Pub Date : 2022-07-13DOI: 10.14710/medstat.15.1.36-47
D. A. Mahkya, K. Notodiputro, B. Sartono
Stock is an investment instrument that has risk in its management. One effort to minimize this risk is to model and make further forecasts of stock price movements. Time series data forecasting with autoregressive models is often found in several cases with the most popular approach being the ARIMA model. The tree-based method is one of the algorithms that can be used to forecast both in classification and regression. One ensemble approach to tree-based methods is Extra Trees. This study aims to forecast using the Extra Trees algorithm by evaluating forecasting accuracy with Rolling Forecast Origin on BRMS stock price data. Based on the results obtained, it is known that Extra Trees produces a fairly good accuracy for forecasting up to 6 days after training data with a MAPE of less than 0.1%.
{"title":"EXTRA TREES METHOD FOR STOCK PRICE FORECASTING WITH ROLLING ORIGIN ACCURACY EVALUATION","authors":"D. A. Mahkya, K. Notodiputro, B. Sartono","doi":"10.14710/medstat.15.1.36-47","DOIUrl":"https://doi.org/10.14710/medstat.15.1.36-47","url":null,"abstract":"Stock is an investment instrument that has risk in its management. One effort to minimize this risk is to model and make further forecasts of stock price movements. Time series data forecasting with autoregressive models is often found in several cases with the most popular approach being the ARIMA model. The tree-based method is one of the algorithms that can be used to forecast both in classification and regression. One ensemble approach to tree-based methods is Extra Trees. This study aims to forecast using the Extra Trees algorithm by evaluating forecasting accuracy with Rolling Forecast Origin on BRMS stock price data. Based on the results obtained, it is known that Extra Trees produces a fairly good accuracy for forecasting up to 6 days after training data with a MAPE of less than 0.1%.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42446034","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 : 2022-07-10DOI: 10.14710/medstat.15.1.48-59
A. Aswi, M. Tiro, S. Sudarmin, Sukarna Sukarna, S. Cramb
A number of previous studies on Covid-19 have used Bayesian spatial Conditional Autoregressive (CAR) models. However, basic CAR models are at risk of over-smoothing if adjacent areas genuinely differ in risk. More complex forms, such as localised CAR models, allow for sudden disparities, but have rarely been applied to modelling Covid-19, and never with covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases with and without covariates, examine the impact of covariates and spatial priors on the identified clusters and which factors affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with population density included fits the data better than a corresponding model without covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively.
{"title":"THE INTERPLAY BETWEEN CLUSTERS, COVARIATES, AND SPATIAL PRIORS IN SPATIAL MODELLING OF COVID-19 IN SOUTH SULAWESI PROVINCE, INDONESIA","authors":"A. Aswi, M. Tiro, S. Sudarmin, Sukarna Sukarna, S. Cramb","doi":"10.14710/medstat.15.1.48-59","DOIUrl":"https://doi.org/10.14710/medstat.15.1.48-59","url":null,"abstract":"A number of previous studies on Covid-19 have used Bayesian spatial Conditional Autoregressive (CAR) models. However, basic CAR models are at risk of over-smoothing if adjacent areas genuinely differ in risk. More complex forms, such as localised CAR models, allow for sudden disparities, but have rarely been applied to modelling Covid-19, and never with covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases with and without covariates, examine the impact of covariates and spatial priors on the identified clusters and which factors affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with population density included fits the data better than a corresponding model without covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45913787","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 : 2022-07-10DOI: 10.14710/medstat.15.1.60-71
Dadang Mohamad, Grida Saktian Laksito, S. Sukono
This study aims to determine how the influence of service quality and customer value on customer loyalty through customer satisfaction on DAMRI Transport Mode in Bandung, The research method used is quantitative, the sampling technique uses non-probability sampling and a sample of 260 respondents is obtained, the analytical tool used is Path Analysis and hypotheses using a significance test using the SPSS Version 24 and SEM AMOS analysis tool. The results of this study indicate that direct testing for direct testing of the customer loyalty variable it is found that service quality and customer satisfaction to customer loyalty has a positive and significant effect for use bus DAMRI in Bandung, while for customer value it has no effect on customer loyalty for use bus DAMRI in Bandung. With regard to customers' ownership, it is possible to increase the quality of service quality and customer loyalty to customers by giving goods a consumer satisfaction that would allow them to be loyal to using DAMRI bus as a mode of transportation in everyday activities.
本研究旨在通过客户满意度确定服务质量和客户价值对万隆DAMRI运输模式的客户忠诚度的影响，使用的研究方法是定量的，抽样技术采用非概率抽样，获得260名受访者的样本，使用的分析工具是路径分析和假设，使用SPSS Version 24和SEM AMOS分析工具进行显著性检验。本研究结果表明，直接测试对顾客忠诚变量的直接测试发现，服务质量和顾客满意度对万隆客车DAMRI的顾客忠诚有显著的正向影响，而顾客价值对万隆客车DAMRI的顾客忠诚没有影响。关于顾客的所有权，有可能提高服务质量的质量和顾客对顾客的忠诚度，给商品一个消费者满意，使他们在日常活动中忠诚地使用DAMRI巴士作为一种交通方式。
{"title":"EFFECT SERVICE QUALITY AND CUSTOMER VALUE TO CUSTOMER LOYALTY THROUGH CUSTOMER SATISFACTION USE OF DAMRI TRANSPORTATION MODE IN BANDUNG","authors":"Dadang Mohamad, Grida Saktian Laksito, S. Sukono","doi":"10.14710/medstat.15.1.60-71","DOIUrl":"https://doi.org/10.14710/medstat.15.1.60-71","url":null,"abstract":"This study aims to determine how the influence of service quality and customer value on customer loyalty through customer satisfaction on DAMRI Transport Mode in Bandung, The research method used is quantitative, the sampling technique uses non-probability sampling and a sample of 260 respondents is obtained, the analytical tool used is Path Analysis and hypotheses using a significance test using the SPSS Version 24 and SEM AMOS analysis tool. The results of this study indicate that direct testing for direct testing of the customer loyalty variable it is found that service quality and customer satisfaction to customer loyalty has a positive and significant effect for use bus DAMRI in Bandung, while for customer value it has no effect on customer loyalty for use bus DAMRI in Bandung. With regard to customers' ownership, it is possible to increase the quality of service quality and customer loyalty to customers by giving goods a consumer satisfaction that would allow them to be loyal to using DAMRI bus as a mode of transportation in everyday activities.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46383685","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 : 2022-07-05DOI: 10.14710/medstat.15.1.72-82
H. Yasin, B. Warsito, A. Hakim, Rahmasari Nur Azizah
The presence of outliers will affect the parameter estimation results and model accuracy. It also occurs in the spatial regression model, especially the Spatial Autoregressive (SAR) model. Spatial Autoregressive (SAR) is a regression model where spatial effects are attached to the dependent variable. Removing outliers in the analysis will eliminate the necessary information. Therefore, the solution offered is to modify the SAR model, especially by giving special treatment to observations that have potentially become outliers. This study develops to modeling the life expectancy data in Central Java Province using a modified spatial autoregressive model with the Mean-Shift Outlier Model (MSOM) approach. Outliers are detected using the MSOM method. Then the result is used as the basis for modifying the SAR model. This modification, in principle, will reduce or increase the average of the observed data indicated as outliers. The results show that the modified model can improve the model accuracy compared to the original SAR model. It can be proved by the increased coefficient of determination and decreasing the Akaike Information Criterion (AIC) value of the modified model. In addition, the modified model can improve the skewness and kurtosis values of the residuals getting closer to the Normal distribution.
{"title":"LIFE EXPECTANCY MODELING USING MODIFIED SPATIAL AUTOREGRESSIVE MODEL","authors":"H. Yasin, B. Warsito, A. Hakim, Rahmasari Nur Azizah","doi":"10.14710/medstat.15.1.72-82","DOIUrl":"https://doi.org/10.14710/medstat.15.1.72-82","url":null,"abstract":"The presence of outliers will affect the parameter estimation results and model accuracy. It also occurs in the spatial regression model, especially the Spatial Autoregressive (SAR) model. Spatial Autoregressive (SAR) is a regression model where spatial effects are attached to the dependent variable. Removing outliers in the analysis will eliminate the necessary information. Therefore, the solution offered is to modify the SAR model, especially by giving special treatment to observations that have potentially become outliers. This study develops to modeling the life expectancy data in Central Java Province using a modified spatial autoregressive model with the Mean-Shift Outlier Model (MSOM) approach. Outliers are detected using the MSOM method. Then the result is used as the basis for modifying the SAR model. This modification, in principle, will reduce or increase the average of the observed data indicated as outliers. The results show that the modified model can improve the model accuracy compared to the original SAR model. It can be proved by the increased coefficient of determination and decreasing the Akaike Information Criterion (AIC) value of the modified model. In addition, the modified model can improve the skewness and kurtosis values of the residuals getting closer to the Normal distribution.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44720002","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 : 2022-07-02DOI: 10.14710/medstat.15.1.104-115
S. Annas, R. Ruliana, W. Sanusi
Online teaching can be a solution in the learning process during the pandemic to stop the spreading of the Covid-19 infection. Universitas Negeri Makassar (UNM) as an educational institution provided a Learning Management System (LMS) to support the online teaching and learning process with the platform name SYAM-OK. In this research, we examine the behavioral model of a student's acceptance of the use of an information system SYAM-OK in online teaching. 120 students in the sample used online teaching fully during the pandemic. The data was obtained from an online questionnaire using a google form whose contents were based on Technology Acceptance Model (TAM). The variable of TAM consists of Perceived Ease of Use, Perceived Usefulness, Attitude Towards, Behavioral Intention, and Actual Use. The Structural Equation Modeling (SEM) PLS method was used in this research for modeling the relationship between TAM variables. Based on the results of the SEM we obtained that Perceived Usefulness significantly affects the Attitude Towards and Attitude Towards significantly affects the behavioral intention. By using the bootstrapping and T statistics, we conclude that SEM has identified the significant effects between variables of TAM.
{"title":"STRUCTURAL EQUATION MODELING FOR ANALYZING THE TECHNOLOGY ACCEPTANCE MODEL OF STUDENTS IN ONLINE TEACHING DURING THE COVID-19 PANDEMIC","authors":"S. Annas, R. Ruliana, W. Sanusi","doi":"10.14710/medstat.15.1.104-115","DOIUrl":"https://doi.org/10.14710/medstat.15.1.104-115","url":null,"abstract":"Online teaching can be a solution in the learning process during the pandemic to stop the spreading of the Covid-19 infection. Universitas Negeri Makassar (UNM) as an educational institution provided a Learning Management System (LMS) to support the online teaching and learning process with the platform name SYAM-OK. In this research, we examine the behavioral model of a student's acceptance of the use of an information system SYAM-OK in online teaching. 120 students in the sample used online teaching fully during the pandemic. The data was obtained from an online questionnaire using a google form whose contents were based on Technology Acceptance Model (TAM). The variable of TAM consists of Perceived Ease of Use, Perceived Usefulness, Attitude Towards, Behavioral Intention, and Actual Use. The Structural Equation Modeling (SEM) PLS method was used in this research for modeling the relationship between TAM variables. Based on the results of the SEM we obtained that Perceived Usefulness significantly affects the Attitude Towards and Attitude Towards significantly affects the behavioral intention. By using the bootstrapping and T statistics, we conclude that SEM has identified the significant effects between variables of TAM. ","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48520932","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 : 2022-06-15DOI: 10.14710/medstat.15.1.83-93
G. Darmawan, D. Rosadi, B. N. Ruchjana, R. Pontoh, Asrirawan Asrirawan, W. Setialaksana
In this study, Covid-19 modeling in Indonesia is carried out using a time series model. The time series model used is the time series model for discrete data. These models consist of Feedforward Neural Network (FFNN), Error, Trend, and Seasonal (ETS), Singular Spectrum Analysis (SSA), Fuzzy Time Series (FTS), Generalized Autoregression Moving Average (GARMA), and Bayesian Time Series. Based on the results of forecast accuracy calculation using MAPE (Mean Absolute Percentage Error) as model evaluation for confirmed data, the most accurate case models is the bayesian model of 0.04%, while all recovered cases yield MAPE 0.05%, except for FTS = 0.06%. For data for death cases SSA and Bayesian Models, the best with MAPE is 0.07%.
{"title":"FORECASTING COVID-19 IN INDONESIA WITH VARIOUS TIME SERIES MODELS","authors":"G. Darmawan, D. Rosadi, B. N. Ruchjana, R. Pontoh, Asrirawan Asrirawan, W. Setialaksana","doi":"10.14710/medstat.15.1.83-93","DOIUrl":"https://doi.org/10.14710/medstat.15.1.83-93","url":null,"abstract":"In this study, Covid-19 modeling in Indonesia is carried out using a time series model. The time series model used is the time series model for discrete data. These models consist of Feedforward Neural Network (FFNN), Error, Trend, and Seasonal (ETS), Singular Spectrum Analysis (SSA), Fuzzy Time Series (FTS), Generalized Autoregression Moving Average (GARMA), and Bayesian Time Series. Based on the results of forecast accuracy calculation using MAPE (Mean Absolute Percentage Error) as model evaluation for confirmed data, the most accurate case models is the bayesian model of 0.04%, while all recovered cases yield MAPE 0.05%, except for FTS = 0.06%. For data for death cases SSA and Bayesian Models, the best with MAPE is 0.07%.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42693002","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 : 2022-06-10DOI: 10.14710/medstat.15.1.24-35
Tiara Yulita, A. R. Effendie
Estimation of claims reserves is a very important role for insurance companies because the information will be used to assess the insurance company’s ability to meet future claim payment obligations. In practice, claims reserves are divided into two Incurred but Not Reported (IBNR) and Reported but Not Settled (RBNS). Reserving by Detailed Conditioning (RDC) is one of the individual methods that can estimate claims reserves of both the IBNR and RBNS, which involves detailed condition so-called claim characteristics, and some information else so-called background variable. The result of estimating claims reserves using RDC with background variable is not stable because many combinate of calculation from each background variable. The purpose of this study is to overcome these problems, which we can combine RDC and Gamma Generalized Linear Model (GLM) as an effective method for estimating claims reserves. By using Bootstrapping Individual Claims Histories (BICH) method, the results show that estimation of claims reserves using RDC and Gamma GLM gives the fewest value of Mean Square Error of Prediction (MSEP) rather than RDC with Poisson GLM, RDC, and Chain Ladder. Where the smaller the value of the resulting MSEP estimate, the closer to the actual claim reserve value.
{"title":"ESTIMATION OF IBNR AND RBNS RESERVES USING RDC METHOD AND GAMMA GENERALIZED LINEAR MODEL","authors":"Tiara Yulita, A. R. Effendie","doi":"10.14710/medstat.15.1.24-35","DOIUrl":"https://doi.org/10.14710/medstat.15.1.24-35","url":null,"abstract":"Estimation of claims reserves is a very important role for insurance companies because the information will be used to assess the insurance company’s ability to meet future claim payment obligations. In practice, claims reserves are divided into two Incurred but Not Reported (IBNR) and Reported but Not Settled (RBNS). Reserving by Detailed Conditioning (RDC) is one of the individual methods that can estimate claims reserves of both the IBNR and RBNS, which involves detailed condition so-called claim characteristics, and some information else so-called background variable. The result of estimating claims reserves using RDC with background variable is not stable because many combinate of calculation from each background variable. The purpose of this study is to overcome these problems, which we can combine RDC and Gamma Generalized Linear Model (GLM) as an effective method for estimating claims reserves. By using Bootstrapping Individual Claims Histories (BICH) method, the results show that estimation of claims reserves using RDC and Gamma GLM gives the fewest value of Mean Square Error of Prediction (MSEP) rather than RDC with Poisson GLM, RDC, and Chain Ladder. Where the smaller the value of the resulting MSEP estimate, the closer to the actual claim reserve value.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44921461","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 : 2022-06-10DOI: 10.14710/medstat.15.1.12-23
Hesikumalasari Hesikumalasari, I. Budiantara, V. Ratnasari, Khaerun Nisa'
The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables. Consequently, it can be approached by using semiparametric regression model. The predictor variable that has a linear relationship with the response variable can be approached by using linear parametric curve called parametric component. Meanwhile, the unknown relationship between the response variable with another predictor variable can be approached by using nonparametric curve called nonparametric component. If the predictor variable in nonparametric component is more than one, then it can be approached by using a different nonparametric curve named combined or mixed estimator. In this research, nonparametric component is approached using mixed estimator of multivariable linear truncated spline and multivariable kernel. The objective of this research is to estimate the model of semiparametric regression curve with mixed estimator of multivariable truncated spline and multivariable kernel. Estimation of this mixed model using ordinary least square method.
{"title":"ESTIMATION OF SEMIPARAMETRIC REGRESSION CURVE WITH MIXED ESTIMATOR OF MULTIVARIABLE LINEAR TRUNCATED SPLINE AND MULTIVARIABLE KERNEL","authors":"Hesikumalasari Hesikumalasari, I. Budiantara, V. Ratnasari, Khaerun Nisa'","doi":"10.14710/medstat.15.1.12-23","DOIUrl":"https://doi.org/10.14710/medstat.15.1.12-23","url":null,"abstract":"The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables. Consequently, it can be approached by using semiparametric regression model. The predictor variable that has a linear relationship with the response variable can be approached by using linear parametric curve called parametric component. Meanwhile, the unknown relationship between the response variable with another predictor variable can be approached by using nonparametric curve called nonparametric component. If the predictor variable in nonparametric component is more than one, then it can be approached by using a different nonparametric curve named combined or mixed estimator. In this research, nonparametric component is approached using mixed estimator of multivariable linear truncated spline and multivariable kernel. The objective of this research is to estimate the model of semiparametric regression curve with mixed estimator of multivariable truncated spline and multivariable kernel. Estimation of this mixed model using ordinary least square method.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47468258","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 : 2021-12-31DOI: 10.14710/medstat.14.2.125-136
T. Tarno, Trimono Trimono, D. A. I. Maruddani, Yuciana Wilandari, Rianti Siwi Utami
Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.
股票投资组合是一种可以用来将损失风险降至最低的投资形式。在股票投资组合中，风险价值（VaR）可以通过投资组合的回报来预测。如果投资组合收益方差是异方差的，则可以通过将VaR与ARIMA-GARCH或集成ARIMA-GRCH模型方法相结合来进行风险预测。此外，通过回溯检验检验了VaR的准确性。在本研究中，投资组合由PT Indofood CBP Sukses Makmur（ICBP.JK）和PT Indofeed Sukses Macrmur Tbk（INDF.JK）股票组成，时间为2018年1月1日至2021年7月30日。结果表明，最佳模型是集合ARMA-GARCH，MSE为1.3231×10-6。在95%的置信水平和1天的持有期内，集合ARMA-GARCH的VaR为-0.0213。基于回溯测试，由于违约率（VR）的值等于0，因此预测损失风险的值被证明是非常准确的。
{"title":"RISK ASSESSMENT OF STOCKS PORTFOLIO THROUGH ENSEMBLE ARMA-GARCH AND VALUE AT RISK (CASE STUDY: INDF.JK AND ICBP.JK STOCK PRICE)","authors":"T. Tarno, Trimono Trimono, D. A. I. Maruddani, Yuciana Wilandari, Rianti Siwi Utami","doi":"10.14710/medstat.14.2.125-136","DOIUrl":"https://doi.org/10.14710/medstat.14.2.125-136","url":null,"abstract":"Stocks portfolio is a form of investment that can be used to minimize the risk of loss. In a stock portfolio, the Value at Risk (VaR) can be predicted through the portfolio return. If portfolio return variance is heteroskedastic risk prediction can be done by using VaR with ARIMA-GARCH or Ensemble ARIMA-GARCH model approach. Furthermore, the accuracy of VaR is tested through Backtesting test. In this study, the portfolio is formed from PT Indofood CBP Sukses Makmur (ICBP.JK) and PT Indofood Sukses Makmur Tbk (INDF.JK) stocks from 01/01/2018 to 07/30/2021. The results showed that the best model is Ensemble ARMA-GARCH with MSE 1.3231×10-6. At confidence level of 95% and 1 day holding period, the VaR of the Ensemble ARMA-GARCH was -0.0213. Based on the Backtesting test, it is proven to be very accurate to predict the value of loss risk because the value of the Violation Ratio (VR) is equal to 0.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43929705","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 : 2021-12-30DOI: 10.14710/medstat.15.1.1-11
R. Boedi, R. Saputri
Dental age estimation is a branch of forensic odontology that plays a pivotal role in identifying, examining, or determining the legal status of the living and the dead. This research explores the capability of support vector regression to estimate chronological age from the third molar maturity index (I3M) in Indonesian Juveniles and compares the linear and kernel performance. Two hundred and twenty-two orthopantomo-graphy were measured using I3M in the lower left third molar and processed using R Studio with Caret extension. The analysis was separated into two groups, group 1 using only I3M as a predictor, and group 2 using both I3M and sex. Both groups were analyzed using SVR with the linear and polynomial kernel. The result suggests that using polynomial kernel SVR in group 1 produces the best results, with an R2 value of 0.64, RMSE of 1.588 years, and MAE of 1.25 years using degree = 3, c = 0.25. However, the addition of a sex predictor in the model reduces its accuracy when using the polynomial kernel.
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