Jurnal Varian最新文献

Application of Principal Component Regression in Analyzing Factors Affecting Human Development Index 主成分回归在人类发展指数影响因素分析中的应用

Jurnal Varian Pub Date : 2023-05-23 DOI: 10.30812/varian.v6i2.2366

Sumarni Susilawati, D. Didiharyono

{"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}

引用次数: 0

The NADI Mathematical Model on the Danger Level of the Bili-Bili Dam 比利-比利大坝危险等级的NADI数学模型

Jurnal Varian Pub Date : 2023-05-19 DOI: 10.30812/varian.v6i2.2237

Sukarna Sukarna, Andi Muhammad Ridho Yusuf Sainon Andin P, S. Side, A. Aswi, Supriadi Yusuf

{"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}

引用次数: 0

Impact of SST Anomalies on Coral Reefs Damage Based on Copula Analysis 基于Copula分析的海温异常对珊瑚礁破坏的影响

Jurnal Varian Pub Date : 2023-05-19 DOI: 10.30812/varian.v6i2.2324

P. P. Oktaviana, K. Fithriasari

{"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}

引用次数: 0

K-Means – Resilient Backpropagation Neural Network in Predicting Poverty Levels k -均值-弹性反向传播神经网络预测贫困水平

Jurnal Varian Pub Date : 2023-05-17 DOI: 10.30812/varian.v6i2.2756

B. Poerwanto

{"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}

引用次数: 0

Regression Model of Land Area and Amount of Production to the Selling Price of Corn 土地面积和产量对玉米销售价格的回归模型

Jurnal Varian Pub Date : 2023-05-17 DOI: 10.30812/varian.v6i2.2753

Astriyani Oktafian, V. Mandailina, Mahsup Mahsup, Wasim Raza, Kirti Verma, S. Syaharuddin

{"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}

引用次数: 0

Robust Spatial-Temporal Analysis of Toddler Pneumonia Cases and its Influencing Factors 幼儿肺炎病例及其影响因素的稳健时空分析

Jurnal Varian Pub Date : 2023-05-16 DOI: 10.30812/varian.v6i2.2599

M. Musdalifah, S. Siswanto, N. Ilyas

{"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}

引用次数: 0

Kernel Nonparametric Regression for Forecasting Local Original Income 核非参数回归预测局部原始收入

Jurnal Varian Pub Date : 2023-05-16 DOI: 10.30812/varian.v6i2.2585

Joji Ardian Pembargi, M. Hadijati, N. Fitriyani

{"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}

引用次数: 1

Predicting Stock Markets Using Binary Logistic Regression Based on Bry-Boschan Algorithm 基于Bry-Boschan算法的二元Logistic回归预测股票市场

Jurnal Varian Pub Date : 2023-05-16 DOI: 10.30812/varian.v6i2.2385

Mujiati Dwi Kartikasari, Renanta Dzakiya Nafalana

{"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}

引用次数: 0

Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline 基于多变量自适应回归样条的Covid-19疫苗感知分类

Jurnal Varian Pub Date : 2023-05-16 DOI: 10.30812/varian.v6i2.2639

Rizki Fitri Ananda, L. Harsyiah, Muhammad Rijal Alfian

{"title":"Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline","authors":"Rizki Fitri Ananda, L. Harsyiah, Muhammad Rijal Alfian","doi":"10.30812/varian.v6i2.2639","DOIUrl":"https://doi.org/10.30812/varian.v6i2.2639","url":null,"abstract":"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","PeriodicalId":188119,"journal":{"name":"Jurnal Varian","volume":"12 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":"128082448","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}

引用次数: 0

Measurement of DEA-Based ICT Development Efficiency Level with Modified CCR Method 基于dea的信息通信技术发展效率水平的改进CCR方法测度

Jurnal Varian Pub Date : 2022-11-23 DOI: 10.30812/varian.v6i1.2197

Defri Muhammad Chan, H. Mawengkang, Sawaluddin Nasution

引用次数: 0