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Computer-Based Data Visualization Analysis for Simplifying Complex Information Salsabila Nasution; Fatwa Aulia; Saprina Putri Utama Ritonga; Anggi Jelita Sitepu; Supiyandi Supiyandi
Prosiding Seminar Nasional Ilmu Komputer, Sosial Sains, Teknik dan Multi-Disiplin Ilmu Vol. 1 (2025)
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/ikosstemi.v1.43

Abstract

This study aims to analyze global temperature data by employing computer visualization as a tool to simplify complex information. The dataset was obtained from Kaggle, specifically the Global Land Temperatures by City dataset, which contains monthly average temperature data from various cities worldwide. The methods applied include data preprocessing, descriptive statistical analysis, and data visualization using the Python programming language with the Pandas, Matplotlib, and Seaborn libraries. The visualization results reveal an upward trend in the global average temperature from 1900 to 2020, with an increase of approximately 1°C, indicating the occurrence of global warming. Computer visualization has proven to be effective in helping researchers and policymakers better understand temperature change patterns compared to numerical table-based analysis. Therefore, this study emphasizes that the application of computer visualization is an efficient solution for presenting and analyzing large-scale data, making it more interpretable.
Impact of Social Restrictions on ISPA Dynamics in Tasikmalaya City (2018-2023): A Counterfactual SIRS Model Analysis Anggi Jelita Sitepu; Putri Nabawy; M Hasan Wijaya
JITCoS : Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): Journal of Information Technology and Computer System
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.77

Abstract

The COVID-19 pandemic and the implementation of Non-Pharmaceutical Interventions (NPIs), such as Large-Scale Social Restrictions (PSBB), have drastically altered the transmission landscape of endemic respiratory diseases. This study aims to quantitatively evaluate the impact of social restrictions on the incidence of Acute Respiratory Infection (ISPA) in Tasikmalaya City and to elucidate the mechanisms driving the post-pandemic case resurgence. A dynamic SIRS (Susceptible-Infected-Recovered-Susceptible) mathematical model was constructed and calibrated using historical time-series data from 2018 to 2023, incorporating the biological factor of waning immunity. To measure policy effectiveness, a counterfactual analysis was performed by comparing the factual simulation (with interventions) against a hypothetical no-intervention scenario. The results demonstrate that the model achieved a high goodness-of-fit, accurately replicating the sharp decline in cases during the 2020-2021 restriction period and the significant "rebound" to pre-pandemic levels in 2023. The counterfactual analysis estimates that social restrictions prevented approximately 50,000 to 60,000 potential ISPA cases cumulatively over the two-year period. The study concludes that while NPIs were highly effective in suppressing transmission, the subsequent resurgence was a predictable mathematical consequence of "immunity debt" the accumulation of susceptible individuals due to prolonged lack of pathogen exposure. These findings underscore the necessity for anticipatory surveillance and targeted interventions during the transition from pandemic to endemic phases.