Penerapan Algoritma K-Means Clustering pada Kinerja Mesin Screw press

Fikri  Kurnia Rahman; Jasril; Suwanto  Sanjaya; Lestari  Handayani; Fitri  Insani

doi:10.47065/bit.v6i2.2002

Fikri Kurnia Rahman Universitas Islam Negeri Sultan Syarif Kasim, Indonesia
Jasril * Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Suwanto Sanjaya Universitas Islam Negeri Sultan Syarif Kasim, Indonesia
Lestari Handayani Universitas Islam Negeri Sultan Syarif Kasim, Indonesia
Fitri Insani Universitas Islam Negeri Sultan Syarif Kasim, Indonesia

DOI: https://doi.org/10.47065/bit.v6i2.2002

Abstract

The screw press is one of the machines used in the process of separating oil from tanks containing Fresh Fruit Bunches (FFB). The machine consists of a twin-screw system that functions to extract oil from the pressing unit, with back pressure applied by a hydraulic double cone. The mixed fruit residue is compreWCSSd, causing the oil contained within the residue to be released due to the pressure exerted by the press machine. Maintenance and repair of machinery are eWCSSntial activities to support productive operations in any sector. Therefore, it is necessary to conduct analysis to identify patterns in machine conditions within the factory. One effective approach to discovering machine condition patterns is through clustering techniques. Clustering is a method of grouping data based on certain parameters to form clusters of objects that share similar characteristics. In this study, data were collected from PT. XYZ for the period of April 2024 to May 2024, with a total of 23,002 records. The analysis was conducted using the K-Means Clustering algorithm, with testing carried out on 3 to 15 clusters. Based on the evaluation using the Davies-Bouldin Index (DBI), the most optimal clustering result was obtained with 3 clusters, achieving the lowest DBI value of 0.386. Meanwhile, using the Elbow Method, the optimal number of clusters was determined to be 4, as indicated by the Elbow point on the WCSS graph, with a Sum of Square Error (WCSS) value of 270. Therefore, it can be concluded that the clustering results using the K-Means Clustering algorithm are relevant for identifying machine condition patterns and are expected to assist in monitoring and managing the condition of the screw press machine.

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