<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022)"^^ . "This study aims to compare the Geographically Weighted Panel Regression\r\n(GWPR) method and the Geographically Temporal Weighted Regression (GTWR)\r\nmethod in describing the spatial-temporal pattern of poverty factors on the amount\r\nof Provincial Minimum Wage (UMP) in Indonesia. The GWPR method is a\r\ncombination of Geographically Weighted Regression (GWR) with panel data\r\nregression that will analyze the spatial variation of each region. In contrast, the\r\nGTWR method handles data non-stationarity both from spatial and temporal\r\naspects simultaneously. In this study, the GWPR method will select the best\r\nregression model by involving two tests, namely the Chow Test and the Hausman\r\nTest by producing a Fixed Effect Model (FEM). Meanwhile, the GTWR method\r\nuses multiple linear regression to determine the independent variables that have a\r\nsignificant effect on the dependent variable. In this study, the GWPR and GTWR\r\nmethods select the optimum bandwidth using Cross-Validation (CV) with the\r\nsmallest value that can be used. To compare the GWPR and GTWR methods that\r\nare good to use, this study will measure the best model with the largest R2 value,\r\nthe smallest RMSE, and the smallest AIC. The results of the study show that both\r\nmethods between GWPR and GTWR will produce significant variables in the\r\ninfluence of poverty on minimum wages in various provinces by creating a\r\nvisualization of the distribution map pattern with significant independent variables\r\nfor each province in Indonesia.\r\n\r\nKeywords: GWR, GWPR, GTWR, Poverty, Spatial temporal.\r\n\r\n\r\n\r\nPenelitian ini bertujuan untuk membandingkan metode Geographically Weighted\r\nPanel Regression (GWPR) dan metode Geographically Temporal Weighted\r\nRegression (GTWR) dalam menggambarkan pola spasial-temporal pada faktor\r\nkemiskinan terhadap besaran Upah Minimum Provinsi (UMP) di Indonesia.\r\nMetode GWPR merupakan gabungan Geographically Weighted Regression (GWR)\r\ndengan regresi data panel yang akan menganalisis variasi spasial setiap daerah.\r\nSebaliknya, metode GTWR untuk menangani ketidakstasioneran data baik dari\r\naspek spasial maupun temporal secara bersamaan. Pada penelitian ini, metode\r\nGWPR akan memilih model regresi terbaik dengan melibatkan dua uji yaitu, Uji\r\nChow dan Uji Hausman dengan menghasilkan Fixed Effect Model (FEM).\r\nSedangkan, metode GTWR menggunakan regresi linear berganda untuk\r\nmengetahui variabel independen berpengaruh signifikan terhadap variabel\r\ndependen. Dalam penelitian ini, metode GWPR dan GTWR memilih bandwidth\r\noptimum dengan menggunakan Cross-Validation (CV) dengan nilai terkecil yang\r\ndapat digunakan. Untuk membandingkan metode GWPR dan GTWR yang baik\r\ndigunakan, dalam penelitian ini akan mengukur model terbaik dengan nilai R2\r\nterbesar, RMSE terkecil, dan AIC terkecil. Hasil penelitian menunjukkan kedua\r\nmetode antara GWPR dan GTWR akan menghasilkan variabel-variabel yang\r\nsignifikan dalam pengaruh kemiskinan terhadap upah minimum di berbagai\r\nprovinsi dengan membuat visualisasi pola peta persebaran dengan variabel\r\nindependen yang signifikan untuk setiap provinsi di Indonesia.\r\n\r\nKata Kunci: GWR, GWPR, GTWR, Kemiskinan, Spasial temporal."^^ . "2024-08-09" . . . . . "FAKULTAS MATEMATIKA DAN ILMU PENGETAHUAN ALAM"^^ . . . . . . . " RENTA APRIANI"^^ . "MONICA"^^ . " RENTA APRIANI MONICA"^^ . . . . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (File PDF)"^^ . . . "ABSTRAK - Monica Apriani.pdf"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (File PDF)"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (File PDF)"^^ . . . "SKRIPSI TANPA BAB PEMBAHASAN - Monica Apriani.pdf"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "preview.jpg"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "medium.jpg"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "small.jpg"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "preview.jpg"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "medium.jpg"^^ . . . "COMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL\r\nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY\r\nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA\r\n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE\r\nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN\r\n\r\n2020 – 2022) (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #82401 \n\nCOMPARISON OF GEOGRAPHICALLY WEIGHTED PANEL \nREGRESSION (GWPR) AND GEOGRAPHICALLY TEMPORALLY \nWEIGHTED REGRESSION (GTWR) METHODS FOR SPATIAL DATA \n(CASE STUDY OF THE INFLUENCE OF POVERTY FACTORS ON THE \nAMOUNT OF MINIMUM WAGES IN PROVINCES IN INDONESIA IN \n \n2020 – 2022)\n\n" . "text/html" . . . "500 ilmu pengetahuan alam dan matematika" . . . "510 Matematika" . .