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<![CDATA[Structural Equation Modeling-Partial Least Square for Poverty Modeling in Papua Province ]]>
<![CDATA[Wirajaya Kusuma]]>;
<![CDATA[Rifani Nur Sindy Setiawan]]>;
<![CDATA[Kirti Verma]]>;
<![CDATA[Carina Firstca Utomo]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.852
<![CDATA[Poverty in Papua Province in 2018 has increased from the previous year. The poverty rate in Papua Province in March 2018 reached 27,74%. This study aims to analyze the factors that influence it so that it can be handled properly. The research method used in this research is Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach. The research variables used consisted of 4 latent variables (Poverty, Economy, Human Resources (HR), and Health) with 16 indicators (manifest variables). Based on the analysis that has been done, it is found that economic and health variables have a negative and significant effect on poverty with path coefficients of -0,421 and -0,270, respectively. The health variable has a positive and significant effect on HR with a path coefficient of 0,496. Meanwhile, the HR variable has a positive and significant effect on the economy with a path coefficient of 0,801. It can be concluded that there are two variables that have a significant effect on poverty in Papua Province, including the economy and health.]]>
<![CDATA[Response Surface Regression with LTS and MM-Estimator to Overcome Outliers on Red Roselle Flowers ]]>
<![CDATA[Trianingsih Eni Lestari]]>;
<![CDATA[Rike Desy Tri Yuansa Yuansa]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.882
<![CDATA[The surface response method is similar to the regression analysis method which uses procedures or ways of estimating the response function regression model based on the Ordinary Least Square (OLS) method. Unfortunately, using the quadratic method has no drawbacks because it is easily sensitive to assumption deviations due to outlier cases. One of the solutions to the outlier problem is using robust regression. The method of parameters in the regression is very diverse, but the methods used in this study are the Least Trimmed Square (LTS) and MM-estimator methods because both methods have a high breakdown point of nearly 50%. The variables studied were the response variable consisting of red roselle plant height (Y1) and red roselle flower weight (Y2). While the independent variables were soil moisture factor (X1) and NPK fertilizer application factor (X2). The purpose of this study is to estimate the response surface regression parameters. using the LTS and MM-estimator methods on data that contains outliers. The resulting model in data analysis shows the same result that the best model is using the LTS estimation method. The modeling result of plant height obtained an R-Square value of 98,27% with an error is 1,243. Meanwhile, for the red rosella plant flower weight model, the R-Square value was 97,31% with an error is 0.6632.]]>
<![CDATA[A Cluster Analysis with Complete Linkage and Ward's Method for Health Service Data in Makassar City ]]>
<![CDATA[Isma Muthahharah]]>;
<![CDATA[Agusalim Juhari]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.883
<![CDATA[Health care facilities are a place used to organize health efforts. Health service data in Makassar City has not shown which sub-districts have excellent service criteria, good enough, and not good. Therefore, it is necessary to group sub-districts with cluster analysis using hierarchy method. The hierarchy method used in this study is only 2, namely complete linkage and ward's method. Complete linkage method is the opposite of the approach to the minimum distance principle that is the furthest distance between objects while Ward's Method is a method that aims to minimize variance between objects in one cluster. There are four health services used, namely Hospitals, Health Centers, Home Care and Telemedicine with 15 sub-districts. This study also used a validity test namely Index Davies Bouldin (IDB) to determine the criteria of health services. The results of the analysis on complete linkage formed 3 clusters, namely cluster 1 with good health services, cluster 2 with excellent health services, and cluster 3 with poor health services. In addition, ward's Method also formed 3 clusters, namely cluster 1 with good health services, clusters 2 with poor service, and cluster 3 with excellent health services.]]>
<![CDATA[Spatial Econometric Model on Economic Growth in West Nusa Tenggara ]]>
<![CDATA[Siti Soraya]]>;
<![CDATA[Baiq Candra Herawati]]>;
<![CDATA[Muttahid Shah]]>;
<![CDATA[Syaharuddin Syaharuddin]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.912
<![CDATA[Gross Regional Domestic Product (GRDP) is a reflection of a region's economic growth. West Nusa Tenggara (NTB) is one of the provinces that contributes to good GRDP for Indonesia. The purpose of this research is to modeling GRDP in NTB using spatial econmetrics. The data used is the GRDP data of each district / city in NTB Province as a response variable and factors that affect the number of workers, capital value and electrification ratio as predictor variables. The results showed that there is a spatial dependence on the district / city GRDP in NTB Province on the error model so that the model formed is the Spatial Error Model (SEM) with a rho of 71.1% and an AIC value of 173.34.]]>
<![CDATA[Modelling Crop Insurance Based on Weather Index Using The Homotopy Analysis for American Put Option ]]>
<![CDATA[Agus Sofian Eka Hidayat]]>;
<![CDATA[Monica Sandi Afa]]>;
<![CDATA[Dedi Kurniawan]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.993
<![CDATA[The crop insurance in Indonesia (AUTP) is much focused on the area impacted by flood, drought, and pest attack. The complication of the procedure to claim the loss must follow several conditions. The different approaches in the insurance sector, using weather index can be taken into consideration to produce a variety of insurance products. This insurance product used the American put option with the primary asset is the rainfall and the cumulative rainfall to exercise the claim, considering the optimal execution limit. The homotopic analysis is used to determine the valuation of the American put option, which also becomes the insurance premium. The case study is focused on areas experiencing a drought so that insurance claims can be exercise when the rainfall index value is below a predetermined limit. Considering the normality of the rainfall data, the calculation of insurance premium was done for the first growing season. The insurance premium is varies based on the optimal execution limit, while the calculation of profit is based on the optimum limit exercise and the minimum rainfall for the growing season, and its different depended on insurance claim acceptance limits.]]>
<![CDATA[Spline and Kernel Mixed Nonparametric Regression for Malnourished Children Model in West Nusa Tenggara ]]>
<![CDATA[Muhammad Sopian Sauri]]>;
<![CDATA[Mustika Hadijati]]>;
<![CDATA[Nurul Fitriyani]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.1003
<![CDATA[Health sector development is essential to improve human life quality, especially in West Nusa Tenggara (NTB) Province. Based on data from the NTB Provincial Health Office from 2011 to 2016, children under five suffering from malnutrition continued to increase, caused by several factors that affected the incident. Therefore, appropriate analysis is needed to model children who suffer from malnutrition in NTB Province in 2016, consisting of 10 districts based on the variables that influence it. The analysis in this study was carried out using a nonparametric regression mixed-model spline truncated and kernel. The estimation of the nonparametric regression curve depends on the optimal knot points and bandwidths parameter. Therefore, in determining the optimal knot points and bandwidths obtained from Generalized Cross-Validation (GCV). Based on the analysis that has been done, we obtained a nonparametric regression mixed-model spline truncated and kernel optimal knot points, such as for each variable and optimum bandwidths, such as and , with the value of GCV. The mixed model acquired has a good model by considering the values of and MSE. Besides, the MAPE value indicated a high degree of accuracy, so that the model obtained has an excellent forecast.]]>
<![CDATA[Determining Bullying Text Classification Using Naive Bayes Classification on Social Media ]]>
<![CDATA[Ade Clinton Sitepu]]>;
<![CDATA[Wanayumini Wanayumini]]>;
<![CDATA[Zakarias Situmorang]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.1086
<![CDATA[Cyber-bullying includes repeated acts with the aim of scaring, angering, or embarrassing those who are targeted Cyber-bullying is happening along with the rapid development of technology and social media in society. The media and users need to filter out bully comments because they can indirectly affect the mental psychology that reads them especially directly aimed at that person. By utilizing information mining, the system is expected to be able to classify information circulating in the community. One of the classification techniques that can be applied to text-based classification is Naïve Bayes. The algorithm is good at performing the classification process. In this research, the precision of the algorithm's has been carried out on 1000 comment datasets. The data is grouped manually first into the labels "bully" and "not bully" then the data is divided into training data and test data. To test the system's ability, the classified data is analyzed using the confusion matrix method. The results showed that the Naïve Bayes Algorithm got the level of precision at 87%. and the level of area under the curve (AUC) at 88%. In terms of speed of completing the system, the Naïve Bayes Algorithm has a very good rate of speed with completion time of 0.033 seconds.]]>
<![CDATA[Mortality Projection on Indonesia's Abridged Life Table to Determine the EPV of Term Annuity ]]>
<![CDATA[Galuh Oktavia Siswono]]>;
<![CDATA[Ulil Azmi]]>;
<![CDATA[Wawan Hafid Syaifudin]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.1094
<![CDATA[The life insurance industries usually use the Life Table for the valuation process, especially in calculating premiums and policy values of a policy. However, the Life Table is rarely updated; and it may even take years before they are updated. This happens because the insurers believe that the information in the Life Table is still related to the current state of a country and for the next several years. In fact, data and information related to mortality rates in a country are constantly changing and always being updated annually. Therefore, as an approach, researchers use the projection of mortality to approach the mortality rate in the future. Thus, future mortality data can be predicted so that better policies can be made by the governments or insurance industries. In this study, the Abridged Life Table of Indonesia is used in the projection of mortality for both sexes (male and female) of the population in Indonesia. The results of mortality projection are then used to calculate the Expected Present Value (EPV) of a term annuity-due under uniform distribution of deaths (UDD) for several values of and ages. The results obtained show that there is a decrease in the value of the mortality rate in the next few years. Therefore, it can be assumed that there is a possibility for longevity risk to occur in the future.]]>
<![CDATA[Algorithms Error in The VisualGSCA Program ]]>
<![CDATA[Thesa Adi Purwanto]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.1096
<![CDATA[The VisualGSCA program uses an incorrect algorithm, which results in scale inconsistencies between observed and latent variables. The observed variable is standardized, while the latent variable is normalized. This affects the calculation of the wrong estimate parameter value and the goodness-fit value of FIT and AFIT becomes inaccurate. This error occurs because the algorithm used is not a pure GSCA algorithm but a reduced GSCA algorithm that ignores the structural model, resulting in an incorrect FIT value. This study aims to prove that the old version of the GSCA program has problems using its algorithm so that it can affect the results of its statistical calculations. The data used in this study are data from previous studies that have been processed with the old version of the GSCA program, so that the results can be compared with the latest version of the GSCA program. The results obtained prove that there are indeed differences in the value of the Loading Factor and FIT, so that research that has been done previously needs to be reanalyzed using the latest program.]]>
<![CDATA[PLS-SEM Analysis to Know Factors Affecting The Interest of Buying Halal Food in Muslim Students ]]>
<![CDATA[Cindy Cahyaning Astuti]]>
<![CDATA[ Jurnal Varian Vol 4 No 2 (2021) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v4i2.1141
<![CDATA[The increase of halal food products has led to increase in culinary tourism in recent years. One of the districts that has experienced a rapid increase in culinary tourism is Sidoarjo Regency. The development of culinary tourism in the last few years in Sidoarjo Regency generally targets are the students. This study will aim to determine the factors that influence the interest in buying halal food and what factors have the greatest influence on the interest in buying halal food. The analysis technique uses the Partial Least Squares Structural Equation Modeling (PLS-SEM. Based on the results of the analysis, it is known that of the 5 predictor variables used in the analysis process, there are 4 variables that have a significant effect on Purchase Interest (Y). It can be concluded that increasing of Halal Awareness (X1), Halal Certification (X2), Health (X3) and Value Perception (X5) will further increase Purchase Interest (Y). Meanwhile, based on value of coefficient on each variable, it is known that Health (X3) has the largest coefficient value (0.260), so it can be concluded that Health (X3) has the greatest influence on Purchase Interest (Y).]]>
