Modeling Infant Mortality Rate In North Sumatra Using Robust Regression With Generalized Scale (GS) Estimations

Isti Marfu'ah, Yuliana Susanti, Etik Zukhronah

Abstract


Infant Mortality Rate (IMR) is a key indicator in assessing the success of national health development programs. In recent decades, Indonesia’s IMR has declined, but consistent efforts are still needed, especially in provinces with high contributions. In 2023, North Sumatra ranked 3rd among provinces with the highest IMR. Therefore, an analysis is needed to identify factors that may contribute to infant mortality in this region. This study uses several variables, including the number of health centers, the percentage of births in health facilities, the number of low-birth-weight babies, and the number of pregnant women detected as HBsAg reactive. The aim is to develop an IMR model in North Sumatra that can serve as an evaluation reference. Regression analysis is a suitable method to understand the influence of these variables on IMR. However, unmet normality assumptions in classical regression may lead to inaccurate estimates. To address this, robust regression can be applied to obtain models that are more resistant to outliers. This study uses the Generalized Scale (GS) estimation method in robust regression. The resulting GS model produces an adjusted R² value of 86.76% and an AIC value of 108.2717.

Keywords


robust regression; GS estimation; infant mortality; North Sumatra

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References


Andriani, S. (2017). Uji Park Dan Uji Breusch Pagan Godfrey Dalam Pendeteksian Heteroskedastisitas Pada Analisis Regresi. Al-Jabar : Jurnal Pendidikan Matematika, 8(1), 63–72. https://doi.org/10.24042/ajpm.v8i1.1014

Badan Pusat Statistik Sumatera Utara. (2023). Sensus Penduduk 2020 - Sumatera Utara. 09, 1–44. http://sp2010.bps.go.id/

Hamidah, I., Susanti, Y., & Sugiyanto. (2023). Seminar Nasional LPPM UMMAT Perbandingan Estimasi Scale Dan Estimasi Generalized Scale Estimation Dalam Pemodelan Balita Stunting Indonesia. 2(April), 539–546.

Husain, A., & Jamaluddin, S. R. W. (2024). Pemodelan Data Angka Kematian Bayi Menggunakan Regresi Robust. SAINTEK: Jurnal Sains, Teknologi & Komputer, 1(1), 1–7. https://jurnal.larisma.or.id/index.php/SAINTEK/article/view/326

Ihsan, H., Sanusi, W., & Nurfadillah, N. (2019). Estimasi Parameter Regresi Linear Pada Kasus Data Outlier Menggunakan Metode Estimasi Method Of Moment. Journal of Mathematics, Computations, and Statistics, 1(1), 38. https://doi.org/10.35580/jmathcos.v1i1.9176

Montgomery, D. C., Peck, E. A., & Vining, G. G. (2015). Introduction to Linear Regression Analysis-4th edition. 6.

Nugraha, B. (2022). Pengembangan Uji Statistik: Implementasi Metode Regresi Linier Berganda dengan Pertimbangan Uji Asumsi Klasik. Applied Mathematics and Computation (Issue 9), Pradina Pustaka. https://books.google.co.id/books?hl=en&lr=&id=PzZZEAAAQBAJ&oi=fnd&pg=PR5&dq=+Pengembangan+uji+statistik:+Implementasi+metode+regresi+linier+berganda+dengan+pertimbangan+uji+asumsi+klasik.+In+Applied+Mathematics+and+Computation+(Issue+9).+&ots=KxyX_3ubqj&s

Nurdin, N., Raupong, & Islamiyati, A. (2014). Penggunaan Regresi Robust Pada Data Yang Mengandung Pencilan Dengan Metode Momen. Matematika, Statistika Dan Komputasi, 10(2), 115.

Permana, R. A., & Ikasari, D. (2023). Uji Normalitas Data Menggunakan Metode Empirical Distribution Function Dengan Memanfaatkan Matlab Dan Minitab 19. Semnas Ristek (Seminar Nasional Riset Dan Inovasi Teknologi), 7(1), 7–12. https://doi.org/10.30998/semnasristek.v7i1.6238

Rukmono, P., Anggunan, Pinilih, A., & Yuliawati, Si. Sh. (2021). Hubungan Antara Tempat Melahirkan Dengan Angka Kematian Neonatal Di Rsud Dr. H. Abdoel Moeloek Provinsi Lampung. Student Journal, Prevalensi Hbsag Positif Antara Donor Darah Sukarela Dengan Donor Darah Pengganti Di Utd Pmi Provinsi Lampung Tahun 2019-2020, 1, 435–444.

Sholihah, S. M., Aditiya, N. Y., Evani, E. S., & Maghfiroh, S. (2023). Konsep Uji Asumsi Klasik Pada Regresi Linier Berganda. Jurnal Riset Akuntansi Soedirman, 2(2), 102–110. https://doi.org/10.32424/1.jras.2023.2.2.10792

Sihombing, P. R., Suryadiningrat, Sunarjo, D. A., & Yuda, Y. P. A. C. (2023). Identifikasi Data Outlier (Pencilan) dan Kenormalan Data Pada Data Univariat serta Alternatif Penyelesaiannya. Jurnal Ekonomi Dan Statistik Indonesia, 2(3), 307–316. https://doi.org/10.11594/jesi.02.03.07

Susanti, Y., Pratiwi, H., & Qona’ah, I. (2021). Regresi Robust Teori dan Penerapannya (I). UNS PRESS.


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