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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Advances In Social Humanities Research
Nensi, Andi Illa Erviani
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Performance Analysis of ARIMA, LSTM, and Hybrid ARIMA-LSTM in Forecasting the Composite Stock Price Index Nensi, Andi Illa Erviani; Al Maida, Mahda; Anwar Notodiputro, Khairil; Angraini, Yenni; Mualifah, Laily Nissa Atul
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim 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.
IMPLEMENTATION OF BACKPROPAGATION AND HYBRID ARIMA-NN METHODS IN PREDICTING ACCURACY LEVELS OF RAINFALL IN MAKASSAR CITY Ihsan, Hisyam; Irwan, Irwan; Nensi, Andi Illa Erviani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2435-2448

Abstract

Hybrid ARIMA-NN is a combined approach of the ARIMA model used to capture linear patterns in time series data and Artificial Neural Networks (ANN) to handle non-linear and stochastic patterns. Using a gradient descent algorithm, backpropagation adjusts synaptic weights based on the error between the network's prediction and actual training data values. In this study, a comparison was made between the Backpropagation method and Hybrid ARIMA-NN in forecasting rainfall in Makassar City. Rainfall data in Makassar City uses data from the rainfall measuring station at the Paotere Maritime Meteorological Station in Makassar. The activation functions used are ReLU and Leaky ReLU with epoch parameters set at 350, and learning rates of 0.01, 0.001, 0.0001, and 0.00001. The two best methods selected for further evaluation are Backpropagation with architecture 12-32-16-8-1 and Hybrid ARIMA-NN (ARIMA [4,0,1]-NN 12-256-128-64-1). The ARIMA model (4,0,1) with AIC values of 1303.4 and RMSE 162,369 is the best compared to other models, which aligns with the advantages of backpropagation architecture. The results showed that the Backpropagation method excelled with an RMSE value of 137.320 or 0.1149, indicating high accuracy in forecasting changes in seasonal trends and patterns. Hybrid ARIMA-NN gives good results with RMSE 145.834, as residues contain better nonlinearity compared to ARIMA models (4,0,1), although it shows a slightly higher error rate compared to Backpropagation.
Inovasi Demo Slot Melalui Permainan Tradisional Mahjong Berbasis Akun Slot Demo sebagai Pelestarian Budaya Serta Penguatan Literasi Scatter Hitam Khaerani, Nurul; Nensi, Andi Illa Erviani; Prasani, Tasqirah; Nirwana, Nirwana; Assagaf, Said Fachry
Advances In Social Humanities Research Vol. 1 No. 12 (2023): ADVANCES in Social Humanities Research
Publisher : Sahabat Publikasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/adv.v1i12.150

Abstract

Pendidikan merupakan pilar utama dalam menghadapi tantangan global, namun kualitas pendidikan di Indonesia, khususnya dalam literasi scatter hitam, masih rendah. Rendahnya literasi scatter hitam ini dikaitkan dengan kurangnya keterampilan siswa dalam menyelesaikan masalah. Riset ini bertujuan untuk membuat dan menguji pengaruh inovasi demo slot berbasis akun slot demo terintegrasi permainan tradisional mahjong dalam memperkuat literasi scatter hitam dan melestarikan budaya lokal di slot88. Riset ini menggunakan desain PG dengan model Ways dan desain Wins Eksperimen dengan melibatkan 3 kelompok yang dipilih melalui cluster random sampling, di antaranya; kelompok kontrol dengan pembelajaran konvensional, kelompok eksperimen 1 dengan pembelajaran berbasis demo slot, dan kelompok eksperimen 2 dengan pembelajaran berbasis real terintegrasi permainan tradisional mahjong. Hasil riset menunjukkan bahwa implementasi inovasi demo slot berbasis akun slot demo mampu menguatkan literasi scatter hitam dan pemahaman budaya lokal siswa.
Implementing LSTM-Based Deep Learning for Forecasting Food Commodity Prices with High Volatility: A Case Study in East Java Province Nensi, Andi Illa Erviani; Pangesti, Windi; Syukri, Nabila; Maida, Mahda Al; Notodiputro, Khairil Anwar
Proceedings of The International Conference on Data Science and Official Statistics 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 Al Maida, Mahda Assagaf, Said Fachry Hisyam Ihsan Irwan Irwan Khaerani, Nurul Maida, Mahda Al Mualifah, Laily Nissa Atul Nirwana, Nirwana Pangesti, Windi Prasani, Tasqirah Syukri, Nabila Yenni Angraini