Application of the Ordinary Kriging method for prediction of the positive spread of Covid-19 in West Java

The Ordinary Kriging method is a method used to predict an observation at an unobserved location based on observed points that are spatially related. Corona Virus Disease 19 (Covid-19) is a contagious disease and viral pathogenic infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2). This virus spreads almost all over the world including Indonesia and it gives an influence between location. In this study, a case study was carried out with predictions on the positive distribution data of Covid-19 at 27 districts/cities in West Java. The process of calculating predictions of the positive distribution Covid-19 is started by the determination of the experimental semivariogram model. The Covid-19 data have an Exponential Semivariogram model which is used as an input of the Ordinary Kriging. Furthermore, using the R program for the Ordinary Kriging, we can predict the observation of positive Covid-19 at unobserved locations in West Java. © 2021 Institute of Physics Publishing. All rights reserved.

Perception analysis of the Indonesian society on twitter social media on the increase in BPJS kesehatan contribution in the Covid 19 pandemic era

In the conditions of the Covid-19 Pandemic, the president of Indonesia, Ir Joko Widodo, on May 13, 2020, announced an increase in BPJS Kesehatan contribution. This announcement made everyone in the community boisterous, including Indonesians on social media twitter. Currently, Indonesia is ranked fifth in the world using social media twitter, so data mining on Twitter is a good opportunity to see the public's response to a developing issue. This paper will discuss perceptions analysis of the Indonesian people on social media regarding the issue of increasing the contribution of BPJS Kesehatan. The issue of increasing the contribution of BPJS Kesehatan has been a topic of conversation on Twitter for a long time. After 30 days of data crawling, 145,359 tweets were obtained. This amount of data proves that the Indonesian people are very active in issuing opinions regarding the issue of increasing contribution BPJS Kesehatan. Various kinds of differences of opinion in each conversation are classified into three types of opinion using the Naive Bayes method. The three types of opinions are classified into positive opinions, negative opinions, and neutral opinions. The opinion classification results obtained were 73% containing negative opinions, 18% positive opinions, and 9% neutral opinions. This can serve as an early warning for the government to see the public's response in every policy taken. So that each policy can be evaluated for the better. © 2021 Institute of Physics Publishing. All rights reserved.

Comparison multi-layer perceptron and linear regression for time series prediction of novel coronavirus covid-19 data in West Java

Until now, the pandemic conditions of Covid-19 are still ravaging the world, even in Indonesia and West Java. Various attempts have been made to stop it. West Java implements Large Scale Social Restrictions, is known as Pembatasan Sosial Skala Besar (PSBB). However, over time, a discourse emerged to loosen PSBB. One of the World Health Organization's (WHO) requirements to loosen is the effective reproduction rate of Corona Virus cases below 1. Therefore, this study focuses on predicting the number of cases in West Java. The methods based on multi-layer perception (MLP) and linear regression (LR). The data were obtained from the C Covid -19 positive case from March to mid-August 2020 in West Java. The experiments show that MLP reaches optimal if it used 13 hidden layers with learning rate and momentum = 0.1. The MLP had a smaller error than LR. Both of them predict the number of cases in the next 30 days from August 14, 2020. The results show that West Java will still have an increase in the number of new cases of Covid -19. © 2021 Institute of Physics Publishing. All rights reserved.

Posted prediction in social media base on Markov chain model: twitter dataset with covid-19 trends

The influence of social media is very attractive in disseminating information;even social media analysis is one of the focuses in the field of research in terms of data mining. In its development not only the field of social science that exists but many studies of social media that can be solved stochastically to calculate the trend of the emergence of a discussion on social media. In this paper, we investigated calculations and predictions using Markov Chains on the emergence of discussions on Twitter media related to coronavirus disease tweets or better known as covid-19. The tweet data obtained is a random sample of the tweet posts that are crawled at the specified time. The tweet data is crawled at three different observations each day for thirteen days continuously. The results of data crawling are calculated to determine the transition from one observation to the next observation. The stages of the process are;crawling tweet data with keywords coronavirus and covid-19;data cleaning process;data processing;Markov Chain modeling;n-step distribution and long-term prediction;interpretation of results. The computational results used are opportunity distribution conditions for the number of tweets. As a transition between two states, namely low (0) and high (1) relative to mean or median. The results of the opportunity distribution obtained in the next 145-time steps (0.28767, 0.71233) and (0.47368, 0.52632) in the probability distribution of the number of tweets are respectively the mean and median values. The results of the modeling show that the conversation on Twitter for 145-time steps in the next prediction is estimated to remain high along with the outbreak of coronavirus or covid-19 before this epidemic subsides. © 2021 Institute of Physics Publishing. All rights reserved.