Articles
168 Documents
<![CDATA[Predicting Stock Markets Using Binary Logistic Regression Based on Bry-Boschan Algorithm ]]>
<![CDATA[Mujiati Dwi Kartikasari]]>;
<![CDATA[Renanta Dzakiya Nafalana]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v6i2.2385
<![CDATA[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%.]]>
<![CDATA[PLS Analysis: How Family Support Affect Students' Self-Confidence in Completing Thesis ]]>
<![CDATA[Arya Fendha Ibnu Shina]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v6i2.2442
<![CDATA[A student's success can not be separated from a final project called a thesis as a determinant of student graduation in college. However, in the process, students often face various obstacles and challenges. To overcome this, students need to have high self-confidence. In increasing self-confidence, the role of the family is required as the main factor that encourages students to succeed. The purpose of this paper is to determine the influence of forms of family support in the form of informational support, instrumental support, appraisal and reward support, and emotional support on the self-confidence of 58 students of the Islamic Guidance and Counseling Study Program class of 2018 UIN Sunan Kalijaga Yogyakarta who are working on a thesis for students. The data analysis method used is Partial Least Square (PLS). Partial Least Square (PLS) is a component or variant-based Structural Equation Model (SEM) model. Partial Least Square (PLS) is a variant-based Structural Equation Model (SEM) model.The results of this study state that 46.7% of students' self-confidence variations in completing a thesis are influenced by family support while the rest is influenced by other factors. In addition, emotional support has a significant effect on student self-confidence in doing a thesis with a p-value of 0.000. Thus, it can be concluded that emotional support becoming the form of family support that played the most important role in increasing student self-confidence. The results of this study will later be used as suggestion for students’s family and also for counselors in modifying behavior to increase students’s self-confidence.]]>
<![CDATA[Kernel Nonparametric Regression for Forecasting Local Original Income ]]>
<![CDATA[Joji Ardian Pembargi]]>;
<![CDATA[Mustika Hadijati]]>;
<![CDATA[Nurul Fitriyani]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v6i2.2585
<![CDATA[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.]]>
<![CDATA[Robust Spatial-Temporal Analysis of Toddler Pneumonia Cases and its Influencing Factors ]]>
<![CDATA[Musdalifah Musdalifah]]>;
<![CDATA[Siswanto Siswanto]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v6i2.2599
<![CDATA[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.]]>
<![CDATA[Classification Of Perceptions Of The Covid-19 Vaccine Using Multivariate Adaptive Regression Spline ]]>
<![CDATA[Rizki Fitri Ananda]]>;
<![CDATA[Lisa Harsyiah]]>;
<![CDATA[Muhammad Rijal Alfian]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v6i2.2639
<![CDATA[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]]>
<![CDATA[Regression Model of Land Area and Amount of Production to the Selling Price of Corn ]]>
<![CDATA[Astriyani Oktafian]]>;
<![CDATA[Vera Mandailina]]>;
<![CDATA[Mahsup Mahsup]]>;
<![CDATA[Wasim Raza]]>;
<![CDATA[Kirti Verma]]>;
<![CDATA[Syaharuddin Syaharuddin]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v6i2.2753
<![CDATA[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.]]>
<![CDATA[K-Means – Resilient Backpropagation Neural Network in Predicting Poverty Levels ]]>
<![CDATA[Bobby Poerwanto]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v6i2.2756
<![CDATA[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.]]>
<![CDATA[Finding the Best Model in Nonlinear Regression: Using the Coefficient of Determination ]]>
<![CDATA[Vitri Aprilla Handayani]]>;
<![CDATA[Widya Reza]]>;
<![CDATA[Saba Mehmood]]>
<![CDATA[ Jurnal Varian Vol 7 No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v7i1.2322
<![CDATA[In Indonesia, inflation plays a significant role in shaping economic growth. Therefore, it is essential to examine the impact of inflation on economic growth through a comprehensive analysis. This analysis aims to identify the factors influencing economic growth in Indonesia by utilizing nonlinear regression analysis. The study focuses specifically on modeling economic growth in Batam City and its correlation with inflation. The primary goal is to identify the most effective nonlinear regression model that accurately represents the relationship between economic growth and inflation, as determined by the coefficient of determination. The method used in this research is nonlinear regression methods provide a more accurate and comprehensive analysis when dealing with complex relationships and can help uncover valuable insights that may be missed by simpler linear models. The results of the analysis finding the model that is suitable for modeling inflation on economic growth is a quadratic model with a coefficient of determination of 73.4%. The research has found that the best model for explaining the impact of inflation on economic growth is the Quadratic model with an R-value of 0.734 or 75%. These results indicate that the Quadratic model can account for 75% of the influence of inflation on economic growth.]]>
<![CDATA[Application of Artificial Neural Network in Predicting Direct Economic Losses Due to Earthquake ]]>
<![CDATA[Ulil Azmi]]>;
<![CDATA[Soehardjoepri Soehardjoepri]]>;
<![CDATA[Rudi Prihandoko]]>;
<![CDATA[Iqra Asif]]>
<![CDATA[ Jurnal Varian Vol 7 No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v7i1.2326
<![CDATA[Accurately predicting the direct economic losses caused by earthquakes is important for policy makers for disaster budgets. Before a disaster strikes, it is important to consider the public policy costs associated with disaster relief and recovery. The aim of this study is to provide a risk assessment approach, which can benefit all parties involved. Artificial neural networks are widely used for time series forecasting, especially financial forecasting. Therefore, this study proposes a cutting-edge forecasting method such as backpropagation neural network (BPNN) and other prediction methods: neural network autoregressive (NNAR) and ARIMA-GARCH to obtain the best prediction results. This paper applies interpolation data to increase the amount of data used. Two interpolations were applied to amplify the original small sample with virtual points, namely cubic splines and further piecewise interpolation using. The results of this study are the cubic spline interpolation is the most effective way to solve the small sampling problem to predict direct economic losses due to the Indonesian earthquake and the BPNN method outperforms other traditional methods with an RMSE of 0.024 in the training period and 0.174 in the testing period, significantly lower than other methods. The results of this research can be used as reference material for the government in estimating the level of earthquake losses and can be used to develop risk reduction strategies.]]>
<![CDATA[Improved Chi Square Automatic Interaction Detection on Students Discontinuation to Secondary School ]]>
<![CDATA[Fadhil Al Anshory]]>;
<![CDATA[Siswanto Siswanto]]>;
<![CDATA[Sri Astuti Thamrin]]>;
<![CDATA[Ika Inayah]]>
<![CDATA[ Jurnal Varian Vol 7 No 1 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/varian.v7i1.2627
<![CDATA[Improved Chi Square Automatic Interaction Detection (CHAID) with bias correction is the development of the CHAID method by relying on Tschuprow's T test calculations with bias correction in the process of forming a classification tree. This study aims to obtain a classification of factors which influence students for not continuing their education from junior high school or equivalent to high school or equivalent. The results obtained in the classification tree produce nine classifications. Based on the results of the classification tree, the classification of students who do not continue their education to high school or equivalent is: students with disabilities who do not have access to Information and Communication Technology (ICTs) (0.89); students who work without disability but do not have access to ICTs (0.73); and students who do not work without disability but do not have access to in ICTs (0.60). Based on the classification obtained the factors which influence students for not continuing their education to high school or equivalent are access to ICTs, employment status, and persons with disabilities. The classification accuracy of the results uses the Improved-CHAID method with bias correction with a proportion of 80% training data and 20% testing data, namely 72.3033% on training data and an increase of 73.3300% on testing data.]]>
