEK GLOBAL
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/sisfotek.v16i2.16236

Abstract

Accurate Remaining Useful Life (RUL) prediction is essential for implementing effective predictive maintenance strategies in industrial rotating machinery. Bearing motors are particularly critical components whose unexpected failure may cause severe production losses and safety risks. This study presents a comparative investigation of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures for RUL prediction using multi-sensor monitoring data. The dataset consists of 1000 days of simulated operational data from three bearing motors under varying degradation conditions. Five sensor parameters are considered: vibration (RMS), acoustic emission, temperature, stator current, and rotational speed (RPM). After preprocessing and sliding-window segmentation, 2910 time-series sequences were generated and divided into training, validation, and test sets. Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). Experimental results show that LSTM significantly outperforms CNN, achieving an R² of 0.9877 on the test dataset, while CNN achieved R² below 0.34. The findings confirm the importance of temporal dependency modeling in long-horizon degradation prediction and provide guidance for selecting deep learning architectures in predictive maintenance applications.
Open PLC: an Open Source Solution For Training Of Non Maintenance Staff Yani Koerniawan; Rinaldi Rinaldi
Al-Khidmah Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): JANUARI-APRIL
Publisher : Institute for Research and Community Service (LPPM) of the Islamic University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56013/jak.v6i1.5455

Abstract

Programmable Logic Controllers (PLCs) are essential in modern industrial automation and Industry 4.0 environments. However, in many vocational institutions and industrial training centers, PLC access is limited primarily to maintenance departments due to high hardware costs, proprietary software licensing, and restricted system flexibility. This limitation reduces opportunities for non-maintenance staff, students, and interdisciplinary engineering practitioners to develop practical automation competencies. To address this gap, a community service program was conducted at Akademi Komunitas Toyota Indonesia, Karawang, utilizing OpenPLC—an open-source, IEC 61131-3–compliant PLC platform—as a low-cost and flexible training solution. The program involved structured workshops, hands-on practical sessions, and project-based learning activities using affordable hardware such as Arduino and Raspberry Pi. Participants were trained to design, simulate, and implement basic control systems, as well as integrate PLC logic with IoT and simple SCADA environments. The results demonstrate that OpenPLC effectively improves participants’ understanding of PLC programming concepts and enhances their practical skills in automation system development. The open-source nature of the platform reduces financial barriers while enabling broader experimentation beyond maintenance-oriented applications. This initiative shows that OpenPLC can serve as an accessible and sustainable training tool to expand industrial automation competencies across non-maintenance domains.
Co-Authors Anwar Hilman Indrawan Indrawan Raynaldi Yudha Prasetya Rinaldi Rinaldi Suhermanto Suhermanto Wingky Kurniawan