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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Ghiffary, Ghardapaty Ghaly
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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ANALISIS KINERJA MODEL STACKING BERBASIS RANDOM FOREST DAN SVM DALAM KLASIFIKASI RUMAH TANGGA BERDASARKAN GARIS KEMISKINAN MAKANAN DI PROVINSI JAWA BARAT Ghiffary, Ghardapaty Ghaly; Amanda, Nabila Tri; Ardhani, Rizky; 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.v5i3.856

Abstract

The stacking method is an ensemble technique in machine learning that combines predictions from several base models to improve classification accuracy. This research applies the stacking method with two machine learning algorithms, namely Random Forest and Support Vector Machine (SVM) as base learners and logistic regression as a meta learner. This study aims to develop a classification model to identify households based on the food poverty line in West Java Province. The data used is KOR and household data in West Java Province sourced from the 2023 BPS National Socio-Economic Survey (Susenas). The variables used consisted of 24 independent variables with food poverty level as the response variable. Modeling was conducted using feature selection using Recursive Feature Elimination (RFE) and class imbalance handling using the ADASYN method. The results showed that the stacking model was superior to the single model with a balance accuracy of 0.81, sensitivity of 0.72, and specificity of 0.89. Feature importance analysis identified that calorie consumption, expenditure on cigarettes, meat and fruits, and expenditure on rice, eggs and other commodities contributed the most to the classification households based on the food poverty line in West Java Province.
COMPARATIVE PERFORMANCE OF SARIMAX AND LSTM MODEL IN PREDICTING IMPORT QUANTITIES OF MILK, BUTTER, AND EGGS Ghiffary, Ghardapaty Ghaly; Yanuari, Eka Dicky Darmawan; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp407-418

Abstract

This study evaluates how well SARIMAX and LSTM models predict monthly imports of HS-04 commodities (butter, eggs, and milk) in Indonesia. Data were provided by BPS Statistics Indonesia, Bank Indonesia, Ministry of Trade, Trade Map, and Indonesia National Single Window and used from January 2006 to February 2024. The SARIMAX model included exogenous variables such as inflation rates, USD/IDR exchange rates, and major Indonesian holidays (Eid al-Fitr, Eid al-Adha, Christmas, and Lunar New Year). The results show that the SARIMAX and LSTM models predict the import volumes of butter, eggs, and milk with good accuracy. However, the SARIMAX model demonstrated superior forecasting accuracy, achieving a lower RMSE of 7547.89 and a MAPE of 13.16 compared to the LSTM model, which yielded an RMSE of 8787.73 and a MAPE of 14.89. The SARIMAX model performed significantly better when the lunar new year was added as an exogenous variable. In order to support price stability and economic growth in Indonesia, this research provides policymakers and industry stakeholders with critical information to optimize import management strategies for butter, eggs, and milk commodities. Accurate forecasts can contribute to price stability, enhanced food security, and sustainable economic development in Indonesia by enabling informed decisions on import quotas, tariff adjustments, investment in domestic production, and strategic reserves.
Co-Authors Amanda, Nabila Tri Ardhani, Rizky Aulia Rizki Firdawanti Bagus Sartono Mualifah, Laily Nissa Atul Yanuari, Eka Dicky Darmawan Yenni Angraini