Pengelompokan Desa di Kabupaten Tuban Berdasarkan Kondisi Ketahanan Pangan Menggunakan Metode Integrasi K-Means dan Self Organizing Maps

Fuddiani, Kamila Zahra (2024) Pengelompokan Desa di Kabupaten Tuban Berdasarkan Kondisi Ketahanan Pangan Menggunakan Metode Integrasi K-Means dan Self Organizing Maps. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kabupaten Tuban sebagai salah satu daerah penyokong ketahanan pangan regional maupun nasional, mengharuskan pemerintah daerah perlu untuk memaksimalkan potensi tersebut, salah upayanya yakni memenuhi penyediaan data berisi informasi yang mampu menunjukkan dan memberikan penjelasan mengapa daerah tersebut rentan terhadap kerawanan pangan dengan membentuk analisis peta ketahanan dan kerentanan pangan tahunan menggunakan metode pengelompokan. Menggunakan data yang berdasar dari ketetapan Badan Ketahanan Pangan Kementerian Pertanian berupa pilar ketersediaan pangan, pilar keterjangkauan pangan, dan pilar pemanfaatan pangan, hasil analisis pengelompokan wilayah dengan faktor situasi ketahanan dan kerentanan pangan sampai dengan wilayah desa pada penelitian ini diharapkan mampu menjadi manfaat bagi para pengambil keputusan untuk secara cepat dan tepat dalam mengidentifikasi daerah yang lebih rentan, sehingga dapat memberikan dampak yang lebih baik terhadap penghidupan, ketahanan pangan dan gizi masyarakat di Kabupaten Tuban. Berdasarkan hasil perbandingan nilai Indeks Davies Bouldin dari tiga metode pengelompokan berbeda, Self Organizing Maps memiliki nilai indeks paling kecil yakni sebesar 0,9390712 jika dibandingkan dengan metode K-Means dan KSOM, sehingga menjadi metode terbaik yang mengelompokan daerah di Kabupaten Tuban. Pegelompokan menggunakan metode Self Organizing Maps mengelompokan 328 desa di Kabupaten Tuban menjadi 6 pelompok berbeda. Terdapat 6 desa dari 3 kecamatan berbeda yang dikelompokan dalam kelompok satu, terdapat 304 desa dari 20 kecamatan yang berbeda masuk ke kelompok dua, kelompok tiga hanya berisikan dua desa, kelompok empat juga hanya berisikan tiga, kelompok lima berisikan desa 12 dari 6 kecamatan berbeda, sedangkan kelompok 6 hanya berisikan Desa Jatisari Kecamatan Bancar.
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As one of the regions supporting both regional and national food security, Tuban Regency requires the local government to maximize this potential. One approach is to provide data containing information that can identify and explain why certain areas are vulnerable to food insecurity by developing an annual food security and vulnerability map analysis using clustering methods. Based on the guidelines from the Food Security Agency of the Ministry of Agriculture, which include the pillars of food availability, food accessibility, and food utilization, the results of the regional grouping analysis, considering the food security and vulnerability situation down to the village level, are expected to benefit decision-makers. This will enable them to quickly and accurately identify more vulnerable areas, thereby having a better impact on the livelihoods, food security, and nutrition of the people in Tuban Regency. Based on the comparison of the Davies-Bouldin Index values from three different clustering methods, Self-Organizing Maps had the lowest index value of 0.9390712 compared to the K-Means and KSOM methods, making it the best method for grouping areas in Tuban Regency. The clustering using the Self-Organizing Maps method categorized 328 villages in Tuban Regency into six different groups. There are six villages from three different sub-districts grouped in the first group, 304 villages from 20 different sub-districts in the second group, only two villages in the third group, three villages in the fourth group, twelve villages from six different sub-districts in the fifth group, and only Jatisari Village in Bancar Sub-district in the sixth group.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Integrasi, K-Means, Ketahanan Pangan, Self Organizing Maps, Integration, K-Means, Food Security, Self Organizing Maps.
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) > G70.212 ArcGIS. Geographic information systems. Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA336 Artificial Intelligence
Divisions:	Faculty of Mathematics, Computation, and Data Science > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	; Kamila Zahra Fuddiani
Date Deposited:	12 Aug 2024 07:31
Last Modified:	12 Aug 2024 07:31
URI:	http://repository.its.ac.id/id/eprint/115067

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