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All Journal International Journal of Quantitative Research and Modeling
Ai Musrifah
Informatics Engineering, Faculty of Engineering, Suryakancana University, Cianjur, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Environmental Science Physics

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Implemetation of The ARIMA Model For Forecasting COVID-19 in Indonesia Ai Musrifah; Fietri Setiawati Sulaeman
International Journal of Quantitative Research and Modeling Vol. 7 No. 1 (2026): International Journal of Quantitative Research and Modeling
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v7i1.1167

Abstract

Processing large-scale data is indispensable for today's information technology needs, because large-scale data processing can produce decisions that can be useful for formulating or planning strategies that produce knowledge can be used as decisions by top management in achieving organizational goals. As is the case in this new normal era where information about COVID-19 data is needed in the next few days. So this study was made to predict future COVID-19 data. By using methods in data science, large- scale data processing can facilitate the presentation of data to be understood, analyzed and viewed. This research is related to large- scale data processing by adjusting to current needs, namely COVID-19 data analysis based on time series analysis. The method used in this study is OSEMN. The steps carried out in this research are filtering the data, then visualizing the data, and finally forecasting from the existing data. This research was conducted with the aim of processing large-scale COVID-19 datasets into data that is easy to analyze and informative, visualizing COVID-19 datasets to make them easier to understand, and forecasting in the future. The dataset used was obtained from Kaggle entitled "COVID-19 data from John Hopkins University" uploaded by Anthony Goldbloom as Kaggle's CEO which contains confirmed data and death data from around the world. From the dataset, several countries in Southeast Asia, neighboring countries from Indonesia, and Indonesia were selected to explore. From the exploration results obtained various information from the data in the form of a DataFrame which is easy to analyze after Exploratory Data Analysis, various graphic plots that are easy to understand, and get forecasting results using ARIMA algorithm.
Co-Authors Fietri Setiawati Sulaeman