Analysis of Factors Affecting the Human Development Index in Papua Province Using the Geographically Weighted Panel Regression Model
DOI:
https://doi.org/10.25217/numerical.v8i1.5029Keywords:
Fixed Effect Model, Geographically Weighted Panel Regression Model, Human Development IndexAbstract
The level of human quality development between regions or countries can be measured using the Human Development Index (HDI) value. The higher the value of the HDI, the better the quality of human development in the region. Some variables affect the value of the HDI. This study will test six independent variables using the Geographically Weighted Panel Regression (GWPR) method. This GWPR method combines panel data regression with the Geographically Weighted Regression (GWR) method. This GWPR method combines the dimensions of location and time to determine the effect of the independent variable on the dependent variable. Therefore, the purpose of this study is to see which variables have a significant effect on the value of the HDI in Papua Province. By using panel data regression, the best model that can be formed is the Fixed Effect Model (FEM). However, the FEM model that was formed did not meet the heteroscedasticity assumption test on the residuals, so further modeling was carried out using the GWPR model. GWPR modeling on this data uses a kernel weighting function, whereas previously, data transformation was carried out by the concept of the FEM model. The GWPR model with the best kernel weighting function is fixed exponential. In selecting the best model based on the coefficient of determination , the GWPR model is better than the FEM model. Regarding the significance of model parameters, nine groups of districts/cities based on independent variables significantly affect the HDI. In all districts/cities of Papua Province, the per capita expenditure variable significantly affects the HDI's value.
References
Deny Riani Maghfiroh, SST, “Indeks Pembangunan Manusia Provinsi Papua 2019,” Badan Pusat Statistik Provinsi Papua, pp. 4–15.
D. Hari Santoso, F. Anshari Arsyi, A. C. Clarissa, I. N. Setiawan, E. Kurniati, and S. Delyana, “Indeks Pembangunan Manusia 2023,” vol. 18, ©Badan Pusat Statistik, 2024, pp. 27–34.
A. N. Ambarwati, “Latent Class Cluster Analysis Untuk Pengelompokan Kabupaten/Kota Di Provinsi Jawa Tengah Berdasarkan Indikator Indeks Pembangunan Manusia 2017,” variance, vol. 1, no. 2, pp. 46–54, Jan. 2020, doi: 10.30598/variancevol1iss2page46-54.
Y. Taek, R. D. Bekti, and K. Suryowati, “Penerapan Model Geograpgically Weighted Regression (GWR) Menggunakan Fungsi Pembobot Adaptive Kernel Gaussian dan Adaptive Kernel Bisquare Padatingkat Pengangguran Terbuka Di Pulau Papua,” STATIKOM, vol. 8, no. 2, pp. 84–101, Jul. 2023, doi: 10.34151/statistika.v8i2.4459.
A. Maulana, R. Meilawati, and V. Widiastuti, “Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR),” IJAS, vol. 2, no. 1, p. 21, Jul. 2019, doi: 10.13057/ijas.v2i1.26170.
E. Amalia and L. K. Sari, “Analisis Spasial Untuk Mengidentifikasi Tingkat Pengangguran Terbuka Berdasarkan Kabupaten/Kota di Pulau Jawa Tahun 2017,” IJSA, vol. 3, no. 3, pp. 202–215, Oct. 2019, doi: 10.29244/ijsa.v3i3.240.
S. Martha, “Pemodelan Fixed Effect Geographically Weighted Panel Regression Untuk Indeks Pembangunan Manusia Di Kalimantan Barat”.
R. M. -, Reski Wahyu Yanti, and Syandriana Syarifuddin, “Analisis Tingkat Kepentingan terhadap Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia di Indonesia,” Jomta, pp. 45–49, Nov. 2022, doi: 10.31605/jomta.v4i2.2030.
K. D. Lorenza, S. C. Pratiwi, D. Puspita, and S. Rini, “Penerapan Spatial Autoregressive Model (SAR) Untuk Mengetahui Faktor-Faktor Yang Memengaruhi Indeks Pembangunan Manusia (IPM),” vol. 7, 2024.
R. H. Fajri, “Analisis Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia Di Provinsi Riau,” vol. 1, no. 1, 2021.
D. C. Wati and H. Utami, “Model Geographically Weighted Panel Regression (GWPR) Dengan Fungsi Kernel Fixed Gaussian Pada Indeks Pembangunan Manusia Di Jawa Timur,” JMT, vol. 2, no. 1, Jul. 2020, doi: 10.22146/jmt.49230.
N. M. S. Ananda, S. Suyitno, and M. Siringoringo, “Geographically Weighted Panel Regression Modelling of Human Development Index Data in East Kalimantan Province in 2017-2020,” J, vol. 19, no. 2, pp. 323–341, Jan. 2023, doi: 10.20956/j.v19i2.23775.
D. C. Wati, D. A. Azka, and H. Utami, “The Model of Per-Capita Expenditure Figures in Sumatera Selatan uses a Geographically Weighted Panel Regression: Model Angka Pengeluaran Per-Kapita di Sumatera Selatan menggunakan Geographically Weighted Panel Regression,” IJSA, vol. 5, no. 1, pp. 61–74, Mar. 2021, doi: 10.29244/ijsa.v5i1p61-74.
R. N. Fadila, “Pemodelan Indeks Pembangunan Manusia dengan Metode Regresi Panel di Provinsi Jawa Timur,” vol. 12, no. 1, 2023.
A. L. Tiopan Sitorus and E. Simamora, “Metode Geographically Weighted Panel Regression (GWPR) Untuk Menganalisis Faktor Yang Mempengaruhi Kemiskinan Di Provinsi Sumatera Utara,” RRJ, vol. 6, no. 1, pp. 155–167, Jan. 2024, doi: 10.38035/rrj.v6i1.808.
D. C. Wati, I. R. Lina, and A. S. Anggraeni, “Analisis Geographically Weighted Panel Regression di bidang Infrastruktur, Sosial, Kesehatan, Kependudukan, dan Pendidikan terhadap Produk Domestik Regional Bruto di Nusa Tenggara Timur”.
N. F. Gamayanti, J. Junaidi, F. Fadjryani, and N. Nur’eni, “Analysis of Spatial Effects on Factors Affecting Rice Production In Central Sulawesi Using Geographically Weighted Panel Regression,” BAREKENG: J. Math. & App., vol. 17, no. 1, pp. 0361–0370, Apr. 2023, doi: 10.30598/barekengvol17iss1pp0361-0370.
A. Pratama, S. Suyitno, and I. Purnamasari, “Pemodelan Persentase Penduduk Miskin Di Provinsi Kalimantan Timur Menggunakan Model Geographically Weighted Panel Regression,” msa, vol. 9, no. 2, Dec. 2021, doi: 10.24252/msa.v9i2.21021.
R. Raihani, S. Sifriyani, and S. Prangga, “Geographically Weighted Panel Regression Modelling of Dengue Hemorrhagic Fever Data Using Exponential Kernel Function,” JTAM, vol. 7, no. 4, p. 961, Oct. 2023, doi: 10.31764/jtam.v7i4.16235.
Sifriyani, I. N. Budiantara, M. F. F. Mardianto, and Asnita, “Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square,” MethodsX, vol. 12, p. 102605, Jun. 2024, doi: 10.1016/j.mex.2024.102605.
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