XportID: Website for Clustering Indonesian Export Commodities by Destination Continent using Gaussian Mixture Model

Angela Lisanthoni; Trimono Trimono; Dwi Arman Prasetya

doi:10.31764/jtam.v9i1.27500

XportID: Website for Clustering Indonesian Export Commodities by Destination Continent using Gaussian Mixture Model

Angela Lisanthoni, Trimono Trimono, Dwi Arman Prasetya

Abstract

Exports play a crucial role in driving economic growth and increasing foreign exchange reserves. However, Indonesia's export performance has not yet reached its optimal potential, as evidenced by an 11% decline in export value in 2023. The decrease is partly attributed to the limited range of export destination markets. Therefore, this study aims to analyze export trade patterns to identify the most ideal and potential market locations. The research will employ a quantitative approach, using secondary data from the Central Bureau of Statistics and the 2022 BACI dataset, focusing on the top 5 HS2 commodity types by highest export quantity. Clustering analysis is applied to group markets based on similar characteristics, identifying countries with high, medium, and low export potential for Indonesia’s export strategy. The research develops a website-based clustering system called XportID, utilizing a Gaussian Mixture Model (GMM) with the Expectation-Maximization (EM) algorithm to determine optimal cluster parameters. GMM is preferred for its flexibility and probabilistic system, providing more accurate results compared to distance-based methods. There will be 3, 4, and 5 clusters formed and then the best cluster will be selected by comparing the silhouette score obtained. Results show that the Asian continent has 5 clusters with the best value of 0.7035, the American continent has 3 clusters with the best value of 0.8165, the African continent has 3 clusters with the best value of 0.8534, the Australian continent has 3 clusters with the best value of 0.8540, and the European continent has 4 clusters with the best value of 0.8654. Overall results, the clustering system is categorized as strong structure with average value of 0.8185. Countries with high export potential include Malaysia, Philippines, South Korea, Brazil, Mexico, New Zealand, and Spain. Specifically in Africa, commodities related to HS2-15 show potential for growth.

Keywords

Gaussian Mixture Model; Silhouette Score; Export; Clustering.

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DOI: https://doi.org/10.31764/jtam.v9i1.27500