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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik Informatika (JUTIF) Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Musa, Shahrulniza
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology

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A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease Nastiti, Faulinda Ely; Musa, Shahrulniza; Yafi, Eiad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2036-2048

Abstract

In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating LSTM initial weights with weaknesses, using the hybrid optimization algorithm helps overcome these problems and improve model performance. The results of this study suggest that the LSTM-PSO-GWO model can be utilized as an effective and reliable predictive tool to gauge the COVID-19 virus’s spread in Indonesia. 
EPIDEMIC PROGNOSIS: COMPARATIVE PERFORMANCE OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR PREDICTING VIRUS TRANSMISSION DYNAMICS Ely Nastiti, Faulinda; Musa, Shahrulniza; Yafi, Eiad; Ardiyanto, Marta
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3401

Abstract

The transmission of viral diseases, such as COVID-19, influenza, and other viral strains, poses a substantial worldwide challenge. In the context of health, it is necessary to possess a comprehensive comprehension, meticulous examination, and precise anticipation of the dissemination of this infectious disease. Nonetheless, the presence of diverse data characteristics among different nations poses a considerable obstacle in the development of prediction models for assessing the transmission, mortality, and recovery rates in Indonesia. Understanding the intricacies of viral transmission poses a significant hurdle because to the fluctuating nature of the generalization rate, which is contingent upon country-specific data.The research entailed a comparison of different predictive models, including Random Forest, Simple Linear Regression (SLR), Gaussian Naive Bayes, Multi-Layer Perceptron (MLP), H2O, and Long Short-Term Memory (LSTM), with the purpose of predicting viral transmission. The evaluation metrics encompass MAE, RMSE, and MAPE. The outcomes of the examination of comparison models will aid in identifying the most suitable model for forecasting the transmission of the virus, encompassing the rates of recovery, death, and positive cases, within the specific setting of Indonesia. This work has significance in elucidating the inherent trade-off between efficiency and accuracy within the realm of dynamic data modeling, specifically in the context of COVID-19 viral data.
Hybrid LSTM Forecasting Framework with Mutual Information and PSO–GWO Optimization for Short-Term SARS-CoV-2 Prediction in Indonesia Nastiti, Faulinda Ely; Musa, Shahrulniza; Riadi, Imam
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5485

Abstract

SARS-CoV-2 remains an endemic challenge in Indonesia, requiring reliable short-term forecasting tools that support informatics, digital epidemiology, and data-driven public health systems. Standard LSTM models, while widely used for epidemic forecasting, face notable limitations such as sensitivity to poor weight initialization, and reduced ability to capture interactions within heterogeneous high-dimensional data—resulting in inconsistent performance. This research introduces ADELMI (Adaptive Deep Learning Metaheuristic Intelligence), a unified hybrid forecasting framework specifically designed not only to enhance forecasting accuracy but also to overcome core weaknesses of traditional LSTM architectures when applied to complex epidemic datasets. ADELMI integrates Mutual Information and Pearson Correlation for dual feature selection with a hybrid Particle Swarm–Grey Wolf Optimization (PSO–GWO) approach for optimizing LSTM parameters. The dataset includes 657 daily observations and 82 epidemiological, vaccination, and meteorological variables sourced from the Ministry of Health and BMKG (2020–2021). Feature selection reduced the dataset to 20 relevant predictors for recovery and death and one dominant predictor for positive cases. The optimized 50-unit LSTM with early stopping achieved highly accurate 7-day forecasts, producing MAPE scores of 0.01% (positive cases), 1.44% (recoveries), and 3.00% (deaths) across 5-fold cross-validation. These results significantly outperform ARIMA, SIR, and baseline LSTM models. By unifying dual feature selection with hybrid PSO–GWO optimization, ADELMI improves LSTM stability, weight initialization, and multivariate interaction modeling, delivering more reliable forecasts across heterogeneous datasets. This advancement strengthens informatics through DL-metaheuristic multivariate epidemic modeling and enables proactive, adaptive surveillance against evolving threats such as influenza hybrids.
Co-Authors Ardiyanto, Marta Ely Nastiti, Faulinda Faulinda Ely Nastiti Imam Riadi Yafi, Eiad