Implementation of Data Mining Algorithm for Clustering of Palm Oil Harvested Data

Widya Juli Mawaddah (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
Indra Gunawan (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)
Ika Purnama Sari (STIKOM Tunas Bangsa, Pematangsiantar, Indonesia)

JOMLAI: Journal of Machine Learning and Artificial Intelligence
Vol. 1 No. 1 (2022): March

Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. In this study, we will discuss oil palm yields at PPKS Marihat, one of the Oil Palm Research Center branches located in Simalungun Regency, Medan, North Sumatra. Know how it grows. The Clustering algorithm is used in K-Means. Using this method, the data will be grouped into 3 (three) Clusters, where the application of the K-Means Clustering process uses the Rapid Miner tools. The data used is data on oil palm harvests at PPKS Marihat in 2020, consisting of 100 data items. The results obtained are crop yields with an excellent value of 66 items, harvest data with a good deal of 32 items, and harvest data with a reasonably good value of 2 items, based on net total and gross amount for each region. Based on this, it can be concluded that the K-Means Algorithm can be used to Cluster oil palm yields at PPKS Marihat