International Journal on Information and Communication Technology (IJoICT) <p align="justify">International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.</p> <p align="justify">IJoICT includes novel ideas on ICT, state of the art technique implementations, and study cases on developing countries. Each article is featured with details of the proposed method including dataset and external link for program or codes. The journal is published for those who wish to share information about their research and innovations and for those who want to know the latest results in the field of Information and Communication Technology (ICT).&nbsp;&nbsp;The Journal is published by the <a href="" target="_blank" rel="noopener">School of Computing</a>, Telkom University, Bandung, Indonesia. Accepted paper will be immediately available online (open access) without any publication fee.</p> School of Computing, Telkom University en-US International Journal on Information and Communication Technology (IJoICT) 2356-5462 Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT. STL Decomposition and SARIMA Model: The Case for Estimating Value-at-Risk of Covid-19 Increment Rate in DKI Jakarta <p>In this research, we propose an extreme values measure, the Value-at-Risk (VaR) based Seasonal Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which is more sensitive to the seasonality of extreme value than the conventional VaR. We consider the problem of the seasonality and extreme value for increment rate of Covid-19 forecasting. For stakeholder, government and regulator, VaR estimation can be implemented to face the extreme wave of new positive Covid-19 in the future and minimize the losses that possibly affected in term of financial and human resources. Specifically, the estimation of VaR is developed with the difference lies on parameter estimators of STL and SARIMA model. The VaR has coverage probability as well as close 1-α. Thus, we propose to set α&nbsp;as parameter to estimate VaR. Consequently, the performance of VaR will depend not only on parameter model but also α. Our aim estimates VaR with minimum α&nbsp;based on correct VaR value. Numerical analysis is carried out to illustrate the estimative VaR.</p> Agnes Zahrani Copyright (c) 2021 Agnes Zahrani 2021-07-08 2021-07-08 7 2 1 10 10.34818/ijoict.v7i2.553