Pub Date : 2022-11-13DOI: 10.30812/varian.v6i1.1993
Hendra H. Dukalang, Ingka Rizkyani Akolo, Muhammad Rezky Friesta Payu, Setiati Ningsih
Gorontalo city is the capital of Gorontalo province which has a high incidence of stunting. This high incidence rate needs to get attention because stunting can further become one of the indicators of the low quality of human resources in Gorontalo. One method that can be used to analyze the factors that cause stunting is the spatial regression method, namely Spatial Error Model (SEM). SEM model can analyze used R and GeoDa software. The purpose of this study is to find out the factors that affect stunting in Gorontalo City and compare the results of the Spatial Error Model analysis based on the results of R and GeoDa software. The results showed that there are two variables that have a significant effect on stunting incidence, namely the variable number of Complete Basic Immunization (IDL) and the amount of proper sanitation. The R and GeoDa software comparison results showed there were several similar outputs i.e. LM test output, parameter estimation and R-square value, while the different outputs were Moran's I test output, Breusch-Pagan test, and AIC value. Although Moran's I test output and Breusch-Pagan’s test are different, but they produce the same conclusion. The AIC value produced by GeoDa is smaller than R software.
{"title":"Comparison of R and GeoDa Software in Case of Stunting Using Spatial Error Model","authors":"Hendra H. Dukalang, Ingka Rizkyani Akolo, Muhammad Rezky Friesta Payu, Setiati Ningsih","doi":"10.30812/varian.v6i1.1993","DOIUrl":"https://doi.org/10.30812/varian.v6i1.1993","url":null,"abstract":"Gorontalo city is the capital of Gorontalo province which has a high incidence of stunting. This high incidence rate needs to get attention because stunting can further become one of the indicators of the low quality of human resources in Gorontalo. One method that can be used to analyze the factors that cause stunting is the spatial regression method, namely Spatial Error Model (SEM). SEM model can analyze used R and GeoDa software. The purpose of this study is to find out the factors that affect stunting in Gorontalo City and compare the results of the Spatial Error Model analysis based on the results of R and GeoDa software. The results showed that there are two variables that have a significant effect on stunting incidence, namely the variable number of Complete Basic Immunization (IDL) and the amount of proper sanitation. The R and GeoDa software comparison results showed there were several similar outputs i.e. LM test output, parameter estimation and R-square value, while the different outputs were Moran's I test output, Breusch-Pagan test, and AIC value. Although Moran's I test output and Breusch-Pagan’s test are different, but they produce the same conclusion. The AIC value produced by GeoDa is smaller than R software.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131750267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-13DOI: 10.30812/varian.v6i1.1973
N. Andini, S. I. Oktora
Tuberculosis is caused by Mycobacterium Tuberculosis (MT). MT usually attacks the lungs and causes pulmonary-tuberculosis. Tuberculosis cases in Indonesia keep increasing over the years. The presence of Multidrug-Resistant Tuberculosis (MDR-TB) has been one of the main obstacles in eradicating tuberculosis because it couldn’t be cured using standard drugs. In fact, the success rate of MDR-TB treatment in 2019 at the global level was only 57 percent. Research on MDR-TB can be related to the spatial aspect because this disease can be transmitted quickly. This study aims to obtain an overview and model the number of Indonesia’s pulmonary MDR-TB cases in 2019 using the Geographically Weighted Negative Binomial Regression (GWNBR) method. The independent variables used in the model are population density, percentage of poor population, health center ratio per 100 thousand population, the ratio of health workers per 10 thousand population, percentage of smokers, percentage of the region with PHBS policies, and percentage of BCG immunization coverage. The finding reveals that the model forms 12 regional groups based on significant variables where GWNBR gives better results compared to NBR. The significant spatial correlation implies that the collaboration among regional governments plays an important role in reducing the number of pulmonary MDR-TB.
{"title":"Determinants of Multidrug-Resistant Pulmonary Tuberculosis in Indonesia: A Spatial Analysis Perspective","authors":"N. Andini, S. I. Oktora","doi":"10.30812/varian.v6i1.1973","DOIUrl":"https://doi.org/10.30812/varian.v6i1.1973","url":null,"abstract":"Tuberculosis is caused by Mycobacterium Tuberculosis (MT). MT usually attacks the lungs and causes pulmonary-tuberculosis. Tuberculosis cases in Indonesia keep increasing over the years. The presence of Multidrug-Resistant Tuberculosis (MDR-TB) has been one of the main obstacles in eradicating tuberculosis because it couldn’t be cured using standard drugs. In fact, the success rate of MDR-TB treatment in 2019 at the global level was only 57 percent. Research on MDR-TB can be related to the spatial aspect because this disease can be transmitted quickly. This study aims to obtain an overview and model the number of Indonesia’s pulmonary MDR-TB cases in 2019 using the Geographically Weighted Negative Binomial Regression (GWNBR) method. The independent variables used in the model are population density, percentage of poor population, health center ratio per 100 thousand population, the ratio of health workers per 10 thousand population, percentage of smokers, percentage of the region with PHBS policies, and percentage of BCG immunization coverage. The finding reveals that the model forms 12 regional groups based on significant variables where GWNBR gives better results compared to NBR. The significant spatial correlation implies that the collaboration among regional governments plays an important role in reducing the number of pulmonary MDR-TB.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133138095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-13DOI: 10.30812/varian.v6i1.1847
B. H. S. Utami, Dwi Herinanto, M. Gumanti
This study aims to determine the estimation of interval-censored data with a special distribution, namely the binomial distribution. This research is using quantitative methods, the steps are estimating parameters on the interval-censored binomial distribution using the Maximum Likelihood Estimation method. The second step shows the properties of the estimator on the interval-censored binomial distribution. The last is to determine the parameter estimation of interval-censored data from the binomial distribution in survival analysis and provide an example of research containing interval-censored observations which will then be used as a case study. The results showed that the estimator is a sufficient statistic, meaning that it is unbiased. The case study was conducted using interval-censored data regarding the study of ninety-four breast cancer patients to see which group survived longer (survival value) of the two treatments, namely patients who underwent radiotherapy alone and patients who underwent radiotherapy followed by adjuvant chemotherapy.
{"title":"Characteristic Estimator of Interval-Censored Binomial Data and Its Application","authors":"B. H. S. Utami, Dwi Herinanto, M. Gumanti","doi":"10.30812/varian.v6i1.1847","DOIUrl":"https://doi.org/10.30812/varian.v6i1.1847","url":null,"abstract":"This study aims to determine the estimation of interval-censored data with a special distribution, namely the binomial distribution. This research is using quantitative methods, the steps are estimating parameters on the interval-censored binomial distribution using the Maximum Likelihood Estimation method. The second step shows the properties of the estimator on the interval-censored binomial distribution. The last is to determine the parameter estimation of interval-censored data from the binomial distribution in survival analysis and provide an example of research containing interval-censored observations which will then be used as a case study. The results showed that the estimator is a sufficient statistic, meaning that it is unbiased. The case study was conducted using interval-censored data regarding the study of ninety-four breast cancer patients to see which group survived longer (survival value) of the two treatments, namely patients who underwent radiotherapy alone and patients who underwent radiotherapy followed by adjuvant chemotherapy.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"1944 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130162171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-13DOI: 10.30812/varian.v6i1.2142
Andi Shahifah Muthahharah, M. Tiro, A. Aswi
Research on soft-clustering has not been explored much compared to hard-clustering. Soft-clustering algorithms are important in solving complex clustering problems. One of the soft-clustering methods is the Gaussian Mixture Model (GMM). GMM is a clustering method to classify data points into different clusters based on the Gaussian distribution. This study aims to determine the number of clusters formed by using the GMM method. The data used in this study is synthetic data on water quality indicators obtained from the Kaggle website. The stages of the GMM method are: imputing the Not Available (NA) value (if there is an NA value), checking the data distribution, conducting a normality test, and standardizing the data. The next step is to estimate the parameters with the Expectation Maximization (EM) algorithm. The best number of clusters is based on the biggest value of the Bayesian Information Creation (BIC). The results showed that the best number of clusters from synthetic data on water quality indicators was 3 clusters. Cluster 1 consisted of 1110 observations with low-quality category, cluster 2 consisted of 499 observations with medium quality category, and cluster 3 consisted of 1667 observations with high-quality category or acceptable. The results of this study recommend that the GMM method can be grouped correctly when the variables used are generally normally distributed. This method can be applied to real data, both in which the variables are normally distributed or which have a mixture of Gaussian and non-Gaussian.
{"title":"Application of Soft-Clustering Analysis Using Expectation Maximization Algorithms on Gaussian Mixture Model","authors":"Andi Shahifah Muthahharah, M. Tiro, A. Aswi","doi":"10.30812/varian.v6i1.2142","DOIUrl":"https://doi.org/10.30812/varian.v6i1.2142","url":null,"abstract":"Research on soft-clustering has not been explored much compared to hard-clustering. Soft-clustering algorithms are important in solving complex clustering problems. One of the soft-clustering methods is the Gaussian Mixture Model (GMM). GMM is a clustering method to classify data points into different clusters based on the Gaussian distribution. This study aims to determine the number of clusters formed by using the GMM method. The data used in this study is synthetic data on water quality indicators obtained from the Kaggle website. The stages of the GMM method are: imputing the Not Available (NA) value (if there is an NA value), checking the data distribution, conducting a normality test, and standardizing the data. The next step is to estimate the parameters with the Expectation Maximization (EM) algorithm. The best number of clusters is based on the biggest value of the Bayesian Information Creation (BIC). The results showed that the best number of clusters from synthetic data on water quality indicators was 3 clusters. Cluster 1 consisted of 1110 observations with low-quality category, cluster 2 consisted of 499 observations with medium quality category, and cluster 3 consisted of 1667 observations with high-quality category or acceptable. The results of this study recommend that the GMM method can be grouped correctly when the variables used are generally normally distributed. This method can be applied to real data, both in which the variables are normally distributed or which have a mixture of Gaussian and non-Gaussian.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126088422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-13DOI: 10.30812/varian.v6i1.1882
S. Side, Irwan Irwan, M. Rifandi, M. Pratama, R. Ruliana, N. Z. A. Hamid
The increasing number of cases and the development of new variants of the Covid-19 virus globally including the territory of Indonesia, especially in the province of South Sulawesi are increasingly worrying and need to be prevented. Therefore, this study aims to develop a SEIR model on the spread of Covid-19 with vaccination control, optimal control analysis, stability analysis and numerical simulation of the SEIR model on the spread of Covid-19 in South Sulawesi. This study uses the SEIR epidemic model to predict the spread of Covid-19 in South Sulawesi Province with parameters such as birth rate, cure rate, mortality rate, interaction rate and vaccination. The SEIR model was chosen because it is one of the basic methods in the epidemiological model. The method used to build the model is a time delay model by considering the vaccination factor as a model parameter, model analysis using the next generation matrix method to determine the basic reproduction number and stability of the Covid-19 distribution model in South Sulawesi. Numerical model simulation using secondary data on the number of Covid-19 cases in South Sulawesi starting in 2021 which was obtained from the South Sulawesi Provincial Health Office. The results obtained are model analysis provides evidence of the existence of optimal control in the model. Based on the results obtained, it can also be seen that vaccination greatly influences the spread of Covid-19 in South Sulawesi, so that awareness is needed for the people of South Sulawesi to follow the government's recommendation to vaccinate to prevent or reduce the rate of transmission of Covid-19 in South Sulawesi.
{"title":"Optimum Control of SEIR Model on COVID-19 Spread with Delay Time and Vaccination Effect in South Sulawesi Province","authors":"S. Side, Irwan Irwan, M. Rifandi, M. Pratama, R. Ruliana, N. Z. A. Hamid","doi":"10.30812/varian.v6i1.1882","DOIUrl":"https://doi.org/10.30812/varian.v6i1.1882","url":null,"abstract":"The increasing number of cases and the development of new variants of the Covid-19 virus globally including the territory of Indonesia, especially in the province of South Sulawesi are increasingly worrying and need to be prevented. Therefore, this study aims to develop a SEIR model on the spread of Covid-19 with vaccination control, optimal control analysis, stability analysis and numerical simulation of the SEIR model on the spread of Covid-19 in South Sulawesi. This study uses the SEIR epidemic model to predict the spread of Covid-19 in South Sulawesi Province with parameters such as birth rate, cure rate, mortality rate, interaction rate and vaccination. The SEIR model was chosen because it is one of the basic methods in the epidemiological model. The method used to build the model is a time delay model by considering the vaccination factor as a model parameter, model analysis using the next generation matrix method to determine the basic reproduction number and stability of the Covid-19 distribution model in South Sulawesi. Numerical model simulation using secondary data on the number of Covid-19 cases in South Sulawesi starting in 2021 which was obtained from the South Sulawesi Provincial Health Office. The results obtained are model analysis provides evidence of the existence of optimal control in the model. Based on the results obtained, it can also be seen that vaccination greatly influences the spread of Covid-19 in South Sulawesi, so that awareness is needed for the people of South Sulawesi to follow the government's recommendation to vaccinate to prevent or reduce the rate of transmission of Covid-19 in South Sulawesi.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114484754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-13DOI: 10.30812/varian.v6i1.1886
Wiwit Pura Nurmayanti, D. Ratnaningsih, Sausan Nisrina, Abdul Rahim, Muhammad Malthuf, Wirajaya Kusuma
In the current era of Big Data, getting data is no longer a difficult thing because they can access easily it via the internet, which is open access. A large amount of data can cause many problems in the data, such as data that deviates too far from the average (outliers). The method used to handle outlier data is DBSCAN which is density based clustering. The DBSCAN can be applied in various fields, one of which is the social sector, namely the participation of the JKN BPJS Health in West Nusa Tenggara. This study sees the distribution of BPJS Health participation groups, and to detect outliers so that objects with noise are not included in the cluster. The results of the study using the DBSCAN algorithm show that the optimal epsilon value is between 0.37 points by observing the knee of a curve. and MinPts 3, with the highest silhouette value of 0.2763. The highest JKN BPJS participants are in cluster 1 with 5 sub-districts, the second highest cluster is cluster 3 with 5 sub-districts, while the lowest cluster is cluster 2 with 93 sub-districts. The 13 sub-districts are not included in any group because they are noise data.
{"title":"Clustrering of BPJS National Health Insurance Participant Using DBSCAN Algorithm","authors":"Wiwit Pura Nurmayanti, D. Ratnaningsih, Sausan Nisrina, Abdul Rahim, Muhammad Malthuf, Wirajaya Kusuma","doi":"10.30812/varian.v6i1.1886","DOIUrl":"https://doi.org/10.30812/varian.v6i1.1886","url":null,"abstract":"In the current era of Big Data, getting data is no longer a difficult thing because they can access easily it via the internet, which is open access. A large amount of data can cause many problems in the data, such as data that deviates too far from the average (outliers). The method used to handle outlier data is DBSCAN which is density based clustering. The DBSCAN can be applied in various fields, one of which is the social sector, namely the participation of the JKN BPJS Health in West Nusa Tenggara. This study sees the distribution of BPJS Health participation groups, and to detect outliers so that objects with noise are not included in the cluster. The results of the study using the DBSCAN algorithm show that the optimal epsilon value is between 0.37 points by observing the knee of a curve. and MinPts 3, with the highest silhouette value of 0.2763. The highest JKN BPJS participants are in cluster 1 with 5 sub-districts, the second highest cluster is cluster 3 with 5 sub-districts, while the lowest cluster is cluster 2 with 93 sub-districts. The 13 sub-districts are not included in any group because they are noise data.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"24 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131325621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-13DOI: 10.30812/varian.v6i1.1975
Ismail Husein, Arya Impun Diapari Lubis
Stock is an investment in the capital market that is very promising for investors. Investors can also get high returns from the shares invested. However, this stock price is not always stable, it can go up and down drastically. The purpose of this study is to predict stock prices because they often experience instability. The method used in this research is using the Exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model with the Quasi Maximum Likelihood (QML) method. The result of this research is the implementation of this model. The EGARCH model used is the stock price index model that is formed, namely the autoregressive integrated moving average (ARIMA) (0, 1, 2) EGARCH (1.4). The conclusion from the results of the research that predictions using the ARIMA model (0, 1, 2) EGARCH (1, 4) is the best model in accommodating the asymmetric nature of the volatility of the stock price index. The results of this egarch model show more optimal prediction results seen from an error of 3% compared to other modes such as the arch model and the GARCH model.
{"title":"Egarch Model Prediction for Sale Stock Price","authors":"Ismail Husein, Arya Impun Diapari Lubis","doi":"10.30812/varian.v6i1.1975","DOIUrl":"https://doi.org/10.30812/varian.v6i1.1975","url":null,"abstract":"Stock is an investment in the capital market that is very promising for investors. Investors can also get high returns from the shares invested. However, this stock price is not always stable, it can go up and down drastically. The purpose of this study is to predict stock prices because they often experience instability. The method used in this research is using the Exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model with the Quasi Maximum Likelihood (QML) method. The result of this research is the implementation of this model. The EGARCH model used is the stock price index model that is formed, namely the autoregressive integrated moving average (ARIMA) (0, 1, 2) EGARCH (1.4). The conclusion from the results of the research that predictions using the ARIMA model (0, 1, 2) EGARCH (1, 4) is the best model in accommodating the asymmetric nature of the volatility of the stock price index. The results of this egarch model show more optimal prediction results seen from an error of 3% compared to other modes such as the arch model and the GARCH model.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116576718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.30812/varian.v5i2.1908
S. Annas, I. Irwan, R. Safei, Z. Rais
Natural disasters that had occurred in Indonesia consist of hydro-meteorology: floods, droughts, and landslides, geophysical: volcanic earthquakes and volcanic eruptions, and biological: epidemics. Regarding the tectonic earthquake on Sulawesi Island, there are at least 2 earthquake disasters that became national disasters, namely in Central Sulawesi and West Sulawesi in the range of 2017 to 2021. This study aims to cluster tectonic earthquakes on Sulawesi Island, from 2017 to 2020, as the basis for formulating disaster mitigation plans. This study used tectonic earthquake data from 2017 to 2020 obtained from BMKG Gowa, Indonesia. The variables used are magnitude, depth, and distance category. Because they are mixed variables, this study used a k-prototype algorithm. There are four clusters in 2017, six clusters in 2018, five clusters in 2019, and six clusters in 2020 based on the ratio of within-cluster distance against between-cluster distance. It can be related to the active fault on Sulawesi Island. The characteristics of clusters form each year are the greater magnitude of the earthquake, the deeper of deep and the category distance is dominated by the regional level.
{"title":"K-Prototypes Algorithm for Clustering The Tectonic Earthquake in Sulawesi Island","authors":"S. Annas, I. Irwan, R. Safei, Z. Rais","doi":"10.30812/varian.v5i2.1908","DOIUrl":"https://doi.org/10.30812/varian.v5i2.1908","url":null,"abstract":"Natural disasters that had occurred in Indonesia consist of hydro-meteorology: floods, droughts, and landslides, geophysical: volcanic earthquakes and volcanic eruptions, and biological: epidemics. Regarding the tectonic earthquake on Sulawesi Island, there are at least 2 earthquake disasters that became national disasters, namely in Central Sulawesi and West Sulawesi in the range of 2017 to 2021. This study aims to cluster tectonic earthquakes on Sulawesi Island, from 2017 to 2020, as the basis for formulating disaster mitigation plans. This study used tectonic earthquake data from 2017 to 2020 obtained from BMKG Gowa, Indonesia. The variables used are magnitude, depth, and distance category. Because they are mixed variables, this study used a k-prototype algorithm. There are four clusters in 2017, six clusters in 2018, five clusters in 2019, and six clusters in 2020 based on the ratio of within-cluster distance against between-cluster distance. It can be related to the active fault on Sulawesi Island. The characteristics of clusters form each year are the greater magnitude of the earthquake, the deeper of deep and the category distance is dominated by the regional level.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130505250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-01DOI: 10.30812/varian.v5i2.1895
Citra Imama, M. Adriansyah, Hadi Prayogi, Ferdiana Friska Rahmana Putri, Naufal Ramadhan Al Akhwal Siregar, Alfredi Yoani, F. Mardianto
Until now, Coronavirus disease (COVID-19) has become a concern for Indonesia because of its significant development and impact on various sectors of life and hampering the target of achieving Sustainable Development Goals (SDGs). The achievements targeted in the SDGs, such as reducing poverty, hunger, and many more are very difficult to realize in the current pandemic conditions. The uncertain conditions of the pandemic made the government need some new ideas for consideration in creating policies to encourage sustainable development in this situation. This article covers modeling the effect of achieving the second dose of vaccination and the total cases of COVID-19 cases, which are often considered the reason for general negligence in complying with health protocols, especially wearing masks. This research was conducted using spline nonparametric regression because of its flexibility to handle uncertain data patterns. The results of this study are truncated spline nonparametric regression with 3 knots that produce a R-sq equal to 69.952%. Based on the results, the second dose vaccination coverage variables and the total COVID-19 cases together affect mask compliance. This result is expected to be a benchmark for the government to handle COVID-19 and efforts to achieve the SDGs.
{"title":"Mask Compliance Modeling Related COVID-19 in Indonesia Using Spline Nonparametric Regression","authors":"Citra Imama, M. Adriansyah, Hadi Prayogi, Ferdiana Friska Rahmana Putri, Naufal Ramadhan Al Akhwal Siregar, Alfredi Yoani, F. Mardianto","doi":"10.30812/varian.v5i2.1895","DOIUrl":"https://doi.org/10.30812/varian.v5i2.1895","url":null,"abstract":"Until now, Coronavirus disease (COVID-19) has become a concern for Indonesia because of its significant development and impact on various sectors of life and hampering the target of achieving Sustainable Development Goals (SDGs). The achievements targeted in the SDGs, such as reducing poverty, hunger, and many more are very difficult to realize in the current pandemic conditions. The uncertain conditions of the pandemic made the government need some new ideas for consideration in creating policies to encourage sustainable development in this situation. This article covers modeling the effect of achieving the second dose of vaccination and the total cases of COVID-19 cases, which are often considered the reason for general negligence in complying with health protocols, especially wearing masks. This research was conducted using spline nonparametric regression because of its flexibility to handle uncertain data patterns. The results of this study are truncated spline nonparametric regression with 3 knots that produce a R-sq equal to 69.952%. Based on the results, the second dose vaccination coverage variables and the total COVID-19 cases together affect mask compliance. This result is expected to be a benchmark for the government to handle COVID-19 and efforts to achieve the SDGs.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128124276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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