Clustering Productive Palm Land using the K- Means Clustering Algorithm

Abstract

Indonesia is a country with a tropical climate that has many oil palm plantations. CV. Alkema Deo is one of the companies that manage oil palm plantations in Sampit City, East Kotawaringin Regency, Central Kalimantan. CV. Alkema Deo was founded in 2016 and has two plantation locations located on Jl. General Sudirman Km. 18, East Kotawaringin and Seibabi Village, Telawang District, East Kotawaringin. In this study, a qualitative approach was applied using a descriptive research pattern. In qualitative research, data is obtained from sources using various data collection techniques. Research using qualitative methods emphasizes the analysis of thought processes related to the dynamics of the relationship between observed phenomena, and always uses scientific logic. Based on the results of research for authors on a CV. Alkema Deo, the use of Excel in companies is quite good at processing data, but on a CV. Alkema Deo does not yet have land groupings based on productivity levels, so it is difficult to see the level achieved in 6 months based on the set target, and daily production control in terms of area and block area. Data obtained from CV. Alkema Deo is grouped based on area, block, and productivity. Application of data mining for grouping productive oil palm land on a CV. Alkema Deo with 4 variables, namely: land area, length, average production yield, percentage of achievement using the K-Means Algorithm to produce three clusters, namely 8 blocks or 50% including the high productive group (C2), 1 block or 6% blocks including the medium productive plantation group (C1), and 7 blocks or 44% including the small productive plantation group (C0).