Poverty is still become a main problem for Indonesia, where recently, the view point of poverty is not just from income or consumption, but it's defined multidimensionally. The understanding of the structure of multidimensional poverty is essential to government to develop policies for poverty reduction. This paper aims to describe the structure of poverty in East Java by using variables forming the dimensions of poverty and to investigate any clustering patterns in the region of East Java with considering the poverty variables using biclustering method. Biclustering is an unsupervised technique in data mining where we are grouping scalars from the two-dimensional matrix. Using bicluster analysis, we found two bicluster where each bicluster has different characteristics.
AbstrakKemiskinan masih menjadi permasalahan utama di Indonesia, dimana saat ini kemiskinan tidak lagi dipandang dari sisi pendapatan atau konsumsi, tapi sekarang kemiskinan didefinisikan secara multidimensional. Pemahaman akan struktur kemiskinan multidimensi sangat penting bagi pemerintah untuk mengembangkan berbagai kebijakan untuk mengentaskan kemiskinan. Tulisan ini bertujuan untuk menjelaskan struktur kemiskinan di provinsi Jawa Timur dengan menggunakan variabel-variabel pembentuk dimensi kemiskinan serta untuk menyelidiki setiap pola pengelompokkan yang terbentuk dengan menggunakan metode bicluster. Analisis bicluster adalah suatu teknik unsupervised dalam data mining yang mengelompokkan data dalam suatu matriks dua dimensi. Hasil penelitian mengungkapkan terdapat dua bicluster dengan setiap bicluster memiliki karakteristik yang berbeda. Kata kunci: kemiskinan, kemiskinan multidimensi, biclustering.
This paper presents the dataset about the social vulnerability in Indonesia. This dataset contains several dimensions which rely on previous studies. The data was compiled mainly from the 2017 National Socioeconomic Survey (SUSENAS) done by BPS-Statistics Indonesia. We utilize the weight to obtain the estimation based on multistage sampling. We also received additional information on population, the number, and population growth from the BPS-Statistics Indonesia's 2017 Population projection. Furthermore, we provide the distance matrix as the supplementary information and the number of populations to do the Fuzzy Geographically Weighted Clustering (FGWC). This data can be utilized to do further analysis of social vulnerability to promote disaster management. The data can be accessed further at
https://raw.githubusercontent.com/bmlmcmc/naspaclust/main/data/sovi_data.csv
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