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ANALISIS SPASIAL UNTUK MENGIDENTIFIKASI TINGKAT PENGANGGURAN TERBUKA BERDASARKAN KABUPATEN/KOTA DI PULAU JAWA TAHUN 2017
Author(s) -
Eka Rizki Amalia,
Liza Kurnia Sari
Publication year - 2019
Publication title -
indonesian journal of statistics and its applications
Language(s) - English
Resource type - Journals
ISSN - 2599-0802
DOI - 10.29244/ijsa.v3i3.240
Subject(s) - java , unemployment , dependency ratio , minimum wage , workforce , unemployment rate , wage , dependency (uml) , geography , demographic economics , economics , socioeconomics , labour economics , demography , population , economic growth , engineering , sociology , computer science , systems engineering , programming language
Unemployment is one of the economic problems faced by many countries. In Indonesia, the total workforce has reached 128.06 million and 7.04 million people are unemployed. The indicator to measure unemployment is open unemployment rate (TPT). Java Island becomes the island with the highest TPT, which is 4.04 million people, equivalent to 63.08 percent. The regions that have high TPT rates tend to be in the western region of Java, while the eastern region of Java is moderate. This is an initial allegation of regional influence so spatial analysis needs to be carried out. On the other hand, not many studies have included territorial effects. This study aims to spatially identify the influence of human development index (IPM), labor force particapation rate (TPAK), minimum wage and the dependency ratio on the number of TPT in Java in 2017 with the geographically weighted regression (GWR) method. The results of this study indicate that there are differences in the influence of IPM, TPAK, minimum wage and the dependency ratio on TPT in each area in Java. The most significant independent variables and have a positive relationship are minimum wage. This research also shows that GWR is suitable to be applied in modeling the number of TPT regencies /cities in Java Island in 2017. The results of this study can be used by the government in determining the right policy by looking at regional aspects in overcoming unemployment.

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