IMPLEMENTASI ALGORITMA K-MEANS DALAM PENGELOMPOKAN DOSIS PEMUPUKAN KELAPA SAWIT BERDASARKAN KONDISI TANAMAN - Fajar Hardiansyah; Helmi Fauzi Siregar (Smart Education, 2026)
Oil palm is one of the plantation commodities that plays an important role in improving Indonesia's economy. PT Socfindo Kebun Aek Loba has a large amount of oil palm plant condition data; however, the data has not been optimally utilized to determine fertilizer dosage requirements. This study aims to classify oil palm fertilizer dosage requirements based on plant conditions using the K-Means algorithm and to design an application that supports the clustering process. The variables used in this study include plant age, tree height, number of fruit bunches, and number of fronds. The dataset consisted of 200 oil palm plant records. The clustering process was carried out by forming three clusters, namely low, medium, and high fertilizer dosage groups, using the K-Means method with Euclidean Distance calculations. The results showed that out of 200 plant data records processed, 92 data (46%) were classified into the low fertilizer dosage cluster, 54 data (27%) into the medium fertilizer dosage cluster, and 54 data (27%) into the high fertilizer dosage cluster. In addition, this study successfully developed an application using PHP and MySQL that is capable of managing data, performing clustering automatically, and presenting clustering results in the form of tables and charts. The developed application can assist PT Socfindo Kebun Aek Loba in obtaining information regarding fertilizer dosage requirements more quickly, effectively, and systematically, thereby supporting decision-making related to oil palm fertilization.