Browsing by Author "Atmoko, Gesit Widi Atmoko"

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    PENERAPAN ALGORITMA K-MEANS CLUSTERING DALAM PENGELOMPOKAN POTENSI PRODUKSI PERTANIAN DAN PERKEBUNAN DI PROVINSI RIAU
    (Elfitra, 2023-05) Atmoko, Gesit Widi Atmoko; Alfirman, Alfirman
    The agricultural sector is very important for Riau province, as the province is still heavily dependent on the agricultural sector for economic growth. Riau province has the potential for plantation development in order to accelerate access to encourage the development of economic potential and create economic growth and equity in Riau province. The development of plantation and agricultural commodities in Riau province is still not optimal. One of the problems and obstacles faced in its development is the low productivity of plantation and agricultural crops. Therefore, the Riau provincial government needs to know the description of plantations and agriculture in the form of characteristics found in each district or city in Riau province. This research aims to cluster the potential of agricultural and plantation production in Riau province using the k-means method. The k-means method is a clustering technique that is useful for dividing data into several groups where data is grouped based on the same characteristics. In the process of collecting data on agricultural and plantation harvest areas, 528 data were generated. After going through the cleaning step the resulting data becomes 408 data. The results of calculations with the k-means method carried out 3 times the experiment obtained the formation of 3 clustering stops in iteration 7, the formation of 4 clustering stops in iteration 11, and the formation of 5 clustering stops in iteration 12. The highest silhouette coefficient value is in the formation of 3 clusters of 0.804 which is classified as a strong structure. Cluster 1 consists of 21 members which is a cluster with high production, cluster 2 consists of 35 members which is a cluster with medium production, and cluster 3 consists of 352 members which is a cluster with low production.