Penerapan Algoritma K-Means Untuk Klasterisasi Produktivitas Tanaman Jahe
DOI:
https://doi.org/10.29408/jit.v8i1.28195Keywords:
BPS, Ginger Plants, K-Means Clustering, Sumenep DistrictAbstract
Sumenep Regency is one of the regions that produces ginger plants in East Java province, and has become one of the mainstays of the community's economy. However, there are obstacles faced, namely the size of the planting area, erratic weather changes and the lack of modern technology in ginger cultivation which are the main causes in affecting crop yields and product quality. This study aims to implement data mining using a web-based K-Means clustering algorithm to group ginger-producing areas in Sumenep Regency. The data used was 108 data covering the area of harvest areas and the amount of production per sub-district from 2020 to 2023 taken from the official website of the Central Statistics Agency (BPS) of Sumenep Regency. The K-Means algorithm was chosen because of its advantages in efficiently managing large amounts of numerical data, helping to recognize distribution patterns such as harvest area and production amount to produce the right cluster, its relatively simple implementation and easy-to-interpret clustering results. The results of the study show that the Sumenep Regency area can be grouped into 2 clusters based on ginger plant productivity, namely cluster 1 with 104 sub-districts and cluster 2 with 4 sub-districts. This research is expected to help farmers and the government in identifying areas with high and low productivity, optimizing land and resource use, and increasing productivity and income
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