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All Journal Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri PELS (Procedia of Engineering and Life Science)
Rasyid, Mohammad Andi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering

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Maximizing Machine Efficiency in Indonesia's Noodle Industry: Memaksimalkan Efisiensi Mesin di Industri Mie Indonesia Rasyid, Mohammad Andi; Sukmono, Tedjo
Procedia of Engineering and Life Science Vol. 5 (2024): Proceedings of the 7th Seminar Nasional Sains 2024
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/pels.v7i0.1530

Abstract

This study utilizes the Total Productive Maintenance (TPM) method, focusing on the Overall Equipment Effectiveness (OEE) to evaluate machine efficiency within a notable Indonesian noodle manufacturer. In the rapidly growing food manufacturing industry, particularly dry and instant noodles, machine performance is critical for meeting increasing domestic and international market demands. Results reveal that the OEE values are below industry standards, primarily due to speed reductions, idle and minor stoppages, process defects, and breakdown losses. These findings emphasize the urgent need for preventive maintenance strategies to mitigate downtime and enhance productivity, providing valuable insights for global industries facing similar challenges. Highlights: Rapid growth in the food manufacturing industry demands increased productivity. Machine performance is crucial for maintaining optimal production processes. Total Productive Maintenance (TPM) and Overall Equipment Effectiveness (OEE) methods help measure and improve machine efficiency. Keywords: Total Productive Maintenance, Overall Equipment Effectiveness, Six Big Losses.
Predictive Maintenance on Dry 8 Production Machine Line Using Support Vector Machine (SVM) Rasyid, Mohammad Andi; Sukmono, Tedjo; Jakaria, Ribangun Bamban
JTI: Jurnal Teknik Industri Vol 10, No 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v10i2.29802

Abstract

Machines are the main element in manufacturing companies, and the role of machine performance is vital in the production process. Downtime problems caused by machine damage can significantly affect company productivity. This research implements the support vector machine (SVM) method for predicting Dry 8 production machine line maintenance, which aims to reduce downtime and increase productivity. The SVM method is known for its high accuracy and low error rate. The evaluation process used four kernel functions: linear, radial basis function (RBF), polynomial and sigmoid. The linear kernel function performed best with 99.8% accuracy, 83% precision, recall, and f1-score. These results show that the SVM method can be a viable solution to improve the efficiency of machine maintenance. Keywords: Confusion Matrix, Machine Learning, Predictive Maintenance, Support Vector Machine 
Co-Authors Ribangun Bamban Jakaria Tedjo Sukmono