Open AccessUtilization of K-Medoids Algorithm for Klustering of Oil Palm Sprouts
Author(s) -
Sri Nuraini,
Indra Gunawan,
Widodo Saputra
Publication year - 2022
Publication title -
journal of machine learning and artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2828-9102
pISSN - 2828-9099
DOI - 10.55123/jomlai.v1i1.160
Subject(s) - medoid , palm oil , cluster analysis , arecaceae , agricultural engineering , k means clustering , commodity , quality (philosophy) , cluster (spacecraft) , computer science , palm , data mining , mathematics , algorithm , database , business , engineering , agricultural science , artificial intelligence , environmental science , philosophy , physics , finance , epistemology , quantum mechanics , programming language
Palm oil is still a prima donna commodity in the plantation sector and as a major foreign exchange earner to date. Research and development of this commodity is very important to maintain Indonesia's position as the largest palm oil producing country in the world. The purpose of this study was to analyze what internal and external factors are the strengths, weaknesses, opportunities and threats for marketing oil palm sprouts in PPKS Marihat. To analyze what are the priority strategies to be implemented for the marketing of sprouts at PPKS Marihat. The research method used is the K-Medoids clustering algorithm by selecting the sprout data in order to determine the best quality of sprouts. Based on the results of research using the K-Medoids algorithm with manual calculations and testing, the same results were obtained, namely cluster 1 with very good sprouts category had 7 members, cluster 2 with good sprouts category had 12 members and cluster 3 with poor sprouts category had 7 members. . Testing data on Rapid Miner using the K-Medoids algorithm can display 3 classes with an accuracy percentage of 100%. So it can be concluded that the K-Medoids algorithm can be used for clustering oil palm sprouts at PPKS Marihat.