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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Rangkiang Mathematics Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting Indonesian Journal of Statistics and Its Applications
Fitri, Fadhilah
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Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Rangkiang Mathematics Journal

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Comparison of Linear Regression and Polynomial Local Regression in Modeling Prevalence of Stunting Fitri, Fadhilah; Almuhayar, Mawanda
Rangkiang Mathematics Journal Vol. 4 No. 1 (2025): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v4i1.81

Abstract

Stunting is one of the main focuses of the government in Indonesia. This is because nutritional status is one of the benchmarks of community welfare. Stunting can be influenced by various societal aspects such as health, economy, social status, and education. One factor that is thought to be closely related to stunting is the level of education. Therefore, the prevalence of stunting and the level of education will be modeled; in this case, the mean years of schooling is used. Modeling uses two approaches: parametric through linear regression and nonparametric through local polynomial regression. This study compares both models to see which method better explains the stunting phenomenon. The comparison is made through the determination coefficient value or R2, Root Mean Square Error or RMSE, and the fitted curve plot. The results of R2 and RMSE for both models were obtained. The linear regression model has an R2 of 32.94% and an RMSE of 4.84. Meanwhile, for the local polynomial model, it is R2 43.44% and RMSE 4.32. Based on these results, it can be concluded that local polynomial regression is better at modeling the relationship between the prevalence of stunting and mean years of schooling in Indonesia. This finding confirms that the polynomial local regression method can capture phenomena that occur for data that do not follow a particular pattern.
Nonparametric Fourier Series Regression for Unemployment Analysis in Banten Province Barokah, Bunga Miftahul; Fitri, Fadhilah; Wirdiastuti, Chairina
Rangkiang Mathematics Journal Vol. 5 No. 1 (2026): Rangkiang Mathematics Journal
Publisher : Department of Mathematics, Universitas Negeri Padang (UNP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/rmj.v5i1.90

Abstract

The Open Unemployment Rate (OUR) is a vital indicator of regional economic performance, particularly in Banten Province, which faces disparities in education and poverty. This study models the unemployment rate using two predictors: average years of schooling and poverty level, through a nonparametric Fourier series regression for the 2017–2024 period. This method provides greater flexibility in capturing the nonlinear and fluctuating patterns often observed in socio-economic data. The analysis used secondary data from Statistics Indonesia (BPS), beginning with descriptive statistics and data visualization. Models were evaluated using Generalized Cross-Validation (GCV) and the coefficient of determination (R²). The optimal model was found at K = 3, with a GCV of 2.4057 and an R² of 0.5155. The model effectively captured the non-linear relationships between unemployment, education, and poverty. Although the R² value is moderate, this indicates that including additional explanatory variables could enhance the model’s performance. These findings support the use of Fourier series regression as an alternative approach for labor market analysis, especially when linear methods fall short and provide insights for developing more targeted employment policies.
Co-Authors Almuhayar, Mawanda Barokah, Bunga Miftahul Dina Fitria Dina Fitria, Dina Dodi Vionanda Faulina Fishuri, Nufhika Ikhsan, Easbi Inna Auliya Ketrin, Melin Wanike Larissa, Dwika Maulani Meutia Rani Muhammad Faisal Mukhti, Tessy Octavia Nonong Amalita Permana, Dony Prima Sari, Devni Putri, Eno Dwi Putri, Fadhira Vitasha Rahmadina Adityana Rizki Akbar Safitri, Melda Salma, Admi Sari, Widia Kemala Syafriandi Syafriandi Tessy Octavia Mukhti Widiyanti Wirdiastuti, Chairina Yenni Kurniawati Yuki Fitria Zamahsary Martha Zilrahmi, Zilrahmi