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Abstract

The Human Development Index (HDI) is a key indicator for measuring the welfare and prosperity of a region, including West Java Province. This study aims to analyze the factors influencing HDI and predict its future trends. The analysis was conducted using a multiple linear regression method implemented with the Python programming language, with independent variables including Life Expectancy, Expected Years of Schooling, Mean Years of Schooling, and Adjusted Per Capita Expenditure. The results show that Expected Years of Schooling (X3) and Adjusted Per Capita Expenditure (X4) are the most significant factors influencing HDI in West Java, particularly due to the declining trends in these variables. Based on the model, the predicted HDI values for 2024, 2025, and 2026 are 73.19, 72.59, and 71.62, respectively, which fall under the medium HDI category. These findings provide valuable insights for strategic planning to improve HDI in West Java, particularly through interventions targeting the significant variables.

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Article Details

How to Cite
Riza, N., Maresti, F. A., Azzahra, S. S., & Ningsih, S. P. P. (2024). Implementasi Regresi Linear Berganda Prediksi Faktor-faktor Indeks Pembangunan Manusia di Provinsi Jawa Barat. MASALIQ, 5(1), 69-86. https://doi.org/10.58578/masaliq.v5i1.4335

References

Alwi, W., Irwan, M., & Musfirah, M. (2021). Penerapan Regresi Nonparametrik Spline Dalam Memodelkan Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia (Ipm) Di Indonesia Tahun 2018. Jurnal MSA ( Matematika Dan Statistika Serta Aplikasinya ), 9(2). https://doi.org/10.24252/msa.v9i2.23055

Anastashya, M., Putri, A. E., Pinaringan, G. A., & Hendrawati, T. (2023). Faktor-faktor yang Mempengaruhi Indeks Pembangunan Manusia di Provinsi-Provinsi Indonesia. Seminar Nasional Statistika Aktuaria II, 2(2), 520–530. http://prosiding.snsa.statistics.unpad.ac.id

Aprila, D., Andriani, W., & Ananto, R. P. (2023). Financial Management of Nagari Owned Enterprises (BUMNAG) and Its Impact on Community Welfare. Jurnal Akuntansi Bisnis, 16(2), 210–225. https://doi.org/10.30813/jab.v16 i2.4461

Badan Pusat Statistik, “Indeks Pembangunan Manusia(IPM) Jawa Barat 2019,” Badan Pusat Statistik, 2019. Diakses pada 05 Maret 2024, dari https://jabar.bps.go.id/pressrelease/2020/02/17/771/indeks-pembangunan-manusia--ipm--provinsi-jawa-barat-tahun-2019-mencapai-72-03-.html.

Badan Pusat Statistik, “Indeks Pembangunan Manusia(IPM) Jawa Barat 2023,” Badan Pusat Statistik, 2023. Diakses pada 05 Maret 2024, dari https://jabar.bps.go.id

Kadri, I. A., Susilawati, M., & Sari, K. (2020). Faktor–Faktor Yang Berpengaruh Signifikan Terhadap Indeks Pembangunan Manusia Di Provinsi Papua. E-Jurnal Matematika, 9(1), 31. https://doi.org/10.24843/mtk.2020.v09.i01.p275

Khaerunisa, S., Nur Padilah, T., & Haerul Jaman, J. (2024). Implementasi Data Mining Menggunakan Metode Regresi Data Panel Untuk Memprediksi Capaian Indeks Pembangunan Manusia. JATI (Jurnal Mahasiswa Teknik Informatika), 7(5), 3399–3406. https://doi.org/10.36040/jati.v7i5.7260

Khotimah, A. K., Rahman, A. A., Alam, M. Z., Nur, Y. H., & Aufi, T. R. (2024). Analisis Regresi Linier Berganda Dalam Estimasi Indeks Pembangunan Manusia di Indonesia Multiple Linear Regression Analysis In Estimating The Human Development Index In Indonesia. Jurnal Eksponensial, 15(November), 90–99. https://doi.org/10.30872/eksponensial.v15i2.1318

Sari, D. T., Khusna, N. I., & Wulandari, F. (2023). Analisis Tingkat Kemiskinan Di Provinsi Jawa Tengah: Suatu Kajian Berdasarkan Faktor Pendidikan, Sosial, Ekonomi, Lokasi Dan Indeks Pembangunan Manusia. Jurnal PIPSI (Jurnal Pendidikan IPS Indonesia), 8(1), 37. https://doi.org/10.26737/jpipsi.v8i1.3978

Sihite, K., Fatimah, F., Sagala, S. M., Asnidar, A., & Ridha, A. (2024). Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia (IPM) Di Provinsi Jawa Tengah. Santri: Jurnal Ekonomi Dan Keuangan Islam, 2 (1)(1), 6. https://doi.org/10.61132/santri.v2i1.188

Sulistianingsih, E., Suparti, S., & Ispriyanti, D. (2023). Pemodelan Indeks Pembangunan Manusia Di Jawa Tengah Menggunakan Metode Regresi Ridge Dan Regresi Stepwise. Jurnal Gaussian, 11(3), 468–477. https://doi.org/10.14710/j.gauss.11.3.468-477

Syahza, A. (2021). Metodologi Penelitian (Edisi Revisi Tahun 2021) (Issue September). Unri Press. https://www.researchgate.net

Ul Hasanah, F. R., & Yollanda, M. (2022). Penerapan Model Regresi Logistik Terhadap Indeks Pembangunan Manusia (IPM) di Provinsi Sumatera Barat Tahun 2019 – 2021. JOSTECH: Journal of Science and Technology, 2(2), 199–208. https://doi.org/10.15548/jostech.v2i2.4383