Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System

Nisa'ul Hafidhoh; Ardian Prima Atmaja; Gus Nanang Syaifuddiin; Ikhwan Baidlowi Sumafta; Salva Mahardhika Pratama; Hafsah Nur Khasanah

doi:10.14710/jmasif.15.1.63641

DOI: https://doi.org/10.14710/jmasif.15.1.63641

Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System

Nisa'ul Hafidhoh

, Ardian Prima Atmaja, Gus Nanang Syaifuddiin, Ikhwan Baidlowi Sumafta, Salva Mahardhika Pratama, Hafsah Nur Khasanah

Politeknik Negeri Madiun, Indonesia

Received: 8 May 2024; Revised: 22 May 2024; Accepted: 25 May 2024; Available online: 31 May 2024; Published: 31 May 2024.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

BibTex Citation Data :

@article{JMASIF63641,
    author = {Nisa'ul Hafidhoh and Ardian Prima Atmaja and Gus Nanang Syaifuddiin and Ikhwan Baidlowi Sumafta and Salva Mahardhika Pratama and Hafsah Nur Khasanah},
    title = {Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System},
    journal = {Jurnal Masyarakat Informatika},
  volume = {15},
    number = {1},
    year = {2024},
    keywords = {CRISP-DM, exploratory data analysis, machine failure, machine learning, predictive maintenance system},
    abstract = {In facing the Industrial Revolution 4.0, technologies such as the Internet of Things, Big Data and Artificial Intelligence are key to industrial modernization. Machine Learning approach as a part of artificial intelligence is used to process high-dimensional multivariable data and extract hidden relationships in complex industrial environments. In this research, Machine Learning is used to classify machine failures in building a Predictive Maintenance System. This research adopts the CRISP-DM (Cross Industry Standard Process for Data Mining) cycle which consists of the business understanding, data understanding, data preparation, modeling, evaluation and deployment stages. The Predictive Maintenance Dataset in the form of synthetic data used in this research reflects real industrial situations consists of 10,000 rows of data with ten features. Types of machine failure are classified into Heat Dissipation Failure, Power Failure, Overstrain Failure, and Tool Wear Failure. Exploratory Data Analysis is carried out to obtain a summary and visualization of data. The machine learning approach uses the Logistic Regression method and the model evaluation results reach an accuracy of 96.87%, in accordance with the data success criteria. The results of the machine learning modelling developed are implemented in a web-based Predictive Maintenance System application to make it easier for users to monitor machine conditions and predict machine failures.},
   issn = {2777-0648},   pages = {56--66}  doi = {10.14710/jmasif.15.1.63641},
    url = {https://ejournal.undip.ac.id/index.php/jmasif/article/view/63641}
}

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Abstract

In facing the Industrial Revolution 4.0, technologies such as the Internet of Things, Big Data and Artificial Intelligence are key to industrial modernization. Machine Learning approach as a part of artificial intelligence is used to process high-dimensional multivariable data and extract hidden relationships in complex industrial environments. In this research, Machine Learning is used to classify machine failures in building a Predictive Maintenance System. This research adopts the CRISP-DM (Cross Industry Standard Process for Data Mining) cycle which consists of the business understanding, data understanding, data preparation, modeling, evaluation and deployment stages. The Predictive Maintenance Dataset in the form of synthetic data used in this research reflects real industrial situations consists of 10,000 rows of data with ten features. Types of machine failure are classified into Heat Dissipation Failure, Power Failure, Overstrain Failure, and Tool Wear Failure. Exploratory Data Analysis is carried out to obtain a summary and visualization of data. The machine learning approach uses the Logistic Regression method and the model evaluation results reach an accuracy of 96.87%, in accordance with the data success criteria. The results of the machine learning modelling developed are implemented in a web-based Predictive Maintenance System application to make it easier for users to monitor machine conditions and predict machine failures.

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Keywords: CRISP-DM, exploratory data analysis, machine failure, machine learning, predictive maintenance system

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Section: Research Article

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In Vol 15, No 1 (2024): May 2024

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