Implementation of K-Medoids Clustering Algorithm for Grouping Palm Oil Exports by Destination Country

• Published in: 2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS pp. 129 - 134
• Main Authors: Haryadi, Deny, Adidrana, Demi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.10.2021
• Subjects: Clustering; Clustering algorithms; Clustering methods; Destination country; Exports; Finance; K-Medoids algorithm; Manuals; Multimedia systems; Oils; Palm oil; Pandemics
• DOI: 10.1109/ICIMCIS53775.2021.9699176

Palm oil is one of the plantation products that becomes the export of Indonesia's leading commodity. The volume of palm oil exports tends to increase every year. This is influenced by the large demand for palm oil in palm oil importing countries in the world. The high demand for palm oil is an opportunity that must be developed so that Indonesia can compete with its competitors in the current pandemic. Indonesia as the world's largest palm oil producer must be able to group the countries that are priority as the largest importers of palm oil. For this reason, it is necessary to group the countries that are a priority as the largest importer of palm oil. The purpose of this study is to group palm oil exports based on the destination country using the K-Medoids Clustering algorithm. Based on the results of tests that have been conducted in this study using the K-Medoids Clustering algorithm, Cluster 1 is a category of countries importing low palm oil or Low, namely 7 (Netherlands, USA, Spain, Egypt, Bangladesh, Italy, Singapore) of 10 categories of countries tested, then cluster 2 is a category of countries importing medium palm oil or Medium which is 1 (Pakistan) of 10 categories of countries tested, and lastly cluster 3 is a category of high palm oil importing countries or High which is 2 (India and China) from 10 categories of countries tested.