Abstract
One of Indonesia’s export commodities is Electronic Integrated Circuits (EICs). Understanding the characteristics of EIC export destination countries is important for identifying export patterns and to support strategic decision-making. Clustering is one approach that can be used to analyze export destination countries by grouping them based on their similar characteristics. The objective of this study is to evaluate the performance of K-Means and K-Medoids algorithms in analyzing Indonesia’s EIC export destination countries. The data used in this study were obtained from Badan Pusat Statistik (BPS) and consist of export value and weight data from 2021 to 2025 containing 84 destination countries. This dataset is selected because limited studies have compared K-Means and K-Medoids on Indonesia’s EIC export destination countries. The data from BPS were then preprocessed, then normalized by applying Min-Max normalization method. The Elbow Method was subsequently employed to find the optimal number of clusters, with the analysis indicating that four clusters were optimal. Following the Elbow Method, K-Means and K-Medoids algorithms were then applied and assessed based on the Davies-Bouldin Index and Silhouette Score. The findings of this study demonstrate that K-Means outperformed K-Medoids in both the DBI and Silhouette Score by achieving lower DBI value and higher Silhouette Score. The results of both algorithms revealed that most destination countries belonged to the very low export value and weight cluster, accounting for around 88% of countries in K-Means and 60% in K-Medoids. The findings suggest that K-Means is more suitable for clustering Indonesia’s EIC export destination countries and can provide useful insight into export patterns that may support future trade analysis and decision-making.