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All Journal Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Mutaqin, Ahdan Darul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Spatial Clustering Using Generalized LASSO on the Gender and Human Development Index in Papua Island in 2022 Mutaqin, Ahdan Darul; Rahardiantoro, Septian; Masjkur, Mohammad
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9268

Abstract

Equitable development from a gender perspective needs attention. Based on data from the World Economic Forum (WEF), gender equality in Indonesia has increased. Even so, the island of Papua is still very low on gender equality. It can be seen from the Gender Development Index (IPG) from the Central Bureau of Statistics (BPS), there is a considerable gap between the Papua Island IPG and the National. IPG is a comparison between the Human Development Index (IPM) for Men and Women. Based on these conditions, this study aims to classify GPI, Male IPM, and Female IPM by region using the spatial clustering method in 2022. One of the analytical methods that can overcome these conditions is Generalized LASSO. Generalized LASSO can be used on data that only has a response variable (y) for clustering. Generalized LASSO clustering uses a penalty matrix D. The formation of the D matrix is formed by giving values -1 and 1 for areas that intersect or are adjacent and a value of 0 for other areas. The best clustering for IPG uses KNN with K = 3 and the number of clusters formed is 2 clusters. The best clustering for male HDI uses KNN with K = 2 and the number of clusters formed is 8. The best clustering for female HDI uses KNN with K = 2 and the number of clusters formed is 10 clusters.
Co-Authors Mohammad Masjkur Rahardiantoro, Septian