Pemodelan Indeks Pembangunan Manusia Nusa Tenggara Barat Menggunakan Geographically Weighted Regression

Faiqotul Mala, Muhamad Fariq Hidayat


The Human Development Index (HDI) is an indicator for measuring the level of social and economic development of a country or region. The reality is that a local-based model of autonomy is often needed because of the spatial heterogeneity that can occur due to the territory's geographical, social, cultural, or other conditions. This research aims to find spatial effects affecting HDI in West Nusa Tenggara Province. A method that can be used to accommodate is Geographically Weighter Regression (GWR). GWR analysis is the development of multiple linear regression analysis that can address territorial diversity/spatial heterogeneity so as to produce different models and predictions of parameters for each observation region. The modeling was carried out using the Gaussian Kernel Adaptive spatial weigher with an optimal bandwidth value of 27,1227 and a minimum CV value of 5,2927. The GWR model modeling resulted in 10 models for each observation location and showed that life expectancy variables, school expectance, per capita income, and average school-age significantly influenced the IPM in the West Southeast Nusa Province in 2022 with an R2 of 99.92% and a minimum AIC value of -10,0281.


Geographically Weighted Regression (GWR); Human Development Index; spatial

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Badan Pusat Statistik, Statistik Indonesia 2023. Badan Pusat Statistik, 2023.

Badan Pusat Statistik, Indeks Pembangunan Manusia 2022. Badan Pusat Statistik, 2023.

Badan Pusat Statistik Nusa Tenggara Barat, Provinsi Nusa Tenggara Barat Dalam Angka 2023. Badan Pusat Statistik Nusa Tenggara Barat, 2023.

Nurhalizah and P. Sitompul, “Analysis of Ordinary Least Square and Geographically Weighted Regression on the Human Development Index of North Sumatra 2021,” Formosa Journal of Applied Sciences, vol. 1, no. 6, pp. 981–1000, Nov. 2022, doi: 10.55927/fjas.v1i6.1718.

M. Marizal and H. Atiqah, “Pemodelan Indeks Pembangunan Manusia di Indonesia dengan Geographically Weighted Regression (GWR),” Jurnal Sains Matematika dan Statistika, vol. 8, no. 2, p. 133, Sep. 2022, doi: 10.24014/jsms.v8i2.17886.

A. F. Adatunaung, D. Hatidja, and W. C. D. Weku, “Performa Kernel pada Model Geographically Weighted Regression untuk Menentukan Faktor-faktor Yang Mempengaruhi Indeks Pembangunan Manusia di Provinsi Sulawesi Selatan,” Jurnal Ilmiah Sains, vol. 23, no. 2, pp. 140–148, Oct. 2023, doi: 10.35799/jis.v23i2.48867.

M. Nadya, W. Rahayu, and V. M. Santi, “Analisis Geographically Weighted Regression (GWR) Pada Kasus Pneumonia Balita di Provinsi Jawa Barat,” Jurnal Statistika dan Aplikasinya, vol. 1, no. 1, pp. 23–32, 2017, doi:

F. E. Putri, Mukhsar, Baharuddin, B. Abapihi, Ruslan, and Agusrawati, “Pemodelan Indeks Pembangunan Manusia di Indonesia Dengan Pendekatan Geographically Weighted Regression,” in Prosiding Seminar Nasional Sains dan Terapan, 2022, pp. 34–49. Accessed: Dec. 10, 2023. [Online]. Available:

Z. F. Annabilah and H. T. Sutanto, “Pemodelan Indeks Pembangunan Manusia di Jawa Timur Menggunakan Geographically Weighted Regression (GWR),” Jurnal Ilmiah Matematika, vol. 7, no. 1, pp. 14–17, 2019, Accessed: Dec. 10, 2023. [Online]. Available:

A. Langiran, Kismiantini, and E. P. Setiawan, “Penerapan Model Regresi Spasial Dalam Menentukan Faktor-faktor yang Mempengaruhi Indeks pembangunan Manusia di Kabupaten/Kota Pulau Kalimantan,” Jurnal Statistika dan Sains Data, vol. 1, no. 1, pp. 1–9, 2023, Accessed: Dec. 10, 2023. [Online]. Available:

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,” Jurnal Matematika, Statistika dan Komputasi, vol. 19, no. 2, pp. 323–341, Jan. 2023, doi: 10.20956/j.v19i2.23775.

M. H. Kutner, Chris. Nachtsheim, and John. Neter, Applied linear regression models. McGraw-Hill/Irwin, 2004.

Sugiyono, Metode Penelitian Kuantitatif Kualitatif Dan R&D. Bandung : Alfabeta, 2021.

S. Andriani, “Uji Park Dan Uji Breusch Pagan Godfrey Dalam Pendeteksian Heteroskedastisitas Pada Analisis Regresi,” Jurnal Pendidikan Matematika, vol. 8, no. 1, pp. 63–72, 2017, Accessed: Nov. 14, 2023. [Online]. Available:

J. LeSage and R. Kelley Pace, Introduction to Spatial Econometrics. 2009.

A. Stewart. Fotheringham, C. Brunsdon, and M. Charlton, Geographically Weighted Regression. England: John Wiley & Sons Ltd, 2002.

R. E. Caraka and H. Yasin, Geographically Weighted Regression (GWR) : Sebuah Pendekatan Regresi Geografis, Edisi Pertama. Yogyakarta: Mobius, 2017. Accessed: Nov. 14, 2023. [Online]. Available:



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