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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Mahda Al Maida
IPB University
Computer Science & IT Mathematics

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Performance Analysis of ARIMA, LSTM, and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index Andi Illa Erviani Nensi; Mahda Al Maida; Khairil Anwar Notodiputro; Yenni Angraini; Laily Nissa Atul Mualifah
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33379

Abstract

This study evaluates the performance of ARIMA, LSTM, and hybrid ARIMA-LSTM models in predicting the closing and opening prices of the Indonesia Stock Exchange Composite Index (IHSG) over various periods (2007-2020, 2007-2022, and 2007-2024). For the LSTM model, a lag of 1 was chosen based on MAPE analysis, showing strong dependence on the previous day’s price. Different learning rates (0.01, 0.001, 0.0001) and batch sizes (16, 32) were tested on various network architectures. Results indicate that while ARIMA effectively captures linear patterns, LSTM consistently outperforms with lower MAPE values—2.27% for closing and 2.02% for opening prices—especially with a simple (1-50-1) architecture and a learning rate of 0.001. The hybrid ARIMA(0,1,1)-LSTM(1-50-1) model showed competitive results, achieving MAPE of 2.00% for closing and 1.74% for opening prices using batch size 16. However, its success depends on ARIMA’s ability to model linear components. Key findings emphasize LSTM’s dominance in accuracy, the importance of parameter tuning, and the effectiveness of simple network structures. The hybrid approach holds promise when linear and nonlinear data components are clearly separable. This research offers methodological insights for optimizing stock price prediction models and practical guidance for model configuration, contributing to the advancement of financial market forecasting.
Classification of Educational Vulnerability Status Using Boosting Methods on Imbalanced Socioeconomic Data Andi Illa Erviani Nensi; Meavi Cintani; Mahda Al Maida; A. Qeis Tenridapi; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.39670

Abstract

Extreme class imbalance in educational data causes classification models to favor the majority class and fail to identify vulnerable students who are not attending school, despite this group requiring the greatest intervention. This study aims to develop a classification model for identifying educational vulnerability status among school-age children. The positive class corresponds to out-of-school children (minority class), while the negative class represents children who are still attending school. The dataset exhibits an extreme imbalance ratio of approximately 1:48, making conventional classification approaches ineffective in detecting minority-class observations.To address this challenge, four imbalance-aware boosting methods, namely AdaBoost-M2, SMOTEBoost, RBBoost, and RUSBoost, were compared. Model selection was conducted using stratified five-fold cross-validation on the training set, followed by final evaluation on an independent test set. Model performance was assessed using sensitivity, specificity, balanced accuracy, and Area Under the Curve (AUC), which are more appropriate than overall accuracy for highly imbalanced data. The results show that SMOTEBoost L50 with a threshold of 0.50 achieved the highest Balanced Accuracy (0.7474), Sensitivity (0.8824), and AUC (0.7745). However, its low minority-class precision (0.0452) and F1-score (0.0860) indicate a substantial false-positive trade-off. These findings demonstrate that integrating minority-class balancing strategies with boosting mechanisms can substantially improve the detection of vulnerable students in highly imbalanced socioeconomic data. Feature importance analysis revealed that education level or class was the most dominant predictor, followed by household size and frequency of internet use. Overall, this study provides empirical evidence regarding the effectiveness of imbalance-aware boosting methods for educational vulnerability detection and highlights their potential application in supporting more targeted educational interventions.
Implementing LSTM-Based Deep Learning for Forecasting Food Commodity Prices with High Volatility: A Case Study in East Java Province Andi Illa Erviani Nensi; Windi Pangesti; Nabila Syukri; Mahda Al Maida; Khairil Anwar Notodiputro
Joint International Conference 2026: The 2nd Indonesia Forum on Labour and Productivity (IFLP) & The 4th International Conference on Data Science and Official Statistics (ICDSOS) Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.692

Abstract

Accurate food price forecasting is essential for maintaining market stability and food security. East Java Province was selected as the study area because it is one of Indonesia’s main food production centers and a major contributor to national inflation. This study compares three deep learning architectures LSTM, Bi-LSTM, and hybrid CNN-LSTM to forecast the prices of four key food commodities (red chili, shallots, medium-grade rice, and beef) in East Java. Hyperparameter tuning was performed using grid search, and performance was evaluated using MAPE, MAE, and RMSE. The results show that the Bi-LSTM model consistently provides the best performance compared to LSTM and CNN-LSTM across the four analyzed commodities. Based on MAPE, MAE, and RMSE values, Bi-LSTM achieved the lowest forecasting errors for all commodities. The MAPE values of Bi-LSTM were 1.73% for red chili, 0.60% for shallots, 0.23% for medium-grade rice, and 0.08% for beef, all of which were lower than those of LSTM and CNN-LSTM models. These findings highlight Bi-LSTM’s bidirectional architecture, which leverages contextual information from both past and future data sequences, making it the most robust and effective model for forecasting food prices under varying volatility. The study provides practical insights for policymakers and supply chain stakeholders in supporting price stability and food security.
Co-Authors A. Qeis Tenridapi Andi Illa Erviani Nensi Aulia Rizki Firdawanti Bagus Sartono Budi Susetyo Gerry Alfa Dito Laily Nissa Atul Mualifah Meavi Cintani Nabila Syukri Windi Pangesti Yenni Angraini