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All Journal Media Statistika Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Ulfia, Ratu Risha
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Decision Sciences, Operations Research & Management Education Mathematics

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STUDI KOMPARASI METODE SVM-SMOTE DAN SMOTE-TOMEK DALAM MENGATASI IMBALANCE CLASS MENGGUNAKAN MODEL XGBOOST PADA KLASIFIKASI RUMAH TANGGA PENERIMA KUR Yanuari, Eka Dicky Darmawan; Yudhianto, Rachmat Bintang; Ulfia, Ratu Risha; Sartono, Bagus; Firdawanti, Aulia Rizki
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.857

Abstract

This study aims to compare the SMOTE, SVM-SMOTE, and SMOTE-Tomek methods using the XGBoost model in overcoming the problem of class imbalance and to determine the factors that affect the status of KUR recipients in West Java Province. Three XGBoost models with class balancing techniques SMOTE, SVM-SMOTE and SMOTE-Tomek were applied to SUSENAS data of West Java Province in 2023 consisting of 1 response variable and 19 predictor variables. The results showed that the XGBoost model with the SMOTE balancing method produced better accuracy in overall data classification, but was less effective in classifying minority classes as reflected by low sensitivity and F1-Score values. The XGBoost model with the SMOTE-Tomek balancing method showed better performance in capturing minority classes with higher sensitivity and F1-Score values. The most influential variables in this model in order are per capita expenditure, urban/rural classification, motorcycle ownership, dwelling wall materials and land ownership. Per capita expenditure has the largest influence on the classification of KUR recipients, indicating that household financial management is a major factor in lending decisions. Urban/rural classification and motorcycle ownership also contributed significantly, reflecting differences in social and economic access between regions. Overall, economic factors, infrastructure and social accessibility are the main considerations in determining KUR recipient households in West Java Province.
COMPARATIVE EVALUATION OF ARIMA AND GRU MODELS IN PREDICTING RUPIAH DOLLAR EXCHANGE RATE Fitrianti, Dwi; Ulfia, Ratu Risha; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.1-12

Abstract

This study evaluates the effectiveness of the ARIMA (Autoregressive Integrated Moving Average) and GRU (Gated Recurrent Unit) models in forecasting the USD–Rupiah exchange rate. Exchange rate fluctuations influence overall economic stability, making accurate forecasting crucial. Monthly data from January 2001 to March 2024 were analyzed. The ARIMA model, a traditional statistical approach, combines autoregressive (AR), differencing (I), and moving average (MA) components to capture linear patterns, while the GRU model, a deep learning approach, captures nonlinear and complex temporal relationships using update and reset gate mechanisms to retain long-term information. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The GRU model achieved a MAPE of 1.74%, lower than the ARIMA model’s 1.94%, and generated a forecast of Rp. 16,399.91 for April 2024, closer to the actual value of Rp. 16,249.00 compared to ARIMA’s Rp. 15,857.68. The findings demonstrate the GRU model’s superior forecasting performance and provide empirical evidence of its effectiveness in modeling volatile exchange rate data, particularly the Rupiah–USD rate.
Co-Authors Aulia Rizki Firdawanti Bagus Sartono Fitrianti, Dwi Mualifah, Laily Nissa Atul Yanuari, Eka Dicky Darmawan Yenni Angraini Yudhianto, Rachmat Bintang