ANALISIS CLUSTER UNTUK MENGELOMPOKKAN KABUPATEN/KOTA DI SULAWESI SELATAN BERDASARKAN PRODUKTIVITAS TANAMAN PANGAN TAHUN 2023

Umarni, Alifya Zhafirah (2025) ANALISIS CLUSTER UNTUK MENGELOMPOKKAN KABUPATEN/KOTA DI SULAWESI SELATAN BERDASARKAN PRODUKTIVITAS TANAMAN PANGAN TAHUN 2023. Bachelor thesis, Institut Teknologi Kalimantan.

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Abstract

Produktivitas tanaman pangan merupakan salah satu indikator utama mengukur keberhasilan sektor pertanian, khususnya dalam mendukung ketahanan pangan di suatu wilayah. Provinsi Sulawesi Selatan dipilih sebagai fokus penelitian ini karena memiliki potensi besar di bidang pertanian serta memberikan kontibusi penting terhadap perekonomian daerah. Sebagai salah satu daerah penghasil utama tanman pangan seperti padi, jagung, kedelai, kacang tanah, ubi kayu dan ubi jalar, provinsi ini menunjukkan keberagaman hasil pertanian yang signifikan. Tujuan penelitian ini adalah mengelompokan kabupaten/kota di Provinsi Sulawesi Selatan berdasarkan produktivitas tanaman pangan pada tahun 2023 menggunakan metode analisis cluster yaitu K-Means dan Agglomerative Hierarchical Clustering. Hasil analisis menujukkan bahwa jumlah cluster optimal adalah 2 cluster,baik untuk metode K-Means maupun Agglomerative Hierarchical Clustering. Validasi hasil pengelompokan menggunakan Silhouette Score, Davies Bouldin Index, dan Dunn Index menujukkan bahwa metode K-means memberikan hasil pengelompokan yang lebih baik. Cluster ini mengelompokkan kabupaten/kota berdasarkan produktivitas tanaman pangan rendah, dan tinggi. Terdapat 22 kabupaten/kota masuk dalam cluster produktivitas tinggi,namun masih terdapat 2 kabupaten/kota yang masuk kedalam cluster produktivitas rendah. Hal ini dapat mendukung perencanaan kebijakan pertanian yang lebih tepat sasaran.

Item Type: Thesis (Bachelor)
Subjects: H Social Sciences > HA Statistics
Divisions: Jurusan Matematika dan Teknologi Informasi > Statistik
Depositing User: Alifya Zhafirah Umarni
Date Deposited: 04 Jul 2025 01:59
Last Modified: 04 Jul 2025 01:59
URI: http://repository.itk.ac.id/id/eprint/22722

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