Rice Cluster Center Weighted K-Means Clustering analysis using R language, in Malang regency, Indonesia.
Rice milling into rice, (Rice Milling Unit, RMU) is needed to process the harvested grain into rice. In determining the location, the consideration of road access to consumers is still used and it is often far from the rice fields, the source of the grain material to be milled. This paper aims to analyze the ideal location with the consideration of being in a rice field cluster and the area of the rice field, so that it can accommodate crop yields around the cluster, namely the location of the spatial coordinates and the area (hectare). Primary data was obtained by the author from the Department of Agriculture of Malang Regency and the data was processed as input for analysis. Analysis of determining the location of the center of the rice field cluster using the Weighted K-Means Clustering method by comparing several alternative cluster points (K) and the area of rice fields, testing the variation of K is, K = 5, 6, 7 and 8. Optimal test results at K = 7. Calculations using the R language. The test results obtained that the cluster center suggested 7 clusters to accommodate the rice harvest in 7 regions, namely in the west, north (1), middle (2), south (3), east (1) and west (1 ) . Evaluation of the location of the existing RMU, there are 5 locations, so that for the next planning, you can add 2 more clusters to build the RMU and accommodate a more even rice harvest.
inspiring from Parawendy Indarjo:
- https://github.com/pararawendy/weighted_kmeans_R
- https://towardsdatascience.com/using-weighted-k-means-clustering-to-determine-distribution-centres-locations-2567646fc31d
- high appreciation and thanks to Parawendy Indarjo, Data scientist at Bukalapak.