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All Journal Jurnal Statistika dan Aplikasinya RAGAM: Journal of Statistics and Its Application
Yeni Rahkmawati
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health Social Sciences Other

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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.
PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) MENGGUNAKAN PEMBOBOT KERNEL PADA KASUS TINGKAT PENGANGGURAN TERBUKA DI KALIMANTAN Viona Oktafiani; Dewi Sri Susanti; Yeni Rahkmawati
RAGAM: Journal of Statistics & Its Application Vol 3, No 1 (2024): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v3i1.12822

Abstract

AbstractUnemployment is one of the serious problems in Indonesia's economic development. This unemployment describes human resources that have not been utilized optimally, as a result of which people's productivity and income have not been maximized, this can also be one of the causes of poverty and other social problems. This study aims to find out the general picture of the open unemployment rate in the Kalimantan region, get the best model and factors that influence the open unemployment rate and illustrate it through thematic maps. The study began with testing assumptions and spatial effects then continued with testing global regression modeling and Geographically Weighted Regression. The weighting function used in this study is adaptive gaussian kernel. The variable that has a positive effect on the open unemployment rate in the Kalimantan region is population density. While the variable that negatively affects the open unemployment rate is the Labor Force Participation Rate. Keywords:   Open Unemployment Rate, Kalimantan Island, Spatial, GWR
Co-Authors Dewi Sri Susanti Muhammad Jimmy Saputra Viona Oktafiani