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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Scientific Journal of Informatics
Ilmani, Erdanisa Aghnia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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PERBANDINGAN ALGORITMA RANDOM FOREST DAN XGBOOST DALAM KLASIFIKASI PENERIMA BANTUAN PANGAN NON-TUNAI (BPNT) DI PROVINSI JAWA BARAT Yulianti, Riska; Ilmani, Erdanisa Aghnia; Waliulu, Megawati Zein; Sartono, Bagus; Firdawanti, Aulia Rizki
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 6 No. 1 (2025): 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.v6i1.850

Abstract

This study compares the performance of Random Forest and XGBoost algorithms in classifying recipients of Non-Cash Food Assistance (BPNT) in West Java Province. The data used is from the 2023 National Socio-Economic Survey (SUSENAS) comprising 25,890 households, with 23.6% BPNT recipients and 76.4% non-recipients. The study includes data exploration, preprocessing, handling class imbalance, baseline modeling, and hyperparameter tuning using Grid Search. The results indicate that undersampling effectively increases the recall of Random Forest to 80.01% and XGBoost to 74.04%, albeit at the expense of accuracy. The most influential variables in classification include the head of household's employment status, flooring material of the house, and type of land/building ownership proof. These findings support the utilization of data-driven algorithms to enhance the accuracy and fairness of BPNT distribution.
Clustering Time Series Forecasting Model for Grouping Provinces in Indonesia Based on Granulated Sugar Prices Amatullah, Fida Fariha; Ilmani, Erdanisa Aghnia; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L. M. Risman Dwi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8840

Abstract

Clustering time series is the process of organizing data into groups based on similarities in specific patterns. This research uses the prices of granulated sugar in each province of Indonesia. According to USDA reports, sugar consumption in Indonesia in 2023 reached 7.9 million tons. On April 26, 2024, the price of granulated sugar peaked in the Papua Mountains at Rp29,320 per kg, while the lowest price was recorded in the Riau Islands at Rp16,460 per kg. The research aims to cluster provinces based on the characteristics of granulated sugar prices and to use forecasting models for each group. Two groups were formed based on the price patterns of granulated sugar over time. The provinces of Papua and West Papua are in group 2, while the other 30 provinces are in group 1. The best model developed using the auto ARIMA method is ARIMA (2, 1, 0), with a MAPE value of 2.36% for cluster 1, and ARIMA (1, 1, 1), with a MAPE value of 2.59% for cluster 2. These values are less than 10%, indicating that the models built using the auto ARIMA method for clusters 1 and 2 are suitable for forecasting.
THE PERFOMANCE OF THE ARIMAX MODEL ON COOKING OIL PRICE DATA IN INDONESIA Ilmani, Erdanisa Aghnia; Amatullah, Fida Fariha; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp819-828

Abstract

Forecasting is crucial for planning, particularly in addressing potential issues. While ARIMA models are commonly used for time series forecasting, they may need more accuracy by overlooking external factors. The ARIMAX model, which incorporates exogenous variables, is employed to enhance accuracy. This study applies the ARIMAX model to forecast cooking oil prices in Indonesia, known for its complex patterns. Using data from the Directorate General of Domestic Trade and Price Stability (2024), the research highlights fluctuating cooking oil prices from 2010 to 2023 every month. Both ARIMA and ARIMAX models are utilized, with domestic fresh fruit bunch (FFB) prices and the COVID-19 pandemic indicator as exogenous variables. Evaluation based on Mean Absolute Percentage Error (MAPE) shows that the ARIMAX model has a MAPE of 17.31%, compared to 17.69% for the ARIMA model. The lower MAPE value for ARIMAX indicates improved forecasting accuracy by incorporating external factors. Thus, the ARIMAX model is recommended for predicting cooking oil prices, offering better accuracy and valuable insights for policymakers and stakeholders.
Rice Price Forecasting for All Provinces in Indonesia Using The Time Series Clustering Approach and Ensemble Empirical Mode Decomposition Ilmani, Erdanisa Aghnia; Sumertajaya, I Made; Fitrianto, Anwar
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.23536

Abstract

Purpose: Accurate forecasting of rice prices is essential to ensure food security and a healthy economy for a country like Indonesia. Problems regarding time-series phenomena, such as trends or seasonality, are problematic for traditional approaches like ARIMA (Autoregressive Integrated Moving Average). This study analyzes the effect of EEMD (Ensemble Empirical Mode Decomposition) combined with time-series data clustering on forecasting accuracy. Methods: From 2009 until 2023, the thirty-two Indonesian provincial rice prices were grouped monthly into time-series clusters using hierarchical clustering, average linkage, and DTW (Dynamic Time Warping). After clusterization, the time series were decomposed using the ensemble EEMD method to extract their IMFs (Intrinsic Mode Functions) and residual components. Each IMF was assigned an ARIMA model. The model forecast was generated by adding all individual estimates. MAPE (Mean Absolute Percentage Error) was used to measure the model's performance. Result: The prices were divided into three clusters with an optimized region. Price changes are well captured through EEMD, where the residual components contributed predominantly to the long-term trends. The validation of the prediction showed MAPE values under 10% for the majority of the provinces, which indicates a relatively accurate prediction. On the other hand, some regions had inaccuracies that were higher than others due to uncontrollable fluctuations. Novelty: This study integrates clustering with EEMD decomposition for monthly rice price forecasting using data from 32 Indonesian provinces from 2009 - 2023, offering a novel approach that improves traditional techniques. The model can capture distinct regional price patterns and provide essential information to policymakers to manage rice supply and price stabilization. Further studies can develop external hybrid models with economic variables.
Co-Authors Amatullah, Fida Fariha Aulia Rizki Firdawanti Bagus Sartono Erfiani Erfiani Fitrianto, Anwar I Made Sumertajaya Jumansyah, L. M. Risman Dwi Mualifah, Laily Nissa Atul Riska Yulianti, Riska Waliulu, Megawati Zein Yenni Angraini