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All Journal Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan Jurnal Statistika dan Aplikasinya
Yeni Rahkmawati
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Social Sciences Other

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Pemodelan Regresi Semiparametrik Spline pada Persentase Penduduk Miskin di Provinsi Kalimantan Selatan Muhammad Munawwir; Fuad Muhajirin Farid; Yeni Rahkmawati
Jurnal Litbang Sukowati : Media Penelitian dan Pengembangan Vol 8 No 1 (2024): Vol. 8 No. 1, Mei 2024
Publisher : Badan Perencanaan Pembangunan, Riset dan Inovasi Daerah Kabupaten Sragen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32630/sukowati.v8i1.444

Abstract

Poverty is one of the unresolved development challenges throughout the world, including Indonesia. One of the regions in Indonesia where the percentage of poor people is below the national level is South Kalimantan Province. The percentage of poor people in South Kalimantan Province from 2000 to March 2022 was always below the national level. The economy in South Kalimantan is dominated by the mining and quarrying sector which is less sustainable, allowing the percentage of poor people in South Kalimantan to be prone to increase.  Therefore, it is necessary to model the percentage of poor people based on the factors considered to influence it. This study aims to explain how to model the percentage of poor people based on the assumption that the observed variables affect it. To achieve this goal, the technique applied in modeling the percentage of poor people is spline semiparametric regression. As a result of using this semiparametric spline regression, it was found that the model parameters were not significant, requiring separate modeling for parametric and nonparametric regression. Based on the separation of the model, it’s found that using multiple linear regression there are 2 influential predictor variables, namely adjusted per capita expenditure and the percentage of households using proper water.
THE APPLICATION OF SUPPORT VECTOR MACHINES IN FORECASTING INDONESIA'S EXPORT VALUES Muhammad Jimmy Saputra; Yeni Rahkmawati
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09114

Abstract

Exports play a crucial role in Indonesia's economic growth, but fluctuations in export values can impact national economic stability. While there is existing research on export forecasting, the application of advanced machine learning methods such as Support Vector Machine (SVM) is limited. This study aims to forecast Indonesia’s export values using SVM based on monthly data from January 2017 to February 2025. The data were split into 80:20 proportions for training and testing, with input variables optimized using Partial Autocorrelation Function (PACF) analysis. Fifteen input schemes were tested, and the combination of lag 1 and lag 2 produced the lowest Mean Absolute Percentage Error (MAPE) of 5.04% on the test data, indicating very high accuracy. The forecasted results show a declining trend in export values from 21.87 billion USD in March 2025 to 20.66 billion USD in December 2025, driven by external factors such as global economic slowdown and commodity price fluctuations. Despite the decline, Indonesia’s export values remain relatively high compared to pre-2021 periods. This research highlights the effectiveness of SVM for export forecasting and suggests that this method could be used to inform policy decisions to mitigate global trade risks. Future research could explore the inclusion of additional external variables and other machine learning techniques to further improve forecast accuracy. The novelty of this study lies in the application of SVM for forecasting Indonesia’s export values, filling a gap in the literature on export forecasting models.
Co-Authors Fuad Muhajirin Farid Muhammad Jimmy Saputra Muhammad Munawwir