IRFAN DWI PRAWIRA, - (2025) CLUSTERING OF SCREW PRESS MACHINE CONDITIONS USING THE AGGLOMERATIVE HIERARCHICAL CLUSTERING. Engineering and Technology Journal, 10 (11). pp. 7923-7930. ISSN 2456-3358

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Abstract

The screw press machine is an important component in the palm oil processing process that functions to extract oil from palm kernels. Continuous use of the machine can reduce performance and disrupt the production process. Therefore, machine condition analysis is needed to support preventive maintenance strategies. One method that can be used is the clustering technique. Clustering is a technique for grouping data based on specific parameters to form classes with similar characteristics. This study applied the Agglomerative Hierarchical Clustering (AHC) method with a single linkage approach to group the conditions of screw press machines based on data obtained from PT. XYZ for the period April-May 2024, with a total of 23,002 data points. The research stagesincluded data selection, data pre-processing, normalization using Z-score, clustering with AHC, and evaluation using the Silhouette Coefficient and Davies-Bouldin Index (DBI). The results showed that the AHC method was able to form a representative grouping of machine conditions. Evaluation using the Silhouette Coefficient produced the best number of clusters at 2 clusters witha value of 0.591, indicating that the clustering quality was in the good category. Meanwhile, evaluation using DBI showed thebestnumber of clusters at 4 clusters with a value of 0.404, indicating that the separation between clusters was quite good. Thesefindings can be used as a reference in determining preventive machine maintenance policies so as to increase production efficiency.

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