ABSTRACT
The COVID-19's rapidly spread in Indonesia is a serious concern for government. Various government policies, such as large-scale social restrictions, which is called as PSBB, and the imposing restrictions on community activities, be called as PPKM, have been applied to slow the spread of COVID-19. These policies were supposed to control people activities in many areas, especially that will invite the crowd happening. In this paper, the control charts be explored to obtain the overview of the daily new COVID-19 cases number, which is considered as a production process. The proportion of positive COVID-19 cases be observed and monitored using P-control chart. The data used is from the specimen COVID-19 test in DKI Jakarta Province, since April 2020 to January 2021. It is obtained that the process of daily new COVID-19 cases number has not been statistically controlled. It is detected some mean shifts of proportion in some point of times. Several findings indicate an upward trend, suggesting that the proportion of positive cases COVID-19 is rising. The public's conduct in Jakarta has served as a model for the government in designing policies to tackle the spread of Covid-19. However, due to the high mobility of Jakarta residents and its lack of consistency in the region, the COVID-19 process still remained out of control. © 2022 American Institute of Physics Inc.. All rights reserved.
ABSTRACT
One of the major telecommunication and network service providers in Indonesia is PT Indosat Tbk. During the coronavirus (COVID-19) pandemic, the daily stock price of that company was influenced by government policies. This study addresses stock data movement from February 5, 2020 to February 5, 2021, resulted in 243 data, using the Geometric Brownian motion (GBM). The stochastic process realization of this stock price fluctuates and increases exponentially, especially in the 40 latest data. Because of this situation, the realization is transformed into log 10 and calculated its return. As a result, weak stationary in variance is obtained. Furthermore, only data from December 7, 2020 to February 5, 2021 fulfill the GBM assumption of stock price return, as Rt1∗, t1∗ = 1, 2, 3, …, 40. The main idea of this study is adding datum one by one as much as 10% - 15% of the total data Rt1∗, starting from December 4, 2020 backwards. Following this procedure, and based on the 3% < p-value < 10%, the study shows that its datum can be included in Rt1∗, so t1∗ = −4. −3, −2, …, 40 and form five other data groups, Rt2∗, …, Rt6∗. Considering Mean Absolute Percentage Error (MAPE) and amount of data from each group, Rt6∗ is selected for modelling. Thus, GBM succeeded in representing the stock price movement of the second most popular Indonesian telecommunication company during COVID-19 pandemic. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.