Pub Date : 2023-05-23DOI: 10.30812/varian.v6i2.2366
Sumarni Susilawati, D. Didiharyono
The human development index is an indicator to measure the quality of people's lives. If the human development index number increases, the better the quality of people's lives. There are many factors or variables that affect the level of the human development index, ranging from economic issues, education, health and other factors. However, not all factors have a positive and significant effect. Thus, this study aims to determine the factors that significantly affect the human development index in South Sulawesi. The method used in this study is principal component regression which involves many variables. The variables involved are expected length of schooling, average length of schooling, percentage of population with the highest Diploma, Bachelor and Masters education, school enrollment rate for people aged 7-24 years, percentage of poor people, spending per capita, and life expectancy. From the results of data processing using principal component analysis, 4 main components are obtained which represent the other components, for principal component regression, taking into account the cumulative proportion of > 80%. The results of this study indicate that the human development index in South Sulawesi is influenced by all the variables involved, which is equal to 95.7%. With the variable percentage of poverty being one of the variables that has a negative effect on HDI in South Sulawesi which shows that the higher the percentage of poverty, the lower the human development index. Thus, in order to increase the human development index in Indonesia, it is necessary to take strategic steps to improve people's welfare.
{"title":"Application of Principal Component Regression in Analyzing Factors Affecting Human Development Index","authors":"Sumarni Susilawati, D. Didiharyono","doi":"10.30812/varian.v6i2.2366","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2366","url":null,"abstract":"The human development index is an indicator to measure the quality of people's lives. If the human development index number increases, the better the quality of people's lives. There are many factors or variables that affect the level of the human development index, ranging from economic issues, education, health and other factors. However, not all factors have a positive and significant effect. Thus, this study aims to determine the factors that significantly affect the human development index in South Sulawesi. The method used in this study is principal component regression which involves many variables. The variables involved are expected length of schooling, average length of schooling, percentage of population with the highest Diploma, Bachelor and Masters education, school enrollment rate for people aged 7-24 years, percentage of poor people, spending per capita, and life expectancy. From the results of data processing using principal component analysis, 4 main components are obtained which represent the other components, for principal component regression, taking into account the cumulative proportion of > 80%. The results of this study indicate that the human development index in South Sulawesi is influenced by all the variables involved, which is equal to 95.7%. With the variable percentage of poverty being one of the variables that has a negative effect on HDI in South Sulawesi which shows that the higher the percentage of poverty, the lower the human development index. Thus, in order to increase the human development index in Indonesia, it is necessary to take strategic steps to improve people's welfare.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123489135","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 : 2023-05-19DOI: 10.30812/varian.v6i2.2237
Sukarna Sukarna, Andi Muhammad Ridho Yusuf Sainon Andin P, S. Side, A. Aswi, Supriadi Yusuf
The research discusses the NADI mathematical model due to the overflow of the Bili-Bili dam, using secondary data obtained through online literature review by collecting various information related to the Bili-Bili Dam, starting from the Jeberang River Scheme, the chronology of floods, normal or dry conditions, and dam operation patterns. The aim of this study is to predict the level of danger of Bili-bili dam overflow over time, considering extreme weather factors and standard operating procedures performed by humans. The research uses analytical and computational methods. The study obtained the NADI mathematical model due to the overflow of the Bili-Bili dam, with two equilibrium points: (1) the equilibrium point free of disaster, (2) the disaster equilibrium point, and a basic disaster reproduction number of R0 = 1.219. This indicates that the water discharge from the dam is high and has an impact on the overflowing water for communities around the Jeneberang river. Therefore, it can be concluded that the NADI model can be used to simulate the Bili-bili dam process based on extreme weather and dam SOP, and predict the level of danger of Bili-bili dam overflow, which is also a novelty that has not been done in previous studies.
{"title":"The NADI Mathematical Model on the Danger Level of the Bili-Bili Dam","authors":"Sukarna Sukarna, Andi Muhammad Ridho Yusuf Sainon Andin P, S. Side, A. Aswi, Supriadi Yusuf","doi":"10.30812/varian.v6i2.2237","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2237","url":null,"abstract":"The research discusses the NADI mathematical model due to the overflow of the Bili-Bili dam, using secondary data obtained through online literature review by collecting various information related to the Bili-Bili Dam, starting from the Jeberang River Scheme, the chronology of floods, normal or dry conditions, and dam operation patterns. The aim of this study is to predict the level of danger of Bili-bili dam overflow over time, considering extreme weather factors and standard operating procedures performed by humans. The research uses analytical and computational methods. The study obtained the NADI mathematical model due to the overflow of the Bili-Bili dam, with two equilibrium points: (1) the equilibrium point free of disaster, (2) the disaster equilibrium point, and a basic disaster reproduction number of R0 = 1.219. This indicates that the water discharge from the dam is high and has an impact on the overflowing water for communities around the Jeneberang river. Therefore, it can be concluded that the NADI model can be used to simulate the Bili-bili dam process based on extreme weather and dam SOP, and predict the level of danger of Bili-bili dam overflow, which is also a novelty that has not been done in previous studies.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128946706","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 : 2023-05-19DOI: 10.30812/varian.v6i2.2324
P. P. Oktaviana, K. Fithriasari
The condition of coral reefs in Indonesia is alarming. One of the influenting factors of coral reefs damage is extreme climate change. The aim of this study is to determine the relationship of climate change, that is Sea Surface Temperature (SST) anomaly index, and coral reefs damage in West, Central and East Region of Indonesia. The method used in this study is Copula analysis. Copula is one of the statistical methods used to determine the relationship of two or more variables, in which case the distribution can be normal or not. First, data is transformed into Uniform [0,1] domain. Then, Copula parameter is estimated to get significance parameter. Lastly, the best Copula that has the highest log likelihood value is selected to represent the relationship of data. The result indicates that percentage of coral reefs damage in West and Central Region has relationship with SST Nino 4, while coral reefs damage in East Region does not have relationship with any of SST Nino anomalies. In West Region, the best Copula represents the relationship is Gaussian Copula (parameter = -0.32); it concludes that the higher the value of SST Nino 4, the lower the percentage of coral reefs damage and otherwise. While in Central Indonesia, Frank Copula (parameter = -4.89) is selected; it does not have tail dependency so that the SST Nino 4 and the percentage of coral reefs in damage condition in Central Region has low correlation.
{"title":"Impact of SST Anomalies on Coral Reefs Damage Based on Copula Analysis","authors":"P. P. Oktaviana, K. Fithriasari","doi":"10.30812/varian.v6i2.2324","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2324","url":null,"abstract":"The condition of coral reefs in Indonesia is alarming. One of the influenting factors of coral reefs damage is extreme climate change. The aim of this study is to determine the relationship of climate change, that is Sea Surface Temperature (SST) anomaly index, and coral reefs damage in West, Central and East Region of Indonesia. The method used in this study is Copula analysis. Copula is one of the statistical methods used to determine the relationship of two or more variables, in which case the distribution can be normal or not. First, data is transformed into Uniform [0,1] domain. Then, Copula parameter is estimated to get significance parameter. Lastly, the best Copula that has the highest log likelihood value is selected to represent the relationship of data. The result indicates that percentage of coral reefs damage in West and Central Region has relationship with SST Nino 4, while coral reefs damage in East Region does not have relationship with any of SST Nino anomalies. In West Region, the best Copula represents the relationship is Gaussian Copula (parameter = -0.32); it concludes that the higher the value of SST Nino 4, the lower the percentage of coral reefs damage and otherwise. While in Central Indonesia, Frank Copula (parameter = -4.89) is selected; it does not have tail dependency so that the SST Nino 4 and the percentage of coral reefs in damage condition in Central Region has low correlation.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128486854","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 : 2023-05-17DOI: 10.30812/varian.v6i2.2756
B. Poerwanto
In solving economic problems, the government has implemented several development policies. However, this policy is considered to be too centered on big cities. So, through this research it is hoped that it can provide an overview related to regional groups that fall into the poorer category so that the government can also provide accelerated development policies that are oriented towards improving the economy of residents in the area. This study aims to determine the results of classifying district/city poverty levels in Indonesia as a basis for classification for predictions and to classify district/city poverty levels based on influencing factors. The method used in this study is K-Means Clustering using the poverty depth index and poverty severity index variables, then proceed with using the Backpropagation Neural Network (BNN) algorithm using the GRDP, per capita expenditure, human development index, and mean years of schooling. The results obtained using the K-Means algorithm are that there are 42 districts/cities that belong to cluster 1 where this region has a poverty index depth and severity index value that is higher than the 472 districts/cities in cluster 2. Furthermore, the cluster results are used as response variables for classification with BNN. The accuracy of the model obtained is very high, which is equal to 98.06, so the model is very feasible to be used as a poverty rate prediction model based on the variables used.
{"title":"K-Means – Resilient Backpropagation Neural Network in Predicting Poverty Levels","authors":"B. Poerwanto","doi":"10.30812/varian.v6i2.2756","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2756","url":null,"abstract":"In solving economic problems, the government has implemented several development policies. However, this policy is considered to be too centered on big cities. So, through this research it is hoped that it can provide an overview related to regional groups that fall into the poorer category so that the government can also provide accelerated development policies that are oriented towards improving the economy of residents in the area. This study aims to determine the results of classifying district/city poverty levels in Indonesia as a basis for classification for predictions and to classify district/city poverty levels based on influencing factors. The method used in this study is K-Means Clustering using the poverty depth index and poverty severity index variables, then proceed with using the Backpropagation Neural Network (BNN) algorithm using the GRDP, per capita expenditure, human development index, and mean years of schooling. The results obtained using the K-Means algorithm are that there are 42 districts/cities that belong to cluster 1 where this region has a poverty index depth and severity index value that is higher than the 472 districts/cities in cluster 2. Furthermore, the cluster results are used as response variables for classification with BNN. The accuracy of the model obtained is very high, which is equal to 98.06, so the model is very feasible to be used as a poverty rate prediction model based on the variables used.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133184396","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 : 2023-05-17DOI: 10.30812/varian.v6i2.2753
Astriyani Oktafian, V. Mandailina, Mahsup Mahsup, Wasim Raza, Kirti Verma, S. Syaharuddin
Currently, land area, production and maize prices in West Nusa Tenggara province are sometimes unstable. One of the factors affecting the instability of maize prices is the shift in planting patterns at the farm level. The purpose of this study is to determine the effect of land area and total production on the selling price of maize. The method used is quantitative with data analysis techniques using multiple linear regression. The source of data is from the Central Bureau of Statistics, Department of Agriculture and Plantation of NTB. The regression equation found is Y = 3109.911 + 0.007X1 - 0.001X2. This result shows that the X1 variable of 0.007 means that every time there is an increase in the land area variable by 1%, the selling price increases by 7%. While the X2 variable decreased by 1%. The hypothesis with the calculation of the partial t-test of land area is 1.249, which means that land area has no influence on the selling price of NTB corn in 2012-2021. In future research, it is necessary to conduct research on the development of corn planting land area, production, productivity per unit of land area nationally associated with the rate of population growth, corn demand, and the growth of corn imports nationally.
{"title":"Regression Model of Land Area and Amount of Production to the Selling Price of Corn","authors":"Astriyani Oktafian, V. Mandailina, Mahsup Mahsup, Wasim Raza, Kirti Verma, S. Syaharuddin","doi":"10.30812/varian.v6i2.2753","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2753","url":null,"abstract":"Currently, land area, production and maize prices in West Nusa Tenggara province are sometimes unstable. One of the factors affecting the instability of maize prices is the shift in planting patterns at the farm level. The purpose of this study is to determine the effect of land area and total production on the selling price of maize. The method used is quantitative with data analysis techniques using multiple linear regression. The source of data is from the Central Bureau of Statistics, Department of Agriculture and Plantation of NTB. The regression equation found is Y = 3109.911 + 0.007X1 - 0.001X2. This result shows that the X1 variable of 0.007 means that every time there is an increase in the land area variable by 1%, the selling price increases by 7%. While the X2 variable decreased by 1%. The hypothesis with the calculation of the partial t-test of land area is 1.249, which means that land area has no influence on the selling price of NTB corn in 2012-2021. In future research, it is necessary to conduct research on the development of corn planting land area, production, productivity per unit of land area nationally associated with the rate of population growth, corn demand, and the growth of corn imports nationally.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122205322","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 : 2023-05-16DOI: 10.30812/varian.v6i2.2599
M. Musdalifah, S. Siswanto, N. Ilyas
Pneumonia is a disease that causes inflammation of the lungs and is one of the most common diseases infecting toddlers. As a directly infectious disease, there is a possibility of the influence of location diversity on the number of pneumonia sufferers. Robust Geographically and Temporally Weighted Regression (RGTWR) is a method used to model data by considering the heterogeneity of location and time and to overcome outliers in the data. The data used is the number of pneumonia sufferers aged under five and the factors that are thought to influence it, namely the number of health centers, population density, percentage of children under five with complete basic immunizations, percentage of children under five who are exclusively breastfed 0-6 months, and percentage of poor people. This study was conducted to model pneumonia sufferers under five and to find out the factors that significantly affect the number of sufferers in each observation. RGTWR produces an optimal model with an R2 value of 99.9997%, a Mean Absolute Deviation of 21.6852, and a Median Absolute Deviation of 6.9661 compared to the Geographically and Temporally Weighted Regression model. Variables number of puskesmas, percentage of infants with complete basic immunization, and percentage of poor population are factors that influence the number of pneumonia sufferers under five in most locations in 34 provinces and 5 years of observation.
{"title":"Robust Spatial-Temporal Analysis of Toddler Pneumonia Cases and its Influencing Factors","authors":"M. Musdalifah, S. Siswanto, N. Ilyas","doi":"10.30812/varian.v6i2.2599","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2599","url":null,"abstract":"Pneumonia is a disease that causes inflammation of the lungs and is one of the most common diseases infecting toddlers. As a directly infectious disease, there is a possibility of the influence of location diversity on the number of pneumonia sufferers. Robust Geographically and Temporally Weighted Regression (RGTWR) is a method used to model data by considering the heterogeneity of location and time and to overcome outliers in the data. The data used is the number of pneumonia sufferers aged under five and the factors that are thought to influence it, namely the number of health centers, population density, percentage of children under five with complete basic immunizations, percentage of children under five who are exclusively breastfed 0-6 months, and percentage of poor people. This study was conducted to model pneumonia sufferers under five and to find out the factors that significantly affect the number of sufferers in each observation. RGTWR produces an optimal model with an R2 value of 99.9997%, a Mean Absolute Deviation of 21.6852, and a Median Absolute Deviation of 6.9661 compared to the Geographically and Temporally Weighted Regression model. Variables number of puskesmas, percentage of infants with complete basic immunization, and percentage of poor population are factors that influence the number of pneumonia sufferers under five in most locations in 34 provinces and 5 years of observation.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115847961","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 : 2023-05-16DOI: 10.30812/varian.v6i2.2585
Joji Ardian Pembargi, M. Hadijati, N. Fitriyani
Regional Original Revenue (ROR) is an income collected based on regional regulations under statutory regulations. ROR aims to give authority to Regional Governments to sponsor the implementation of regional autonomy following regional potential. Every year, the Central Lombok Regency government sets ROR targets to assist the government in formulating regional policies. The targets set by the government are sometimes not following their realization. This study aims to determine a model that can be used in forecasting ROR targets. One way to predict the value of ROR is by using a nonparametric regression approach. This approach is flexible since it is not dependent on a particular model. The use of the nonparametric kernel regression method with the Gaussian kernel function obtained a minimum GCV value of 1,769688931 with an optimum bandwidth value of of 0,212740452 and of 0,529682589. Modeling with optimum bandwidth produces a coefficient of determination of 87,55%. The best model is used for forecasting and produces a MAPE value of 5,4%. The analysis results show that what influences the value of ROR is ROR receipts in the previous month and the previous 12 months.
地区原始收入(Regional Original Revenue, ROR)是在法定规定下，根据地区规定征收的收入。区域认可的目的是授权区域政府根据区域潜力赞助实施区域自治。每年，中央龙目岛政府设定ROR目标，以协助政府制定区域政策。政府设定的目标有时无法实现。本研究旨在确定一个可用于预测ROR目标的模型。预测ROR值的一种方法是使用非参数回归方法。这种方法是灵活的，因为它不依赖于特定的模型。利用高斯核函数的非参数核回归方法得到最小GCV值为1 769688931，最优带宽值为0 212740452和0 529682589。以最优带宽建模产生的决定系数为87,55%。最好的模型用于预测，并产生5.4%的MAPE值。分析结果表明，影响ROR值的因素是前一个月和前12个月的ROR收入。
{"title":"Kernel Nonparametric Regression for Forecasting Local Original Income","authors":"Joji Ardian Pembargi, M. Hadijati, N. Fitriyani","doi":"10.30812/varian.v6i2.2585","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2585","url":null,"abstract":"Regional Original Revenue (ROR) is an income collected based on regional regulations under statutory regulations. ROR aims to give authority to Regional Governments to sponsor the implementation of regional autonomy following regional potential. Every year, the Central Lombok Regency government sets ROR targets to assist the government in formulating regional policies. The targets set by the government are sometimes not following their realization. This study aims to determine a model that can be used in forecasting ROR targets. One way to predict the value of ROR is by using a nonparametric regression approach. This approach is flexible since it is not dependent on a particular model. The use of the nonparametric kernel regression method with the Gaussian kernel function obtained a minimum GCV value of 1,769688931 with an optimum bandwidth value of of 0,212740452 and of 0,529682589. Modeling with optimum bandwidth produces a coefficient of determination of 87,55%. The best model is used for forecasting and produces a MAPE value of 5,4%. The analysis results show that what influences the value of ROR is ROR receipts in the previous month and the previous 12 months.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130176446","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 : 2023-05-16DOI: 10.30812/varian.v6i2.2385
Mujiati Dwi Kartikasari, Renanta Dzakiya Nafalana
In the stock market, there are bullish and bearish terms that are reflected in the movement of the stock price index. One of the stock price indexes listed on the Indonesia Stock Exchange (IDX) is the IDX Composite. Stock market conditions fluctuate along with changes in stock prices that move randomly, while investors expect market conditions to be active (bullish market). Several factors influence the movement of the IDX Composite, one of which is macroeconomic factors. The purpose of this research is to find out the condition of stock market as well as predict its condition using macroeconomics indicators. The method used to determine stock market conditions (bullish or bearish) is the Bry-Boschan algorithm, while the method used to predict the stock market using macroeconomic indicators is the binary logistic regression method. The Bry-Boschan algorithm is widely used to detect peaks and troughs in business cycle analysis. Binary logistic regression is used to model data with responses that have two categories or are in the form of binary numbers. Results show that the IDX Composite experienced 42 times (month) bearish periods and 191 times (month) experienced bullish periods. The obtained model has an accuracy value of 81.55%.
{"title":"Predicting Stock Markets Using Binary Logistic Regression Based on Bry-Boschan Algorithm","authors":"Mujiati Dwi Kartikasari, Renanta Dzakiya Nafalana","doi":"10.30812/varian.v6i2.2385","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2385","url":null,"abstract":"In the stock market, there are bullish and bearish terms that are reflected in the movement of the stock price index. One of the stock price indexes listed on the Indonesia Stock Exchange (IDX) is the IDX Composite. Stock market conditions fluctuate along with changes in stock prices that move randomly, while investors expect market conditions to be active (bullish market). Several factors influence the movement of the IDX Composite, one of which is macroeconomic factors. The purpose of this research is to find out the condition of stock market as well as predict its condition using macroeconomics indicators. The method used to determine stock market conditions (bullish or bearish) is the Bry-Boschan algorithm, while the method used to predict the stock market using macroeconomic indicators is the binary logistic regression method. The Bry-Boschan algorithm is widely used to detect peaks and troughs in business cycle analysis. Binary logistic regression is used to model data with responses that have two categories or are in the form of binary numbers. Results show that the IDX Composite experienced 42 times (month) bearish periods and 191 times (month) experienced bullish periods. The obtained model has an accuracy value of 81.55%.","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115333974","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 : 2023-05-16DOI: 10.30812/varian.v6i2.2639
Rizki Fitri Ananda, L. Harsyiah, Muhammad Rijal Alfian
Indonesia is one of the countries infected with the covid-19 virus. One of the government's efforts is the covid-19 vaccination. However, the covid-19 vaccination caused controversy for some people because many people refused to be vaccinated. Public perception of the covid-19 vaccine can be categorized into two, namely positive and negative, based on survey from Indonesia ministry of health about acceptance of covid-19 vaccine state that this can be influenced by many factors. These factors are important to know as an effort to increase acceptance of covid-19. Multivariate Adaptive Regression Splines (MARS). The purpose of this study is to determine the classification model of public perception of the covid-19 vaccine and the factors that influence it. The method used in this study is Multivariate Adaptive Regression Splines (MARS). This method is appropriate classification method to be applied to categorical response variable data, The outcomes demonstrate that the optimum mars model is produced by combining BF= 24, MI =3, MO= 1, and GCV=0.07340546. The resulting classification level is 91.5% with influencing factors yaitu gender (x_1), age (x_2), last education (x_4), willingness to vaccinate (x_6), education (x_8). Based on the results obtained, the government can consider these factors for socialization
印度尼西亚是感染covid-19病毒的国家之一。政府的努力之一是covid-19疫苗接种。然而，由于许多人拒绝接种疫苗，covid-19疫苗接种引起了一些人的争议。根据印度尼西亚卫生部关于covid-19疫苗接受度的调查，公众对covid-19疫苗的看法可分为积极和消极两种，这可能受到许多因素的影响。了解这些因素对于提高对covid-19的接受度很重要。多元自适应样条回归(MARS)。本研究的目的是确定公众对covid-19疫苗认知的分类模型及其影响因素。本研究使用的方法是多元自适应样条回归(MARS)。结果表明，当BF= 24, MI =3, MO= 1, GCV=0.07340546时，可以得到最优的火星模型。分类水平为91.5%，影响因素有性别(x_1)、年龄(x_2)、末受教育程度(x_4)、接种意愿(x_6)、受教育程度(x_8)。根据得到的结果，政府可以考虑这些因素进行社会化
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Pub Date : 2022-11-23DOI: 10.30812/varian.v6i1.2197
Defri Muhammad Chan, H. Mawengkang, Sawaluddin Nasution
Data Envelopment Analysis (DEA) is the use of non-parametric mathematical programming that is useful for measuring the efficiency of the Decision Making Unit (DMU) of an organization. This study uses the Cooper and Rhodes (CCR) method known as the DEA-CCR multiplier which aims to determine the weight value of each input and output variable of the DMU being evaluated, but it is not sufficient to measure efficiency optimization. To get an efficient value of the weight value of each DMU as a reference to get updated DMU input and output values. So that the DMU efficiency value is obtained which is evaluated. The results of this study show how to modify the Multiplier Model-CCR into the Envelopment Model-CCR. Then displays the efficient level DMU which is evaluated as a result of the weight each DMU gets from the results of processing the LINDO application. Illustrations of changes in input variables and output variables are displayed in the form of tables and figures before and after the changes. The modified DEA-CCR model can also complete DMU super efficiency, effectiveness and productivity.
数据包络分析(DEA)是对非参数数学规划的使用，用于衡量组织决策单元(DMU)的效率。本研究采用Cooper and Rhodes (CCR)方法，即DEA-CCR乘数，该方法旨在确定待评估DMU的每个输入和输出变量的权重值，但不足以衡量效率优化。获取每个DMU的权值的有效值作为参考，以获取更新后的DMU输入输出值。得到了DMU效率值，并对其进行了评价。本文的研究结果显示了如何将乘数模型修正为包络模型。然后显示DMU的效率级别，这是每个DMU从处理LINDO应用程序的结果中获得的权重的结果。输入变量和输出变量的变化以表格和图形的形式显示在变化前后。改进后的DEA-CCR模型也能实现DMU超高的效率、有效性和生产率。
{"title":"Measurement of DEA-Based ICT Development Efficiency Level with Modified CCR Method","authors":"Defri Muhammad Chan, H. Mawengkang, Sawaluddin Nasution","doi":"10.30812/varian.v6i1.2197","DOIUrl":"https://doi.org/10.30812/varian.v6i1.2197","url":null,"abstract":"Data Envelopment Analysis (DEA) is the use of non-parametric mathematical programming that is useful for measuring the efficiency of the Decision Making Unit (DMU) of an organization. This study uses the Cooper and Rhodes (CCR) method known as the DEA-CCR multiplier which aims to determine the weight value of each input and output variable of the DMU being evaluated, but it is not sufficient to measure efficiency optimization. To get an efficient value of the weight value of each DMU as a reference to get updated DMU input and output values. So that the DMU efficiency value is obtained which is evaluated. The results of this study show how to modify the Multiplier Model-CCR into the Envelopment Model-CCR. Then displays the efficient level DMU which is evaluated as a result of the weight each DMU gets from the results of processing the LINDO application. Illustrations of changes in input variables and output variables are displayed in the form of tables and figures before and after the changes. The modified DEA-CCR model can also complete DMU super efficiency, effectiveness and productivity. \u0000 ","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124670924","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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