Predictive Analytics on Time Series Data To Generate A Deterministic Decision Model: A Case Study on School Reopening Safely During The Pandemic

Feby Artwodini Muqtadiroh (), Diana Purwitasari, Muhammad Reza Pahlawan, Riris Diana Rachmayanti, Tsuyoshi Usagawa, Eko Mulyanto Yuniarno, Supeno M. S. Nugroho and Mauridhi Hery Purnomo
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Feby Artwodini Muqtadiroh: Department of Information Systems, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Diana Purwitasari: Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Muhammad Reza Pahlawan: Department of Accounting, Sekolah Tinggi Ilmu Ekonomi Indonesia, Surabaya 60118, Indonesia
Riris Diana Rachmayanti: Department of Health Promotion and Behavioral Sciences, Universitas Airlangga, Surabaya, Indonesia
Tsuyoshi Usagawa: Department of Computer Science and Electrical Engineering, Graduate School of Science and Technology, Kumamoto University, Japan
Eko Mulyanto Yuniarno: Department of Electrical Engineering and Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Supeno M. S. Nugroho: Department of Electrical Engineering and Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Mauridhi Hery Purnomo: Department of Electrical Engineering and Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia

International Journal of Information Technology & Decision Making (IJITDM), 2025, vol. 24, issue 06, 1685-1715

Abstract: This research focuses on developing a new decision-making model to evaluate school reopening strategies during the COVID-19 pandemic. The model integrates deep learning and factor analysis to address the urgent need to restart educational services without worsening the health crisis. It starts by gathering time series data from various districts to apply deep learning for predicting virus dynamics, emphasizing feature extraction and hyperparameter optimization. The subsequent phase involves factor analysis to discover key factors influencing virus spread, using outputs from the deep learning step. Based on these factors, clustering methods then sort districts into controllable or vulnerable groups. The final stage combines these analyzes into a deterministic decision model aiding policymakers in crafting school reopening guidelines. The model identifies three primary controllable factors: infection growth rate, reduction in active cases, and lowered mortality rates. Clustering then reveals that three groups are controllable, enabling specific interventions. This model is noteworthy for considering causal links between pandemic metrics and its adaptability to diverse datasets across districts/subdistricts, offering a scalable solution for decision-makers. The results highlight the importance of local infection trends and tailored data in shaping policies, showing that strong predictive analytics and insight into significant factors are crucial for developing effective, safe school reopening plans.

Keywords: Pandemic; decision-making; deep learning prediction model; clustering; factor analysis; school reopening (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0219622025500191

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