EKSPLORASI PENGGUNAAN ALGORITMA K-MEANS CLUSTERING UNTUK ANALISIS TREN HARGA KOMODITAS WILAYAH INDONESIA TIMUR

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Published: Oct 20, 2020
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Clustering, Commodities, K-Means, Machine Learning, Trends
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Marchel Yusuf Rumlawang Arpipi

Abstract

The application of machine learning has been very useful in decision making and analysis in various industrial fields. One of the most frequently used algorithms is K-Means for clustering. This algorithm is used to analyze commodity price trends in Eastern Indonesia. The purpose of this study is to see how K-Means works in grouping each commodity in a region into several clusters, calculating the average silhouette score produced, and measuring the extent to which each sample in a particular cluster matches its closest group compared to its closest neighbors. The market segmentation results show that each group with similar behavior and characteristics can be treated differently in marketing and pricing strategies, especially for commodities such as rice and beef, as well as cooking oil and sugar, which influence each other in such a way that if the price of one commodity rises, the price of other commodities will also rise.

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Vol. 15 No. 2 (2020): Jurnal Komputer dan Informatika
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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