Implementasi K-Medoids Clustering Dalam Pengelompokkan Harga 8 Jenis Minyak Goreng
DOI:
https://doi.org/10.31598/sintechjournal.v6i3.1419Keywords:
K-Medoids, Clustering, Edible OilsAbstract
In everyday life, cooking oil has become a necessity with international sales prices varying depending on the quality and type. The types of cooking oil sold in the international market are very diverse, including coconut, olive, palm kernel oil, palm oil, peanuts, rapeseed, soybeans and sunflower. Therefore, it is necessary to group data on selling prices of cooking oil on the international market to get the best grouping of cooking oil. The data used in this research is historical price data of 8 edible oils kaggle August 1992 to July 2022. Data grouping in this research uses the k-medoids algorithm. The k-medoids algorithm produces a more balanced group, better performance and accuracy than other algorithms. The aim of this research is that the k-medoids algorithm is able to group cooking oil price data into 4 group models, namely group models 2, 3, 4 and 5 and obtain the best group model based on the dbi value. The research results showed that the cooking oil price data was successfully grouped into group 2, 3, 4 and 5 models with the best group based on the lowest dbi performance value being the group 2 model with a dbi value of 0,000 and olive oil being the cooking oil with the highest price in the world while 7 types other cooking oils have more or less the same price (in the same price group).
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