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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Teknik Industri Terintegrasi (JUTIN)
Koerniawan, Yani
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Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Mengurangi Downtime Mesin Conveyor di Asembly Line Bodi Kendaraan dengan Metode Quality Control Circle Rinaldi, Rinaldi; Indratama, Aria; Prasetya, Raynaldi Yudha; Koerniawan, Yani
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 2 (2025): April
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i2.44354

Abstract

The operation of conveyor machines as one of the manufacturing production tools inevitably carries the risk of line stop issues, which negatively impact efficiency due to machine or robot errors and halts. This study discusses improvements to reduce downtime of conveyor machines widely used at PT XYS by applying the Quality Control Circle (QCC) method. The results indicate that machine errors were primarily caused by the controller's temperature exceeding standard limits, leading to damage in the output power voltage due to instability. As a countermeasure, a cooling fan was installed to lower the controller temperature. This effort successfully reduced the average monthly downtime from 1,468 minutes to 476 minutes. The improvement was further supported by updates to the Standard Operating Procedure (SOP) and the implementation of a preventive maintenance checksheet.
Literature review : Conditional based maintenance in manufacture industry Koerniawan, Yani; Wahyu, Andhika
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 7 No. 4 (2024): October
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v7i4.34377

Abstract

Manufacturing companies are increasingly dependent on the performance of their equipment to remain competitive. The best performance equipment demands accurate and timely maintenance. ConditionBased Maintenance (CBM) is a strategy to prevent functional failures or a significant performance decrease of the monitored equipment. CBM which relies on a wide range of resources and techniques required to detect abnormal situations or predict the future condition of an asset. This paper will create framework to be a basic of guidlenes for selecting parameters. And emphasize framework with literature reviews. Then develop guidelines based on framework which emphasized by literature review. Finally, examine the guidelines by case studies to evaluate the effectiveness of proposed guidelines
LSTM-BASED MACHINE LEARNING FOR REMAINING USEFUL LIFE PREDICTION OF BEARING MOTORS USING MULTI-SENSOR MONITORING Koerniawan, Yani; Prasetya, Raynaldi Yudha
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8890

Abstract

This study presents an advanced Long Short-Term Memory (LSTM) machine learning framework for predicting the Remaining Useful Life (RUL) of bearing motors through multi-sensor monitoring. Critical parameters, including vibration (RMS), acoustic emission, temperature, stator current, and rotational speed (RPM), were simulated over a 1000-day operational period for three motors with varying conditions. Failure thresholds were defined to represent severe operational conditions. The LSTM model achieved RMSE values of 28.15, 30.29, and 29.21 days and R² values of 0.989, 0.9876, and 0.9877 for training, validation, and test datasets, respectively. These results demonstrate high predictive accuracy and reliability. Integrating multi-sensor data improves the model’s robustness and supports proactive maintenance planning. The study provides a foundation for future integration of LSTM-based predictive models with IoT-enabled real-time monitoring systems in industrial applications.
Co-Authors Indratama, Aria Prasetya, Raynaldi Yudha Rinaldi Rinaldi Wahyu, Andhika