call reviewer

Dear Dr.  at 

I hope that this email finds you well.

I am Supriyadi. the editor-in-chief of Jurnal Sistem dan Manajemen Industri (Journal System and Management of Industries), a journal on Industrial Engineering published by Universitas Serang Raya (Indonesia).

We currently have an article entitled Early Fault Detection System for Sugar Mill Machines through Various Machine Learning Approach. Considering your expertise in Fault Detection issues, I would like to invite you to serve as the Reviewer in our journal to review the article.

Abstract
The milling machine is a crucial aspect in the sugarcane agroindustry production system, a disturbed milling machine will cause a decrease in production efficiency, sap quality degeneration, and excessive energy consumption. 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 sugarcane agroindustry milling machines and evaluate the performance of various machine learning models on historical sensor data to identify the most promising approach. The method used in this research is empirical based using data collected from temperature, vibration, pressure and humidity sensors using a variety of machine learning including logistic regression, decision tree, and random forest. Results showed that random forest had the best accuracy with 98.13%, followed by decision tree with 97.87%, and logistic regression with 89.70%. Feature contribution analysis shows that the vibration signal is the most dominant contributing signal compared to other features. The results show that machine learning is a potential approach used to predict faults in sugarcane milling machines to help the sugarcane agriculture industry in making decisions when there is a disturbance in sugarcane milling machines.

We apologize for interrupting your time.

Sincerely,
Supriyadi
Managing Editor
Jurnal Sistem dan Manajemen Industri (Journal System and Management of Industries)
http://e-jurnal.lppmunsera.org/index.php/JSMI