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All Journal Jurnal TIN Universitas Tanjungpura ELKHA : Jurnal Teknik Elektro OPSI Jurnal Edukasi dan Penelitian Informatika (JEPIN) OPERATION EXCELLENCE: Journal of Applied Industrial Engineering GERVASI: Jurnal Pengabdian kepada Masyarakat Industri Inovatif : Jurnal Teknik Industri INAQUE: Journal of Industrial & Quality Engineering Jurnal Teknik Industri Terintegrasi (JUTIN)
Yopa Eka Prawatya
Universitas Tanjungpura Pontianak
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Journal : OPSI

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Utilizing machine learning for predictive maintenance of production machinery in small and medium enterprises Prawatya, Yopa Eka; Djanggu, Noveicalistus H; Rahmahwati, Ratih
OPSI Vol 18 No 1 (2025): OPSI - June 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i1.13479

Abstract

Predictive maintenance involves the early detection of potential machine failures and subsequent maintenance to prevent such failures. Machine learning is a pertinent statistical method for predictive maintenance, enabling the early detection of machine failures and the implementation of preventive measures through a model. The development of the machine learning model commences with data collection from the machine, encompassing vibration, acceleration, machine temperature, and machine sound, facilitated by a microcontroller equipped with sensors. Subsequently, the data undergoes cleaning, including removing outliers or missing values and standardization. Data is partitioned into 70% allocated for training and 30% for testing. After determining hyperparameters and their values through hyperparameter tuning, the training data is utilized to train machine learning models, such as K-nearest neighbor, decision tree, and random forest models. Post-training, the models are evaluated using the remaining test data, employing performance metrics such as accuracy, precision, recall, and F1-score. The random forest model excels due to its utilization of a substantial number of trees for predictions and the full exploitation of the variables which F1-score is 91.22%. The best-performing model is subsequently deployed into a monitoring system, providing real-time machine condition predictions. The deployment results validate the accurate prediction of machine failures.
Co-Authors Afrilia, Nurul Anggoro, Franciskus Febry Ateng, Verico Ekawijaya Aulia, Yayang Bartolomeus Lumbantoruan Delvinella, Wella Evy Wardenaar Febri Prima Hafzoh Batubara Hermawan, Yuda Isworo, Eko Ivan Sujana Khairunisa, Aminah Lumbantoruan, Bartolomeus Nasher, Ammarullah Rusdyllah Ethwear Servac Noveicalistus H Djanggu Noveicalistus H Djanggu Putri, Famelga Clea Rahmahwati, Ratih Ratih Rahmawati Retno Budi Lestari SANDI KURNIAWAN Saputri, Rustati Setyautama, Hanisa Silvia Uslianti Syahmi Sajid Tri Wahyudi Umaisyah, Umaisyah Wanti Fitrianti Wibowo, Andi Agung Wijayanto, Dedi Zulfahmi, Ahmad Zulmuis, Zulmuis