Pemanfaatan Algoritma K-Medoids Clustering dalam Menentukan Pendapatan Bersih Komoditas Pertanian

Faisal Muhammad (Universitas Raharja)
Wiranti Sri Utami (Universitas Cendekia Abditama)
Muhammad Subali (Universitas Cendekia Abditama)
Janu Ilham Saputo (Universitas Raharja)
Haryanto (Universitas Raharja)
Martinus Gawi Tiga (Universitas Raharja)

Bulletin of Information Technology (BIT)
Vol 7 No 2 (2026)

Agricultural products are one of the sectors that have a major role in the Indonesian economy. Currently, Indonesia is the largest producer in the world that produces Palm Oil, Cloves, Cinnamon, Nutmeg, and many others. Abundant agricultural products can be applied to research using Data Mining techniques. Data Mining is a technique that applies statistical analysis and artificial intelligence in extracting useful information from a database. In this study the author will use the K-Medoids method, K-Medoids is one of the Data Mining techniques. Analysis of K-Medoids results uses the silhouette coefficient which is used to measure the distance between clusters. The objective value using K-Medoids cluster analysis on the dataset used is 5.742047 and 5.093438. After conducting cluster analysis with the silhouette coefficient, the best results obtained are 2 clusters from 12 data and 12 attributes.