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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Harjono, Rhaka Pradena
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Penerapan Metode K-Means Clustering Untuk Analisis Potensi Lahan Pangan Pada Provinsi Kalimantan Selatan Harjono, Rhaka Pradena; Pakereng, Magdalena A. Ineke
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.596

Abstract

National food needs are increasing along with the current population of 237 million people with growth that continues to increase every year, so it needs to be balanced with the provision of sufficient agricultural land resources. The application of the K-Means in the grouping of potential agricultural land in South Kalimantan Province, with the aim of obtaining groups of potential land data. By using the K-means clustering algorithm, the data is divided into 3 clusters, namely cluster 0 with a low potential of 6 districts, cluster 1 with a medium potential of 5 districts, and cluster 2 with a high potential of 2 districts.
Penerapan Metode K-Means Clustering Untuk Analisis Potensi Lahan Pangan Pada Provinsi Kalimantan Selatan Harjono, Rhaka Pradena; Pakereng, Magdalena A. Ineke
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.596

Abstract

National food needs are increasing along with the current population of 237 million people with growth that continues to increase every year, so it needs to be balanced with the provision of sufficient agricultural land resources. The application of the K-Means in the grouping of potential agricultural land in South Kalimantan Province, with the aim of obtaining groups of potential land data. By using the K-means clustering algorithm, the data is divided into 3 clusters, namely cluster 0 with a low potential of 6 districts, cluster 1 with a medium potential of 5 districts, and cluster 2 with a high potential of 2 districts.
Co-Authors Magdalena Ariance Ineke Pakereng