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dc.contributor.advisorSuyanto
dc.contributor.authorSiagian, Dini Lestari Efendi
dc.date.accessioned2024-04-24T01:41:28Z
dc.date.available2024-04-24T01:41:28Z
dc.date.issued2023
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/93133
dc.description.abstractThe increase in the open jobless rate in North Sumatra Province reached 6.30 percent. Due to the distinct characteristics of each region in North Sumatra Province, the open unemployment rate problem illustrates the geographical influence of location and the potential for spatial heterogeneity. Geographically Weighted Regression is a method that can help in dealing with spatial problems. By modeling influencing variables, This study intends to learn more about North Sumatra Province's open unemployment rate in 2021. Local partial GWR testing findings demonstrate that the variable of residents who work during the week has significant spatial heterogeneity or is local in nature. However, the factors of the human development index, labor force participation rate, GRDP rate at constant prices and participation rate are purely global in nature. One model that has spatial heterogeneity is Y Kab.Nias = 7,774 − 0,155X1 + 0,245X2 − 1,667X5 − 0,133X7 − 3,26e−5x8 . a fixed gaussian weighting function was used in this investigation for weighting. According to the criteria for the model's goodness, the findings demonstrate that the GWR model outperforms multiple linear regression analysis (OLS), as evidenced by the values for R2, AIC, MSE, and MAPE.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectGeographically weighted regressionen_US
dc.subjectregresi linier bergandaen_US
dc.subjecttingkat pengangguran terbukaen_US
dc.subjectSDGsen_US
dc.titlePemodelan Tingkat Pengangguran Terbuka di Kabupaten /Kota Provinsi Sumatera Utara Menggunakan Metode Geographichally Weigthed Regressionen_US
dc.title.alternativeModeling Open Unemployment Rates in Regencies/Cities of Nort Sumatra Province Using The Geographically Weighted Regression Methoden_US
dc.typeThesisen_US
dc.identifier.nimNIM190803006
dc.identifier.nidnNIDN0013085903
dc.identifier.kodeprodiKODEPRODI44201#Matematika
dc.description.pages81 Pagesen_US
dc.description.typeSkripsi Sarjanaen_US


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