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Mualifah, Laily Nissa Atul
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Transportation Veterinary Other

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Comparative Analysis of ARIMA and LSTM for Forecasting Maximum Wind Speed in Kupang City, East Nusa Tenggara Magfirrah, Indah; Ilma, Meisyatul; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.25834

Abstract

This study compares the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models for predicting maximum wind speed based on accuracy measured by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Based on the results of the research, the LSTM model is better than the ARIMA model in predicting maximum wind speed in Kupang City, East Nusa Tenggara Province. The best LSTM model has hyperparameters of 200 epochs; batch size of 32; learning rate of 0,001; and 8 neurons. Based on the evaluation results of predicted data against actual data, the MAPE value of the LSTM model is 19,40%. The benefit of this research is that it can contribute to the literature on the development of wind utilization as a basis for building power plants on small islands as a renewable resource, particularly in Kupang City, East Nusa Tenggara.
Comparison of Seasonal ARIMA and Support Vector Machine Forecasting Method for International Arrival in Lombok MY, Hadyanti Utami; Setyowati, Silfiana Lis; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.26478

Abstract

Seasonal Autoregressive Integrated Moving Average is a statistical model designed to analyze and forecast data with that shows seasonal patterns and trends. Support Vector Machine (SVM) is a machine learning-based technique that can be used to forecast time series data. SVM uses the kernel tricks to overcome non-linearity problems, whereas The SARIMA model is well-suited for data that exhibit seasonal fluctuations that repeat over time. Lombok International Airport is the main gateway to West Nusa Tenggara and has become a symbol of tourism growth in the region. Time series analysis is a very useful tool in determining patterns and forecasting the number of international arrivals at Lombok International Airport within a certain period. This study aims to compare the SARIMA model and SVM which can read non-linear patterns in the number of international arrivals at Lombok International Airport. After obtaining the SARIMA and SVM models, the two models are evaluated using test data based on the smallest RMSE value. The SVM model with a linear kernel trick provides the smallest RMSE when compared to SARIMA with SVM RMSE is 238,655. While the best model in Seasonal ARIMA is SARIMA (3,1,0)(1,0,0)12, the forecasting results show SARIMA is better in the forecasting process for the next 10 months.
TIME SERIES MODEL FOR TRAIN PASSENGER FORECASTING Hakim, Bashir Ammar; Billy, Billy; 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/barekengvol19iss2pp755-766

Abstract

Trains as a means of public transportation have an important role in connecting various regions of Jabodetabek. Therefore, it is necessary to have a deep understanding of the trend of train passenger movements and predict the number of train passengers in the next period in order to optimize the management and service of train passengers properly. In this study, we examine two methods that can be used as forecasting methods for train passenger data sourced from the Central Statistics Agency (BPS), namely ARIMA and Prophet. This study demonstrates that the optimal ARIMA model is ARIMA (0,2,1), achieving a Mean Absolute Percentage Error (MAPE) of 4.91% and a Root Mean Square Error (RMSE) of 1754.970. In addition, the Prophet model, which is an additive regression model designed by Facebook for time series forecasting was also obtained with a MAPE of 0.04% and an RMSE of 1170.59. Considering the MAPE and RMSE values of the two models, the Prophet model emerges as the most suitable for forecasting the number of train passengers in the Jabodetabek region.
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.
Evaluation of the SARIMA and Prophet Models in Forecasting Ship Passenger Numbers at Balikpapan Port Cintani, Meavi; Nizar, Yeky Abil; Angraini, Yenni; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.31606

Abstract

Balikpapan Port serves as a vital transportation hub in eastern Indonesia, particularly in supporting the development of the Nusantara Capital City (IKN). This study evaluates the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet models in predicting short-term ship passenger volumes using monthly data from January 2006 to December 2024 obtained from the East Kalimantan Provincial Transportation Office. Our analysis identifies SARIMA (MAPE = 24%) as the more accurate model compared to Prophet (MAPE = 34%). The optimal SARIMA model was then used to generate a focused forecast for December 2025, providing targeted insights for peak-season port management. These results assist port authorities in resource allocation, infrastructure planning, and policy formulation to accommodate anticipated passenger surges during critical periods.
LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG) Cici Suhaeni; Mualifah, Laily Nissa Atul; Wijayanto, Hari
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study investigates public discourse on Indonesia's Free Nutritious Meals (Makan Bergizi Gratis/MBG) program through Latent Dirichlet Allocation (LDA) topic modeling of YouTube comments. Filling a research gap on online public opinion regarding the MBG policy, this study identifies dominant themes and discursive patterns in public perception. A three-topic model, validated through coherence score evaluation and pyLDAvis visualization, reveals key topics: concerns over food prices and distribution, perceived benefits for children and society, and emotionally and politically driven reactions. The findings provide valuable insights into public opinion, while also highlighting challenges in processing Indonesian-language text, such as informal language and noisy data. This study contributes to understanding public perceptions of social policies in digital environments and recommends future research directions, including improved text preprocessing and alternative topic modeling approaches. By shedding light on online public discourse, this research informs policymakers and stakeholders about the effectiveness and potential areas for improvement in the MBG program.
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.
Peramalan Harga Emas Berjangka Menggunakan Metode ARIMA-GARCH Hasanah, Mauizatun; Putri, Mega Ramatika; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32723

Abstract

Gold futures price forecasting plays an important role in investment decision-making and risk management, especially in the midst of volatile commodity market dynamics. This research aims to build an accurate gold futures price forecasting model by combining Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The ARIMA model is used to capture linear patterns and historical trends in time series data, while the GARCH model is able to handle the high volatility characteristic of gold price movements, something that conventional forecasting models often fail to capture. The data used in this study is daily gold futures price data collected over the period January 3, 2023 to March 31, 2025, which covers both normal market conditions and periods of turmoil, making it relevant to describe the overall market dynamics. The forecasting results show that the ARIMA-GARCH model with components (3,1,3) (1,1) with a MAPE of 4.52% indicates a good level of accuracy in the context of forecasting gold futures prices that have high volatility. Thus, this model provides precise forecasting results with actual data so that it can be used by market participants and policy makers in managing risks and designing strategies.
THE COMPARISON OF LONG SHORT-TERM MEMORY AND BIDIRECTIONAL LONG SHORT-TERM MEMORY FOR FORECASTING COAL PRICE Siregar, Indra Rivaldi; Nugraha, Adhiyatma; 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/barekengvol19iss1pp245-258

Abstract

Coal remains vital for global energy despite recent demand fluctuations due to the COVID-19 pandemic and geopolitical tensions. The International Energy Agency (IEA) projected a decline in global coal demand starting in early 2024, driven by increasing renewable energy adoption. As one of the top coal exporters, Indonesia must adjust to these changes. This study aims to forecast future coal prices using historical data from Indonesia's Ministry of Energy and Mineral Resources (KESDM), applying and comparing Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models. While BiLSTM has shown advantages in other contexts and studies, its effectiveness for coal price forecasting remains underexplored. To ensure robust predictions, we employ walk-forward validation, which divides the data into six segments and evaluates 90 hyperparameter combinations across all segments. The BiLSTM model consistently outperforms the LSTM model, achieving lower average RMSE and MAPE values. Specifically, BiLSTM records an average MAPE of 7.847 and RMSE of 10.485, compared to LSTM's 10.442 and 11.993, respectively. The Diebold-Mariano (DM) test using squared error and absolute error loss functions further corroborates these findings, with most segments showing significant improvements in favor of BiLSTM, indicated by negative DM-test statistics and p-values below 0.01 or 0.10. This superior performance continues into the testing data, where BiLSTM maintains lower error metrics and a significant result of the DM test, underscoring its reliability for forecasting. In the final stage, the forecasts from both models indicate a nearly linear downward trend in coal prices over the next 18 months, aligning with the International Energy Agency's 2023 projection of a structural decline in coal demand driven by the sustained growth of clean energy technologies.
COMPARISON OF SARIMA AND SARIMAX METHODS FOR FORECASTING HARVESTED DRY GRAIN PRICES IN INDONESIA Yulianti, Riska; Amanda, Nabila Tri; 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/barekengvol19iss1pp319-330

Abstract

Harvested dry grain (HDG) is a vital commodity for rice availability and plays a strategic role in Indonesia’s agricultural economy. Farmers typically sell HDG to rice millers post-harvest, yet disparities between farm-level selling prices and consumer-level purchase prices. This price gap can lead to financial losses for farmers, highlighting the need for accurate forecasting can lead to potential losses for farmers. SARIMA models are effective in capturing seasonality and trends but often fail to incorporate external factors influencing the dependent variable, resulting in less accurate forecasts when such factors have significant impacts. SARIMAX models, however, can include exogenous variables like the government purchase price (GPP), which supports farmer income by establishing a price floor for HDG and directly influencing farm-level price dynamics. This study aims to compare the SARIMA and SARIMAX models in forecasting HDG prices at the farm level in Indonesia, using GPP as an exogenous variable. The dataset, obtained from Statistics Indonesia, covers January 2008 to March 2024, and the forecasting accuracy is measured using Mean Absolute Percentage Error (MAPE). The findings indicate that the best model is the SARIMAX model (1,1,1)(0,1,2)12, achieving a MAPE of 10.919%. The forecasted results show that HDG prices in 2024 are expected to remain stable, with only a gradual increase throughout the year.
Co-Authors Ahdiat, Keyzha Mutiara Al Maida, Mahda Alahmad, Ali Omar Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Billy, Billy Cici Suhaeni Cintani, Meavi Fitrianti, Dwi Ghiffary, Ghardapaty Ghaly Hakim, Bashir Ammar Hari Wijayanto Hasanah, Lailatul Hasanah, Mauizatun Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indahwati Irdayanti Irdayanti Isnaini, Mardatunnisa Karunia, Nia Kefi Amtiran, Chandraone Putra Kurnadipare, Aleytha Ilahnugrah Magfirrah, Indah Maulana, Muhammad Fahrezi MY, Hadyanti Utami Nensi, Andi Illa Erviani Nizar, Yeky Abil Nugraha, Adhiyatma Nurdin, Achmad Raihan Pangestika, Adelia Putri Pratiwi, Windy Ayu Putri, Mega Ramatika Rahmawati, Elke Frida Rasyid, Baharun Riska Yulianti, Riska Riyanto, Indra Mahib Zuhair Rizki, Anwar Fajar Sachnaz Desta Oktarina Setyowati, Silfiana Lis Siregar, Indra Rivaldi Sri Wahyuni Syam, Ummul Auliyah Ulfia, Ratu Risha Yanuari, Eka Dicky Darmawan Yenni Angraini Zulhijrah, Zulhijrah