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All Journal Jurnal Sistem dan Manajemen Industri IMTechno: Journal of Industrial Management and Technology
Thabed Tholib Baladraf
IPB University
Computer Science & IT Engineering Industrial & Manufacturing Engineering Transportation

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Early fault detection system for sugar mill machines through various machine learning approach Thabed Tholib Baladraf; Taufik Djatna; Agriananta Fahmi Hidayat; Akhmad Fatikhudin; Helynda Mulya Arga Retha; Zulfikar Dabby Anwar
Jurnal Sistem dan Manajemen Industri Vol. 9 No. 2 (2025): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v9i2.10530

Abstract

The milling machine is a crucial aspect of the sugarcane agroindustry production system; a disturbed milling machine will cause a decrease in production efficiency, sap quality degradation, and excessive energy consumption. An early fault anomaly detection system through machine learning is a solution to overcome the problems in sugarcane milling machines. The purpose of this research is to propose a system architecture design for early fault anomaly detection in sugarcane agroindustry milling machines and to evaluate the performance of various machine learning models on historical sensor data, identifying the most promising approach. This study proposes a novel anomaly detection framework for sugarcane milling machines to advance smart monitoring in agro-industrial systems. Using an empirical dataset of 7,673 sensor instances (temperature, vibration, pressure, and humidity), and applying several machine learning algorithms (logistic regression, decision tree, and random forest), the framework integrates multi-sensor data to improve fault prediction and reduce downtime. The results showed that the random forest had the best accuracy, at 98.13%, followed by the decision tree, at 97.87%, and logistic regression, at 89.70%. Feature contribution analysis reveals that the vibration signal is the most dominant contributing factor among other features. The results show that machine learning is a potential approach for predicting faults in sugarcane milling machines, which can help the sugarcane agriculture industry make informed decisions in the event of disturbances in these machines.
Komparasi Moving Average, Exponential Smoothing dan Naive Model dalam Peramalan Produktivitas Sereal di Indonesia Thabed Tholib Baladraf; Hifdzil Adila
IMTechno: Journal of Industrial Management and Technology Vol. 7 No. 2 (2026): Vol. 7 No 2 (2026) : Juli 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/imtechno.v7i2.7851

Abstract

Sereal (beras, jagung, dan gandumg) merupakan kebutuhan pangan utama bagi masyarakat Asia Tenggara, terkhusus Indonesia. Hal ini menjadikan sereal sebagai parameter ketahanan pangan suatu negara sehingga penting untuk dilakukan peramalan terhadap jumlah produksi sereal untuk digunakan perencanaan yang lebih efektif kedepannya dalam memenuhi kebutuhan pangan masyarakat Indonesia. Tujuan dari penelitian ini yaitu untuk membandingkan metode terbaik dalam meramalkan produktivitas sereal di Indonesia dengan menggunakan metode moving average, exponential smoothing, dan naive model. Metode penelitian yang digunakan yaitu pengambilan data sekunder dari World Bank dan United Nation Food Agriculture Organization (FAO) mengenai produktivitas sereal Indonesia pada tahun 1961-2020 dan dilakukan peramalan dengan menggunakan metode moving average, exponential smoothing, dan naive model dan uji validasi dengan parameter MAD dan MAPE. Hasil penelitian menunjukkan bahwa metode moving average, metode exponential smoothing dan naive method dapat membantu dalam memprediksi/meramalkan persediaan sereal kedepannya di Indonesia dengan baik. Hasil peramalan terbaik didapatkan oleh naive method dengan nilai MAD sebesar 2.201.600 dan nilai MAPE sebesar 5,27%.
Co-Authors Akhmad Fatikhudin Helynda Mulya Arga Retha Hidayat, Agriananta Fahmi Hifdzil Adila Taufik Djatna Zulfikar Dabby Anwar