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Budi Hartono
Universitas Stikubank, Semarang
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Analisis Metode Elbow SSE, Silhouette Score, dan Jaccard Stability dalam Pemilihan Jumlah Klaster Data yang Optimal Budi Hartono; Veronica Lusiana
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9271

Abstract

This study discusses the selection of the optimal number of clusters (K) in the K-Means algorithm by utilizing a combination of the Elbow method with the SSE (Sum of Squared Errors) and Silhouette Score metrics. The main problem is that the optimal K value is unknown. Choosing K that is too small can combine different patterns (under-clustering), and choosing K that is too large can break the same pattern into several clusters (over-clustering). The experiment used two-dimensional test data with variations in the number of data 20, 30, 40, 50, and 60. K-Means was run in the range of K = 2 to K = 8, then the SSE value was calculated to form the Elbow curve and the average Silhouette value to evaluate the quality of the cluster. This study added a cluster stability test using the Jaccard Stability value. The highest Silhouette value of 0.4619 was obtained from the data 20 for K = 2. The highest Jaccard stability value of 0.9507 was obtained from 60 data sets for K = 2. The experimental results show that the Elbow method, Silhouette value, and Jaccard stability can be used complementarily in determining the optimal K. In some test data, both metrics produce consistent K recommendations, while in certain test data, Elbow can provide several candidates, so that validation using the Silhouette value is needed to select the optimal K.
Implementasi Pengelompokan Citra Batik dengan Pendekatan Metode Graph-Based Multi-View Clustering Budi Hartono; Veronica Lusiana
TIN: Terapan Informatika Nusantara Vol 7 No 3 (2026): August 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i3.10967

Abstract

Batik is a cultural heritage of Indonesia characterized by a rich array of motifs, colors, geometric patterns, and complex textures. This visual complexity makes Batik images compelling subjects for analysis using image processing and machine learning approaches, particularly for unsupervised clustering. A primary challenge in this research is effectively representing Batik's visual characteristics so pattern similarities can be accurately identified. Three texture features contrast, entropy, and energy serve as distinct "views" of Batik characteristics. These feature values are obtained by extracting them using the Gray-Level Co-occurrence Matrix (GLCM) method. The study utilizes a Graph-based Multi-View Clustering approach to group Batik images based on inter-image similarity. The study constructs a combined consensus graph with 30 nodes and 219 edges by integrating contrast, energy, and entropy features. This graph exhibits a density of 0.503 and forms a single connected graph component. The density result indicates that 50.3% of all possible image pairs share a connection. The resulting graph forms a connected structure that incorporates information from all three views, yielding a more comprehensive clustering representation than any single view. The combined consensus graph establishes an interconnected structure that produces a representative clustering of Batik images.
Co-Authors Veronica Lusiana