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.

Editor(s): Salman Alfarisi

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, kegagalan mesin, machine learning, predictive maintenance system},
    abstract = {  Dalam menghadapi Revolusi Industri 4.0, teknologi seperti Internet of Things, Big Data, dan Kecerdasan Buatan menjadi kunci dalam modernisasi industri. Pendekatan Machine Learning digunakan untuk memproses data multivariabel berdimensi tinggi dan mengekstrak hubungan tersembunyi dalam lingkungan industri yang kompleks. Machine Learning digunakan untuk mengklasifikasikan kegagalan mesin dalam membangun Predictive Maintenance System. Penelitian ini mengadopsi siklus CRISP-DM (Cross Industry Standard Process for Data Mining) yang terdiri dari tahap business understanding, data understanding, data preparation, modelling, evaluation dan deployment. Predictive Maintenance Dataset berupa data sintetis yang digunakan dalam penelitian ini mencerminkan situasi industri nyata terdiri dari 10.000 baris data dengan sepuluh fitur. Jenis kegagalan mesin diklasifikasikan menjadi Heat Dissipation Failure, Power Failure, Overstrain Failure, dan Tool Wear Failure. Exploratory Data Analysis dilakukan untuk mendapatkan ringkasan dan visualisasi data. Pendekatan machine learning menggunakan metode Logistic Regression dan hasil evaluasi model mencapai akurasi 96,87%, sesuai dengan kriteria sukses data. Hasil pemodelan machine learning yang dikembangkan kemudian diimplementasikan dalam aplikasi Predictive Maintenance System berbasis web untuk memudahkan pemantauan kondisi mesin dan prediksi kegagalan mesin oleh pengguna.                                            },
   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}
}

Citation Format:

Abstract

Dalam menghadapi Revolusi Industri 4.0, teknologi seperti Internet of Things, Big Data, dan Kecerdasan Buatan menjadi kunci dalam modernisasi industri. Pendekatan Machine Learning digunakan untuk memproses data multivariabel berdimensi tinggi dan mengekstrak hubungan tersembunyi dalam lingkungan industri yang kompleks. Machine Learning digunakan untuk mengklasifikasikan kegagalan mesin dalam membangun Predictive Maintenance System. Penelitian ini mengadopsi siklus CRISP-DM (Cross Industry Standard Process for Data Mining) yang terdiri dari tahap business understanding, data understanding, data preparation, modelling, evaluation dan deployment. Predictive Maintenance Dataset berupa data sintetis yang digunakan dalam penelitian ini mencerminkan situasi industri nyata terdiri dari 10.000 baris data dengan sepuluh fitur. Jenis kegagalan mesin diklasifikasikan menjadi Heat Dissipation Failure, Power Failure, Overstrain Failure, dan Tool Wear Failure. Exploratory Data Analysis dilakukan untuk mendapatkan ringkasan dan visualisasi data. Pendekatan machine learning menggunakan metode Logistic Regression dan hasil evaluasi model mencapai akurasi 96,87%, sesuai dengan kriteria sukses data. Hasil pemodelan machine learning yang dikembangkan kemudian diimplementasikan dalam aplikasi Predictive Maintenance System berbasis web untuk memudahkan pemantauan kondisi mesin dan prediksi kegagalan mesin oleh pengguna.

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

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Section: Research Article

Language : ID

In Vol 15, No 1 (2024): Jurnal Masyarakat Informatika

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