Penerapan Agglomerative Hierarchical Clustering dalam Pengelompokan Saham BEI Berdasarkan Kapitalisasi Pasar dan Harga Saham

Mhd. Fauzal Pratama (STIKOM Tunas Bangsa)
Riki Winanjaya (STIKOM Tunas Bangsa)
Bahrudi Efendi Damanik (STIKOM Tunas Bangsa)

Jurnal Rekayasa Proses dan Industri Terapan
Vol. 4 No. 3 (2026): Agustus: Jurnal Rekayasa Proses dan Industri Terapan ( REPIT )

Stock clustering based on their characteristics is needed to obtain an overview of the similarities among stocks within a group. This study aims to apply the Agglomerative Hierarchical Clustering method to group stocks listed on the Indonesia Stock Exchange based on market capitalization and stock price (last price). The data used were IHSG Stock Data obtained from Kaggle, consisting of 824 stocks up to 2023. The research stages included data preparation, normalization using Min-Max Scaling, distance calculation using Euclidean Distance, linkage formation using Ward’s Linkage, dendrogram visualization, and cluster formation. The results showed that three clusters were formed, consisting of 7 stocks in Cluster 1, 751 stocks in Cluster 2, and 66 stocks in Cluster 3. Cluster 1 had an average stock price of IDR 33,875 and an average market capitalization of IDR 467.79 trillion. Cluster 2 had an average stock price of IDR 544.28 and an average market capitalization of IDR 46.23 trillion, while Cluster 3 had an average stock price of IDR 7,236.44 and an average market capitalization of IDR 682.36 trillion. These results indicate that Agglomerative Hierarchical Clustering can be applied to map stocks based on similarities in market capitalization and stock price characteristics