Pub Date : 2022-08-31DOI: 10.24843/mtk.2022.v11.i03.p377
Nindyna Puspasari, NI Luh Putu Suciptawati, M. Susilawati
Covid-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2. The transmission of Covid-19 has negative impact on every aspect. This study aimed to determine the factors that significantly affect the number of Covid-19 cases in Indonesia. Spatial regression analysis was used as the research method. The results obtained that on the dependent variable there is a spatial dependence, so the selected model is Spatial Autoregressive Model (SAR) with an AIC value of 759.09 and an value of 58.49%. The significant influencing factor is proportion of the population over 50 years old and open unemployment rate.
{"title":"METODE ANALISIS REGRESI SPASIAL DALAM MEMODELKAN KASUS COVID-19 DI INDONESIA","authors":"Nindyna Puspasari, NI Luh Putu Suciptawati, M. Susilawati","doi":"10.24843/mtk.2022.v11.i03.p377","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i03.p377","url":null,"abstract":"Covid-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2. The transmission of Covid-19 has negative impact on every aspect. This study aimed to determine the factors that significantly affect the number of Covid-19 cases in Indonesia. Spatial regression analysis was used as the research method. The results obtained that on the dependent variable there is a spatial dependence, so the selected model is Spatial Autoregressive Model (SAR) with an AIC value of 759.09 and an value of 58.49%. The significant influencing factor is proportion of the population over 50 years old and open unemployment rate.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46422337","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 : 2022-08-31DOI: 10.24843/mtk.2022.v11.i03.p381
Nabilatul Jannah, K. Dharmawan, L. Harini
Investment is buying an asset that is expected in the future can be resold and get a high profit value. There are two factors that must be considered when you want to invest in stocks, namely the rate of return on stocks and risk factors. By forming a portfolio is expected to minimize a risk. Value at Risk (VaR) is a form of measurement of risk when making investments. In this study VaR will be calculated using the Monte Carlo Simulation method and the Historical method. This study aims to find out var portfolio estimates involving JCI and DJIA stock indices from two different countries as well as to find out the differences between VaR using Historical and VaR using Monte Carlo Simulations. From the stock index data obtained further determined the value of the parameters, namely the expected return and standard deviation values used to calculate the value of the VaR Portfolio, while the confidence increase used in this study was 99% and with a period of 1 or the next day. Based on the results of the VaR value study using the Monte Carlo Simulation method and the Historical method, the Historical method obtained results of 3,650,506 and 1,029,103. The results showed that VaR values using the Monte Carlo Simulation method got greater results than using the Historical method, because the Monte Carlo Simulation performed repeated iterations by including random number generators.
{"title":"PENGGUNAAN SIMULASI MONTE CARLO DALAM ESTIMASI VALUE AT RISK (VaR) PORTOFOLIO YANG DIBENTUK DARI INDEKS HARGA SAHAM MULTINASIONAL","authors":"Nabilatul Jannah, K. Dharmawan, L. Harini","doi":"10.24843/mtk.2022.v11.i03.p381","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i03.p381","url":null,"abstract":"Investment is buying an asset that is expected in the future can be resold and get a high profit value. There are two factors that must be considered when you want to invest in stocks, namely the rate of return on stocks and risk factors. By forming a portfolio is expected to minimize a risk. Value at Risk (VaR) is a form of measurement of risk when making investments. In this study VaR will be calculated using the Monte Carlo Simulation method and the Historical method. This study aims to find out var portfolio estimates involving JCI and DJIA stock indices from two different countries as well as to find out the differences between VaR using Historical and VaR using Monte Carlo Simulations. From the stock index data obtained further determined the value of the parameters, namely the expected return and standard deviation values used to calculate the value of the VaR Portfolio, while the confidence increase used in this study was 99% and with a period of 1 or the next day. Based on the results of the VaR value study using the Monte Carlo Simulation method and the Historical method, the Historical method obtained results of 3,650,506 and 1,029,103. The results showed that VaR values using the Monte Carlo Simulation method got greater results than using the Historical method, because the Monte Carlo Simulation performed repeated iterations by including random number generators.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42601861","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 : 2022-08-31DOI: 10.24843/mtk.2022.v11.i03.p376
Gusti Ayu Made Candra Rini, NI Luh Putu Suciptawati, I. A. A. Utari
Inequality in income distribution is one of the problems that are often experienced by some countries in the world. Income inequality in Indonesia is measured by an indicator named Gini Ratio. BPS Indonesia noted that in March 2021, the Gini Ratio in Indonesia was 0,384. This figure shows that Indonesia belongs to the category of moderate income inequality, which means that income in Indonesia is not well distributed or there is an inequality in income distribution. For this reason, the inequality that occurs needs to be decreased by recognizing the factors that affect it. The purpose of this study was to determine the factors that significantly affect the Indonesia’s Gini Ratio in 2016-2020 by applying panel data regression. The results show that the model chosen to represent the Indonesia’s Gini Ratio in 2016-2020 is a fixed time effect model with of 40,282%, which is significantly be affected by the human development index, population, open unemployment rate, percentage of poor people, and average hourly wage for worker.
{"title":"IDENTIFIKASI FAKTOR YANG MEMENGARUHI GINI RATIO DI INDONESIA","authors":"Gusti Ayu Made Candra Rini, NI Luh Putu Suciptawati, I. A. A. Utari","doi":"10.24843/mtk.2022.v11.i03.p376","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i03.p376","url":null,"abstract":"Inequality in income distribution is one of the problems that are often experienced by some countries in the world. Income inequality in Indonesia is measured by an indicator named Gini Ratio. BPS Indonesia noted that in March 2021, the Gini Ratio in Indonesia was 0,384. This figure shows that Indonesia belongs to the category of moderate income inequality, which means that income in Indonesia is not well distributed or there is an inequality in income distribution. For this reason, the inequality that occurs needs to be decreased by recognizing the factors that affect it. The purpose of this study was to determine the factors that significantly affect the Indonesia’s Gini Ratio in 2016-2020 by applying panel data regression. The results show that the model chosen to represent the Indonesia’s Gini Ratio in 2016-2020 is a fixed time effect model with of 40,282%, which is significantly be affected by the human development index, population, open unemployment rate, percentage of poor people, and average hourly wage for worker.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49604937","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 : 2022-08-31DOI: 10.24843/mtk.2022.v11.i03.p375
Putri Nur Prasetia, Anita Triska, Julita Nahar
Rice is one of the most important commodities in Indonesia since it is one of the staple foods.Therefore, it becomes one of Indonesian government concerns by setting a goal of 46,8 million tons of rice supply in 2024. Despite 29,67% of the population earns their living from agriculture, forestry, and fisheries, the domestic production of rice could not meet its demand many times. Hence, the forecasting of the production and domestic consumption of rice is needed to know whether the domestic production is able to meet the demand. In this study, the rice production and domestic consumption were forecasted using the Double Exponential Smoothing (DES) method. The DES was chosen due to the pattern of the data shows the trends without seasonality. The accuracy of the forecasting was measured by Mean Absolute Percentage Error (MAPE) and Durbin-Watson statistic test. The yielded forecasts showed that the production rate is lower than the domestic consumption’s so that it would not meet the demand. It was concluded that the DES suitable to be used to forecast production and domestic consumption of rice in Indonesia since its MAPE are 6,48% and 5,91%, respectively. Moreover, the Durbin-Watson statistic showed that there was no autocorrelations on the errors of both data.
{"title":"PENERAPAN METODE DOUBLE EXPONENTIAL SMOOTHING UNTUK MERAMALKAN PRODUKSI DAN KONSUMSI DOMESTIK BERAS DI INDONESIA","authors":"Putri Nur Prasetia, Anita Triska, Julita Nahar","doi":"10.24843/mtk.2022.v11.i03.p375","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i03.p375","url":null,"abstract":"Rice is one of the most important commodities in Indonesia since it is one of the staple foods.Therefore, it becomes one of Indonesian government concerns by setting a goal of 46,8 million tons of rice supply in 2024. Despite 29,67% of the population earns their living from agriculture, forestry, and fisheries, the domestic production of rice could not meet its demand many times. Hence, the forecasting of the production and domestic consumption of rice is needed to know whether the domestic production is able to meet the demand. In this study, the rice production and domestic consumption were forecasted using the Double Exponential Smoothing (DES) method. The DES was chosen due to the pattern of the data shows the trends without seasonality. The accuracy of the forecasting was measured by Mean Absolute Percentage Error (MAPE) and Durbin-Watson statistic test. The yielded forecasts showed that the production rate is lower than the domestic consumption’s so that it would not meet the demand. It was concluded that the DES suitable to be used to forecast production and domestic consumption of rice in Indonesia since its MAPE are 6,48% and 5,91%, respectively. Moreover, the Durbin-Watson statistic showed that there was no autocorrelations on the errors of both data.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42352551","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 : 2022-08-31DOI: 10.24843/mtk.2022.v11.i03.p380
I. Dwiguna, G. Gandhiadi, L. Harini
This research is aimed to determine conduct clustering in accordance with the conditions of districts / cities throughout Indonesia based on the IPM indicator and to determine the performance comparison of Fuzzy C-Means using particle swarm optimization compared to ordinary fuzzy c mean. The study uses 514 district / city data in Indonesia based on four IPM indicators. The research show 4 clusters that describe the condition of the Indonesian region and based on the results of cluster validation shows that there are differences in the ordinary Fuzzy C-Means mean algorithm and Fuzzy C-Means using particle swarm optimization.
{"title":"IMPLEMENTASI FUZZY C-MEAN DAN ALGORITMA PARTICLE SWARM OPTIMIZATION UNTUK CLUSTERING KABUPATEN/KOTA DI INDONESIA BERDASARKAN INDIKATOR INDEKS PEMBANGUNAN MANUSIA","authors":"I. Dwiguna, G. Gandhiadi, L. Harini","doi":"10.24843/mtk.2022.v11.i03.p380","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i03.p380","url":null,"abstract":"This research is aimed to determine conduct clustering in accordance with the conditions of districts / cities throughout Indonesia based on the IPM indicator and to determine the performance comparison of Fuzzy C-Means using particle swarm optimization compared to ordinary fuzzy c mean. The study uses 514 district / city data in Indonesia based on four IPM indicators. The research show 4 clusters that describe the condition of the Indonesian region and based on the results of cluster validation shows that there are differences in the ordinary Fuzzy C-Means mean algorithm and Fuzzy C-Means using particle swarm optimization.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41376156","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 : 2022-06-05DOI: 10.24843/mtk.2022.v11.i02.p373
Muhamad Rifai, I. P. E. N. Kencana, Desak Putu Eka Nilakusmawati
Colleges as service providers must provide satisfaction to their students. At the concept of service, students as a group of consumers should get optimum service. The aim of this research is to determine the effect of the quality of academic services on student satisfaction at the Faculty of Mathematics and Natural Sciences, Udayana University during the Covid-19 pandemic. The analysis technique uses Partial Least Square Structural Equation Modeling (PLS-SEM). The results show that the dimensions of tangibles and empathy are proven to be significant, while dimensions of reliability, responsiveness, and assurance is not proven to significantly affect the quality of academic services. Increased the quality of academic services has proven to have a positive and significant effect on student satisfaction of FMIPA UNUD.
{"title":"APAKAH MUTU LAYANAN AKADEMIK MEMENGARUHI KEPUASAN MAHASISWA FMIPA UNUD BELAJAR DI MASA PANDEMI","authors":"Muhamad Rifai, I. P. E. N. Kencana, Desak Putu Eka Nilakusmawati","doi":"10.24843/mtk.2022.v11.i02.p373","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i02.p373","url":null,"abstract":"Colleges as service providers must provide satisfaction to their students. At the concept of service, students as a group of consumers should get optimum service. The aim of this research is to determine the effect of the quality of academic services on student satisfaction at the Faculty of Mathematics and Natural Sciences, Udayana University during the Covid-19 pandemic. The analysis technique uses Partial Least Square Structural Equation Modeling (PLS-SEM). The results show that the dimensions of tangibles and empathy are proven to be significant, while dimensions of reliability, responsiveness, and assurance is not proven to significantly affect the quality of academic services. Increased the quality of academic services has proven to have a positive and significant effect on student satisfaction of FMIPA UNUD.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43611760","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 : 2022-05-31DOI: 10.24843/mtk.2022.v11.i02.p366
Soraya Sarah Afifah, K. Dharmawan, I. G. A. M. Srinadi
Credit risk is a risk that is often encountered by banks in lending, especially mortgages. Banks can get losses if the risk is not anticipated properly. The purpose of this study is to estimate the number of losses (expected loss and unexpected loss) obtained by Bank XYZ due to default debtors and to estimate the amount of economic capital that must be provided by Bank XYZ in anticipating unexpected losses. The study was conducted using the CreditRisk+ method with a Poisson distribution approach. The ratio between expected loss and unexpected loss obtained from the calculation results is 57%. With the value of economic capital that needs to be provided by Bank XYZ is Rp. 647.594.176.768,-. This means that Bank XYZ needs to monitor the outstanding credit of their debtors who experience default in the credit portfolio in order to avoid possible losses and provide economic capital to cover these losses. So that the estimated value of economic capital can be used as a capital benchmark to anticipate maximum losses and as an indicator for Bank XYZ to earn income from credit activities.
{"title":"PERHITUNGAN RISIKO KREDIT KPR PADA BANK XYZ MENGGUNAKAN METODE CREDITRISK+","authors":"Soraya Sarah Afifah, K. Dharmawan, I. G. A. M. Srinadi","doi":"10.24843/mtk.2022.v11.i02.p366","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i02.p366","url":null,"abstract":"Credit risk is a risk that is often encountered by banks in lending, especially mortgages. Banks can get losses if the risk is not anticipated properly. The purpose of this study is to estimate the number of losses (expected loss and unexpected loss) obtained by Bank XYZ due to default debtors and to estimate the amount of economic capital that must be provided by Bank XYZ in anticipating unexpected losses. The study was conducted using the CreditRisk+ method with a Poisson distribution approach. The ratio between expected loss and unexpected loss obtained from the calculation results is 57%. With the value of economic capital that needs to be provided by Bank XYZ is Rp. 647.594.176.768,-. This means that Bank XYZ needs to monitor the outstanding credit of their debtors who experience default in the credit portfolio in order to avoid possible losses and provide economic capital to cover these losses. So that the estimated value of economic capital can be used as a capital benchmark to anticipate maximum losses and as an indicator for Bank XYZ to earn income from credit activities.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42201093","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 : 2022-05-31DOI: 10.24843/mtk.2022.v11.i02.p370
Yohana Th.V. Seran, K. Dharmawan, Ni Ketut Tari Tastrawati
The stock portfolio is a combination of several stocks that can help reduce investment risk. Risk can be measured using Value at Risk. This study aims to form an optimal portfolio in which stock risk is estimated using VaR with Generalized Extreme Value distribution followed by selecting the optimal portfolio forming stock using the Lexicographic Goal Programming method. The result of this research is that a portfolio with three selected stocks is formed, namely BBRI with a proportion of 63%, KLBF with a proportion of 25% and MNCN with a proportion of 12%. From the optimal portfolio formed, the expected return is 0.00005106 and the risk is 0.0187.
{"title":"ANALISIS PORTOFOLIO OPTIMAL MENGGUNAKAN METODE LEXICOGRAPHIC GOAL PROGRAMMING DENGAN PENDEKATAN VaR – GEV","authors":"Yohana Th.V. Seran, K. Dharmawan, Ni Ketut Tari Tastrawati","doi":"10.24843/mtk.2022.v11.i02.p370","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i02.p370","url":null,"abstract":"The stock portfolio is a combination of several stocks that can help reduce investment risk. Risk can be measured using Value at Risk. This study aims to form an optimal portfolio in which stock risk is estimated using VaR with Generalized Extreme Value distribution followed by selecting the optimal portfolio forming stock using the Lexicographic Goal Programming method. The result of this research is that a portfolio with three selected stocks is formed, namely BBRI with a proportion of 63%, KLBF with a proportion of 25% and MNCN with a proportion of 12%. From the optimal portfolio formed, the expected return is 0.00005106 and the risk is 0.0187.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46590391","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 : 2022-05-31DOI: 10.24843/mtk.2022.v11.i02.p372
D. Maulana, K. Dharmawan, I. G. A. M. Srinadi
Risk measure using Conditional Value at Risk can be calculate if values that exceeds the p-quantile is known in VaR. The models used to accommodate characteristics of the stock portfolio in this research are EVT-GARCH-D-vine copula and EVT-GJR-D-vine copula so the performance of these two models can be compared. A comparison of the performance of the EVT-GARCH-D-vine copula and EVT-GJR-D-vine copula models can be seen from the Kupiec test backtesting process. Exceeded value Kupiec Test on CVaR 99% is 2, CVaR 95% is 6, and CVaR 90% is 13 for AR(1)-GARCH-t(1,1)-GPD and CVaR 99% is 3, CVaR 95% is 7, and CVaR 90% is 13 for AR(1)-GJR-t(1,1)-GPD. The Kupiec test describes the estimated risk value of CVaR running well with the value of the entire model above the significant level of ? = 0.05 so as to provide a conclusion of risk estimates considered feasible.
{"title":"ESTIMASI CVAR PADA PORTOFOLIO SAHAM MENGGUNAKAN METODE GJR-EVT DENGAN PENDEKATAN D-VINE COPULA","authors":"D. Maulana, K. Dharmawan, I. G. A. M. Srinadi","doi":"10.24843/mtk.2022.v11.i02.p372","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i02.p372","url":null,"abstract":"Risk measure using Conditional Value at Risk can be calculate if values that exceeds the p-quantile is known in VaR. The models used to accommodate characteristics of the stock portfolio in this research are EVT-GARCH-D-vine copula and EVT-GJR-D-vine copula so the performance of these two models can be compared. A comparison of the performance of the EVT-GARCH-D-vine copula and EVT-GJR-D-vine copula models can be seen from the Kupiec test backtesting process. Exceeded value Kupiec Test on CVaR 99% is 2, CVaR 95% is 6, and CVaR 90% is 13 for AR(1)-GARCH-t(1,1)-GPD and CVaR 99% is 3, CVaR 95% is 7, and CVaR 90% is 13 for AR(1)-GJR-t(1,1)-GPD. The Kupiec test describes the estimated risk value of CVaR running well with the value of the entire model above the significant level of ? = 0.05 so as to provide a conclusion of risk estimates considered feasible.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47070455","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 : 2022-05-31DOI: 10.24843/mtk.2022.v11.i02.p367
I. Pratama, I. W. Sumarjaya, NI Luh Putu Suciptawati
One of the spectacular advances in technology in the economic field is the cryptocurrency it created. The fluctuating price of Bitcoin, is widely used as a means of making profit. The time series forecasting method that can be used for the case of nonlinear time series data such as Bitcoin data is the smooth transition autoregressive (STAR) model. STAR is an extension of the autoregressive model for nonlinear time data. The purpose of this study is to obtain the results of forecasting Bitcoin price data for the next 2 two months using the STAR method. The data used in this study is Bitcoin daily price data from September 2017 to April 2021. To estimate the STAR model, several things that must be determined are the autoregressive model, transition variables, and transition functions. If the STAR model has been estimated, forecasting will be carried out for the next 2 months, which results in the forecast for the highest Bitcoin price falling on June 30, 2021 and the lowest Bitcoin price falling on May 1, 2021.
{"title":"PERAMALAN HARGA BITCOIN DENGAN METODE SMOOTH TRANSITION AUTOREGRESSIVE (STAR)","authors":"I. Pratama, I. W. Sumarjaya, NI Luh Putu Suciptawati","doi":"10.24843/mtk.2022.v11.i02.p367","DOIUrl":"https://doi.org/10.24843/mtk.2022.v11.i02.p367","url":null,"abstract":"One of the spectacular advances in technology in the economic field is the cryptocurrency it created. The fluctuating price of Bitcoin, is widely used as a means of making profit. The time series forecasting method that can be used for the case of nonlinear time series data such as Bitcoin data is the smooth transition autoregressive (STAR) model. STAR is an extension of the autoregressive model for nonlinear time data. The purpose of this study is to obtain the results of forecasting Bitcoin price data for the next 2 two months using the STAR method. The data used in this study is Bitcoin daily price data from September 2017 to April 2021. To estimate the STAR model, several things that must be determined are the autoregressive model, transition variables, and transition functions. If the STAR model has been estimated, forecasting will be carried out for the next 2 months, which results in the forecast for the highest Bitcoin price falling on June 30, 2021 and the lowest Bitcoin price falling on May 1, 2021.","PeriodicalId":11600,"journal":{"name":"E-Jurnal Matematika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46968142","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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