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INDONESIA
Jurnal Varian
Published by Universitas Bumigora
ISSN : -     EISSN : 25812017     DOI : https://doi.org/10.30812/varian
Core Subject : Economy, Science, Education, Social,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other
Jurnal Varian adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora. Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal ini terbit 2 (dua) kali dalam 1 tahun pada periode Genap (April) dan Ganjil (Oktober). Jurnal Varian fokus memuat publikasi pada Bidang Matematika dan Statistika.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 168 Documents
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Natural Disaster Mapping on Java Island Using Biplot Analysis Widyangga, Pressylia Aluisina Putri; Mardianto, M. Fariz Fadillah; Pratiwi, Firda Aulia; Putrie, Andi Vania Ghalliyah; Andriani, Putu Eka; Amelia, Dita; Dewi, Deshinta Arrova
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.2634

Abstract

Indonesia is located in the ring of fire region. This condition causes Indonesia to have the potential to experience various disasters, such as volcano eruptions. In addition, rapid population growth has led to rampant land conversions that cause floods, landslides, tornadoes, droughts, and forest fires. The research aims to map natural disasters in Indonesia, especially Java Island to find out the provinces and their natural disasters tendency using Biplot analysis. Based on the results, Central Java, East Java, and West Java have a tendency to have floods and landslides. East Java tends to undergo earthquakes and Central Java has the potential to experience volcano eruptions. Through the natural disasters mapping, the government, especially the BMKG, will be able to find various solutions to overcome the natural disasters that have great potential to occur in provinces in Indonesia, especially Java Island as the manifestation toward SDGs Target 2030.
Stunting Prevalence Modeling Using Nonparametric Regression of Quadratic Splines Handayani, Tutik; Sifriyani, Sifriyani; Rian Dani, Andrea Tri
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.2916

Abstract

The nonparametric regression approach is used when the shape of the regression curve between the response variable and the predictor variable is assumed to be of unknown shape. The advantages of nonparametric regression have high flexibility. A nonparametric regression approach that is often used is truncated spline which has an excellent ability to handle data whose behavior changes at certain sub-sub intervals. The purpose of this study is to obtain the best model of multivariable nonparametric regression with linear and quadratic truncated spline approaches using the Generalized Cross Validation (GCV) and Unbiased Risk (UBR) methods and to find out the factors that influence the prevalence of stunting in Indonesia in 2021. The data used were the prevalence of stunting as a response variable and the predictor variable used was the percentage of infants receiving exclusive breastfeeding for 6 months, the percentage of households that have proper sanitation, the percentage of toddlers who get Early Initiation of Breastfeeding (IMD), the percentage of poor people, and the percentage of pregnant women at risk of SEZ. The results showed that the best quadratic truncated spline nonparametric regression model in modeling stunting prevalence was quadraic truncated spline using the GCV method with three knot points. This model has a minimum GCV value of 7.29 with an MSE value of 1.82 and a R2 value of 94.07%.
A Regression Model of Public Interest in COVID-19 Vaccination Ahead of MotoGP Faiqotul Himmah, Elok; Riana, Riana
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3130

Abstract

The lack of a threshold value for herd immunity against COVID-19 ahead of the MotoGP 2022 Event in the Mandalika Circuit Area, Central Lombok Regency is the reason for the author to conduct this research. The objectives of this research are 1) to make a mathematical model of the interest of the people of Central Lombok Regency in the Covid-19 vaccination in welcoming the 2022 MotoGP Event and 2) to determine the influence of MotoGP Event 2022 and other factors on the interest of the people of Central Lombok Regency in carrying out the COVID-19 vaccination. This research is a type of quantitative descriptive research. The independent variables in this study are the variables of ease of getting the Covid-19 vaccination (X1), the efficacy of the COVID-19 vaccination (X2), trust in the government (X3), and the 2022 MotoGP event (X4), while the dependent variable is the variable of interest in covid-19 vaccination (Y). To achieve the objectives of this study, the authors collected data through a questionnaire that was distributed to 332 respondents, they’re people who received the full vaccine aged 12-70 years. The questionnaire was tested for validity and reliability. Data must first be transformed into interval data using the Method of Successive Interval (MSI) then analyzed using multiple linear regression with classical assumptions including normality, multicolllinearity and heteroskedasticity. The results showed that the 2022 MotoGP event did not have a significant effect on the interest of the people of central Lombok Regency to take part in the COVID-19 vaccination. The biggest factor that influences is the factor of people's trust in the government.
Clustering of Study Program Using of Block-Based K-Medoids Muna, Asa Nugrahaini Itsal; Kariyam, Kariyam
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3181

Abstract

The purpose of this research is to classify Study Programs based on eleven mixed data from InternalQuality Management System (QMS) indicators. This grouping can provide a clearer picture of howQMS affects the performance and quality of study programs. By understanding these clusters, universities can identify and design more effective strategies to improve the quality of education. The dataused comes from the National Accreditation Board for Higher Education (BAN-PT) and the websitedatabase, which consists of seven numerical variables: number of lecturers, percentage of doctors, percentage of professors and associate professors, student enumeration, percentage of graduates, programexperience, and availability of laboratories. Meanwhile, the categorical variable consists of four variables: National Accreditation Board of Higher Education (BAN-PT) research ranking, accreditation,international recognition, and level of community service. The clustering method used is the blockbased k-medoids (block-based KM), and multivariate analysis of variance (MANOVA). We applied theDeviation Ratio Index based on K-Medoids (DRIM) to determine the number of clusters. This researchresults that the optimal number of groups that must be formed is three. Based on MANOVA the resultsshowed that the group consisting of 12 study programs had better QMS outcomes than the other twogroups.
Analyzing Lightning Strike Susceptibility Using the Elliptical Fitting Method with a Principal Component Analysis Approach Lovytaji, Helmalia A.; Rozikan, Rozikan; Kuncoro, Djati C.; Karisma, Ria Dhea Layla Nur
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3183

Abstract

Lightning is a high-current discharge that occurs in Cumulonimbus clouds, with CG (Cloud to Ground)lightning strikes posing significant dangers, especially to human life. Pasuruan, located in the highlandsbetween mountains and the ocean in Indonesia, is particularly vulnerable to such strikes. This studyaims to mitigate the impact of lightning strikes, particularly in industrial areas like Pasuruan, by delineating lightning-prone areas using a sophisticated methodological approach. Our research employs arobust Ellipse Fitting Method, parameterized with Principal Component Analysis (PCA), to accuratelydefine the boundaries of these high-risk zones. The Ellipse Fitting Method, which involves formingan ellipse from the intersection of a plane and a cone, uses five key parameters: a center point, twovertex points, and two focus points. PCA is then applied to these parameters to determine the ellipse’sconfiguration, with the center point derived from the mean of all data points. The major and minoraxes are defined by the first and second eigenvalues of the principal components, respectively. The sizeof the ellipse correlates with the confidence level, with higher confidence resulting in a larger ellipse.The result of integrating these advanced techniques is the generation of two PCA models from datacollected across 28 sub-districts in Pasuruan, with findings indicating a high level of vulnerability inLumbang District and a moderate level of risk in Gempol District. This methodological framework notonly enhances the precision in identifying lightning-prone areas but also provides a scalable approachfor similar studies in other regions. Suggestion for the further research are to overcome extreme pointsor extreme points in the PCA confidence ellipse such as MVEE.
Comparison of Naive Bayes Classification Methods Without and With Kernel Density Estimation Hermawan, Agus; Siswanto, Siswanto; Jaya, Andi Kresna
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3199

Abstract

Halal certification is important to give confidence to Muslim consumers around the world regarding the halalness of products. The Halal Product Assurance Organizing Body (BPJPH) is the official auditor in Indonesia that is responsible for the halal certification process. This study aims to address the need for verification and validation of data for halal certification applications in Indonesia by using the data science approach and machine learning technology. In this study, the Naïve Bayes classification method was used to optimize the data verification and validation process. However, this method needs to be improved by applying optimization methods such as Kernel Density Estimation (KDE) to improve classification results. The results showed that the Naïve Bayes classification method with KDE optimization produced better performance than the Naïve Bayes method without optimization. The performance of the Naïve Bayes classification model without optimization achieves 87.6% Accuracy, 85.4% Recall, 88.8% Precision, and 87.1% Fmeasure. Meanwhile, the Naïve Bayes classification model with KDE optimization achieves 97.5% Accuracy, 95.9% Recall, 98.9% Precision, and 97.8% Fmeasure. Thus, it can be concluded that the Naïve Bayes classification algorithm with KDE optimization results in a performance increase of 9.9% compared to the Naïve Bayes method without optimization. This research has important implications in handling complex and non-normally distributed data and providing solutions for BPJPH in the process of verifying halal certification.
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression Permata, Regita Putri; Ni'mah, Rifdatun; Dani, Andrea Tri Rian
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3202

Abstract

There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
An Implementation of K-Medoids Method in Provincial ClusteringBased on Education Indicator Data Apridayanti, Annisa Zuhri; Fathurahman, M; Prangga, Surya
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3205

Abstract

Data mining is searching for interesting patterns or information by selecting data using specific techniques or methods. One method that can be used in data mining is K-Medoids. K-Medoids is a method used to group objects into a cluster. This research aimed to obtain the optimal number of clusters using the K-Medoids method based on Davies-Bouldin Index (DBI) validity on education indicators data by province in Indonesia in 2021. The results showed that the optimal number of clusters using the K-Medoids method based on DBI validity is 5 clusters. Cluster 1 consists of 1 province with a higher average dropout rate, average length of schooling, and well-owned classrooms compared to other clusters. Cluster 2 consists of 15 provinces with an average proportion of school libraries lower than Clusters 3 and 4 and higher than Clusters 1 and 5. Cluster 3 consists of 9 provinces with an average proportion of school libraries, proportions of school laboratories, net enrollment rates, and higher school enrollment rates than other clusters. Cluster 4 consists of 8 provinces with a higher average enrollment rate than the other clusters. Cluster 5 consists of 1 province with a higher average repetition rate and student-per-teacher ratio than other clusters.
Mathematical Modelling and Simulation Strategies for Controlling Damage to Forest Resources Due to Illegal Logging Naben, Irene R; Bano, Elinora N; Blegur, Fried M. A.
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.3391

Abstract

Forests are one of the natural resources that provide many benefits for the welfare of living things.The dense population causes people to depend more on forest resources. One of them is illegal logging.Various strategies to control forest damage due to illegal logging have been carried out, namely by directhandling to improve damaged conditions while preventing the recurrence of forest damage. The purposeof this research is to build a mathematical model of forest resource damage control strategies due toillegal logging, determine assumptions, formulate the model, and conduct analysis and problem solvingincluding: determining the equilibrium point, determining the stability analysis of the equilibrium point,and conducting numerical simulations of the equilibrium point. The last step is to interpret the results ofthe analysis obtained and make conclusions. Based on the research and simulation results of the model,it can be concluded that taking into account the variable of forest resource damage control strategydue to illegal logging, the result shows that if the density of forest resources has been affected by thedisturbance of population density around the forest, it is necessary to have a forest resource damagecontrol strategy in order to compensate for the people around the forest who do a lot of illegal logging.In order to maintain the forest so that the forest does not quickly become extinct and can overcomedrought, prevent flooding, maintain groundwater quality, protect animals, reduce air pollution, climatecontrol, reduce dust particles, prevent the greenhouse effect, supply natural fertilizers, prevent erosion,and maintain springs.
Mapping of Village Population Profile with Schistosomiasis Cases Using Clustering Large Applications Fajri, Mohammad; Rais, Rais; Gamayanti, Nurul Fiskia; Dg Mabaji, Siti Natazha; Rahman Jati, Shalsa Yunita; Arisandi, Rizwan
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3423

Abstract

Schistosomiasis is a tropical disease caused by Schistosoma mansoni (intestinal schistosomiasis) and Schistosoma haematobium (urogenital schistosomiasis). Schistosomiasis in Indonesia is endemic to Central Sulawesi and is commonly found in the Napu Valley and Bada Valley areas, which are administratively included in Poso District and Sigi District. One approach to obtain information on schistosomiasis endemic areas is by mapping the population profile of villages with schistosomiasis cases. This mapping is intended to provide an overview of the social and demographic conditions of villages with schistosomiasis cases. One of the many analysis methods that can be used is cluster analysis. Cluster analysis is a method for grouping data based on the extent of their similarities. Data with similar characteristics will be grouped together, while data with different characteristics will be placed in different groups. Among several types of methods in cluster analysis is Clustering Large Application (CLARA). CLARA is a clustering method which is more robust to unusual data and can be applied to handle large volumes of data. The results of this study are obtained two optimum clusters, each possessing distinct characteristics as determined by Schistosomiasis cases indicators. Cluster 1 with low schistosomiasis cases and cluster 2 with high schistosomiasis cases.

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