Pemetaan Kabupaten/Kota di Provinsi Papua berdasarkan Indikator Daerah Tertinggal

Rahardjo, Anindhieta Annisa Maharani (2024) Pemetaan Kabupaten/Kota di Provinsi Papua berdasarkan Indikator Daerah Tertinggal. Diploma thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Ketimpangan atau ketidaksetaraan pembangunan dapat dipicu oleh berbagai faktor, yakni alokasi sumber daya yang tidak merata, kondisi geografis, tingkat ekonomi, ketersediaan infrastruktur, dan akses layanan publik. Ketidaksetaraan pembangunan antar wilayah berkontribusi pada terbentuknya daerah tertinggal sehingga memerlukan perhatian khusus dalam upaya pengembangan dan peningkatan kesejahteraan. Dalam permasalahan ini, Kementerian Pembangunan Daerah Tertinggal Republik Indonesia (KPDT) menggunakan 6 kriteria penetapan daerah tertinggal. Ditetapkan dalam Peraturan Presiden Nomor 63 tahun 2020, Provinsi Papua merupakan provinsi dengan jumlah daerah tertinggal terbanyak di Indonesia sebanyak 22 kabupaten/kota dengan status daerah tertinggal. Ketertinggalan daerah di Provinsi Papua juga dapat dilihat dari rendahnya nilai rata-rata Indeks Desa Membangun (IDM) dengan status sangat tertinggal dan angka kemiskinan yang tinggi di Papua. Berbagai upaya telah dilakukan oleh pemerintah Provinsi Papua, tetapi kesetaraan pembangunan masih belum optimal dan tepat sasaran, oleh karena itu, diperlukan analisis pemetaan kabupaten/kota di Provinsi Papua berdasarkan indikator daerah tertinggal menggunakan metode analisis cluster hierarki. Metode terbaik yang diperoleh dari hasil penelitian ini adalah average linkage dengan 3 cluster optimum. Cluster 1 terdiri dari 3 kabupaten/kota dengan status daerah tertinggal, cluster 2 terdiri dari 18 kabupaten/kota dengan status daerah sangat tertinggal, dan cluster 3 terdiri dari 1 kabupaten dengan status daerah tertinggal sangat parah. Pemerintah dalam membuat kebijakan yang optimal, dapat memperhatikan setiap karakteristik cluster berdasarkan indikator daerah tertinggal, utamanya dengan memprioritaskan kabupaten pada cluster 3, 2, lalu 1 secara berurutan, sesuai tingkat keparahan ketertinggalan daerah.
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Development inequality or inequality can be triggered by various factors, namely unequal resource allocation, geographical conditions, economic levels, infrastructure availability, and access to public services. Development inequality between regions contributes to the formation of underdeveloped regions that require special attention in efforts to develop and improve welfare. In this issue, the Ministry of Development of Disadvantaged Regions of the Republic of Indonesia (KPDT) uses 6 criteria for determining disadvantaged regions. Stipulated in Presidential Regulation Number 63 of 2020, Papua Province is the province with the highest number of underdeveloped regions in Indonesia with 22 districts/cities with the status of underdeveloped regions. Regional underdevelopment in Papua Province can also be seen from the low average value of the Village Development Index (IDM) with very underdeveloped status and the high poverty rate in Papua. Various efforts have been made by the government of Papua Province, but the equality of development is still not optimal and right on target, therefore, it is necessary to analyze the mapping of districts/cities in Papua Province based on indicators of disadvantaged areas using the hierarchical cluster analysis method. The best method obtained from the results of this study is average linkage with 3 optimum clusters. Cluster 1 consists of 3 districts/municipalities with the status of disadvantaged regions, cluster 2 consists of 18 districts/municipalities with the status of severely disadvantaged regions, and cluster 3 consists of 1 district with the status of severely disadvantaged regions. The government, in making optimal policies, can pay attention to each cluster characteristic based on the indicators of underdeveloped regions, especially by prioritizing districts in clusters 3, 2, and then 1 in order, according to the severity of regional underdevelopment.

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Analisis Cluster, Indikator Daerah Tertinggal, Metode Hierarki, Provinsi Papua, Cluster Analysis, Disadvantaged Area Indicators, Hierarchical Method, Papua Province
Subjects:	H Social Sciences > HA Statistics > HA29 Theory and method of social science statistics H Social Sciences > HD Industries. Land use. Labor > HD108 Classification (Theory. Method. Relation to other subjects ) Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics)
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Anindhieta Annisa Maharani Rahardjo
Date Deposited:	31 Jul 2024 06:31
Last Modified:	30 Aug 2024 02:04
URI:	http://repository.its.ac.id/id/eprint/109741

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