<![CDATA[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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