IMPLEMENTASI DATA MINING PENGELOMPOKAN JUMLAH DATA PRODUKTIVITAS UBINAN TANAMAN PANGAN BERDASARKAN JENIS UBINAN DENGAN METODE CLUSTERING DIKAB LANGKAT (STUDI KASUS : BADAN PUSAT STATISTIK LANGKAT) - Cici Armayani; Achmad Fauzi; Hermansyah Sembiring (Jurnal Informatika Kaputama (JIK), 2021)
Based on data from the ubinan survey at the Central Statistics Agency (BPS) Langkat, there are several sample areas and there are results of farmer productivity in growing food crops. For this reason, it is necessary to group the number of data from the ubinan survey based on the type of food crop ubinan to classify the amount of harvest by farmers in planting food crops in each district. Data Mining is a process of finding information by identifying patterns in data sets. The process of finding information can be done by grouping the data using the Clustring method with the K-Means algorithm. By using K-Means the aim is to facilitate grouping the number of data on the productivity of food crops according to the type of ubinan and the productivity of food plants. And the data used in this research is the ubinan survey data for 3 years, namely 2017 to 2019. From the results of the analysis of the program that has been tested using matlab and the variables have been determined, it can be seen that, for cluster 1, the results of the type of food crop tubing and kecamatan, the amount of data is 423, for cluster 2 the number of data is 387 data types, the number of production and the district is 387 data, for cluster 3 the number of data is 432 data.