Open AccessA Oil Palm Harvest Grouping Using K-Medoids Algorithm
Author(s) -
Dessy Dwi Angraini,
M. Safii,
Fitri Anggraini
Publication year - 2021
Language(s) - English
DOI - 10.35335/ijobas.v10i2.56
Subject(s) - palm oil , computer science , process (computing) , agricultural engineering , similarity (geometry) , palm , medoid , data mining , cluster analysis , algorithm , database , mathematics , environmental science , artificial intelligence , engineering , agroforestry , physics , quantum mechanics , image (mathematics) , operating system
Oil palm (Elaies Guinnnsiss Jacq) is one of the important industrial crops producing cooking oil, industrial oil, and fuel. Indonesia is the largest palm oil producer in the world. The rest of the processing of oil palm fruit is called janjang. Janjang also serves to be used as compost. The data that is processed in this research is the harvest data at PT. Surya Intisariraya Mandau. Data mining is the process of looking for patterns or information in selected data using certain techniques or methods. The processing steps are grouped using the K-Medoids method and then the data will be processed using RapidMiner tools. Where this grouping is done to minimize the amount of similarity of data and appropriate so that it becomes more valid data. This study aims to simplify the grouping of harvest data based on high, medium and low clusters.