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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Teknik Informatika (JUTIF) Pakem : Jurnal Pengabdian Kepada Masyarakat Economics Development Analysis Journal Journal of Statistics and Data Science
Ni Wayan Surya Wardhani
brawijaya university
Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Multigroup Moderation Test in Generalized Structured Component Analysis Mulyanto, Angga Dwi; Solimun, Solimun; Wardhani, Ni Wayan Surya; Suharno, Suharno
CAUCHY Vol 4, No 2 (2016): CAUCHY
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (603.625 KB) | DOI: 10.18860/ca.v4i2.3491

Abstract

Generalized Structured Component Analysis (GSCA) is an alternative method in structural modeling using alternating least squares. GSCA can be used for the complex analysis including multigroup. GSCA can be run with a free software called GeSCA, but in GeSCA there is no multigroup moderation test to compare the effect between groups. In this research we propose to use the T test in PLS for testing moderation Multigroup on GSCA. T test only requires sample size, estimate path coefficient, and standard error of each group that are already available on the output of GeSCA and the formula is simple so the user does not need a long time for analysis.
Parameter Estimation of Structural Equation Modeling Using Bayesian Approach Sari, Dewi Kurnia; Wardhani, Ni Wayan Surya; Astutik, Suci
CAUCHY Vol 4, No 2 (2016): CAUCHY
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (799.733 KB) | DOI: 10.18860/ca.v4i2.3492

Abstract

Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.
Comparison of Nonparametric Path Analysis and Biresponse Regression using Truncated Spline Approach Azizah, Laila Nur; Rohma, Usriatur; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya; Astutik, Suci
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.26739

Abstract

Nonparametric path analysis and biresponse nonparametric regression are two flexible statistical approaches to analyze the relationship between variables without assuming a certain form of relationship. This study compares the performance of the two methods with the truncated spline approach, which has the advantage of determining the shape of the regression curve through optimal selection of knot points. This study aims to evaluate the best model based on linear and quadratic polynomial degree with 1, 2, and 3 knot points. The model is applied to data with 100 samples and simulated data of various sample levels. The results show that the best model in nonparametric path analysis is a quadratic model with three knots, while the best model in biresponse nonparametric regression is a quadratic model with two knots. Biresponse nonparametric regression has a coefficient of determination of 88.8% which is higher than the nonparametric path analysis of 70.9%. The best biresponse nonparametric regression model is the model with quadratic order and two knots.
COMPARISON OF DBSCAN AND K-MEANS CLUSTER ANALYSIS WITH PATH-ANOVA IN CLUSTERING WASTE MANAGEMENT BEHAVIOUR PATTERNS Zuhdi, Muhammad Rizal; Al Jauhar, Hafizh Syihabuddin; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4183

Abstract

This study aims to compare the effectiveness of DBSCAN and K-Means cluster analysis methods in clustering waste management behaviour patterns in Batu City. The data used is secondary data from previous research with a total of 395 respondents taken using the quota sampling method. DBSCAN classifies data based on density with the main parameters epsilon and MinPts, while K-Means uses the average centroid to determine the cluster. The analysis results show that DBSCAN produces a silhouette index of 0.664, which is higher than K-Means with a value of 0.574. DBSCAN also successfully identified noise as much as 10 data that did not belong to any cluster, while K-Means did not have a similar mechanism. The results of Path-ANOVA show that DBSCAN is the most optimal clustering with a more significant partition difference value. Further tests were conducted to strengthen the validation of clustering results using Path-ANOVA. Both methods produced two main clusters, with the second cluster showing higher quality in terms of maintenance, quality, and ease of use of environmental hygiene facilities. This research emphasises the importance of choosing an appropriate clustering method to ensure optimal clustering results, especially in data with complex characteristics.
Spatial Autocorrelation of East Java's Economic Growth Using Cluster-Based Weights Fitriani, Rahma; Wardhani, Ni Wayan Surya; Abdila, Naufal Shela
Economics Development Analysis Journal Vol. 13 No. 4 (2024): Economics Development Analysis Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edaj.v13i4.19161

Abstract

This study incorporates a spatial clustering technique into the formation of a spatial weight matrix as an alternative to the traditional exogenous matrix, aiming to better capture spatial dependencies. The approach is applied to analyze the spatial autocorrelation of economic growth in East Java’s regencies and municipalities using 2019–2021 data. Spatial clusters are identified based on GDP growth (GGDP), Human Development Index (HDI), population density (Dens), and geographical coordinates. These clusters are used to define a customized spatial weight matrix, where regions within the same cluster are designated as neighbors. Moran’s I, calculated using the customized spatial weight matrix, detects significant spatial autocorrelation in GDP growth for all three years, with consistently lower p-values compared to the traditional contiguity-based matrix. For example, in 2020, Moran’s I using the customized matrix yielded a p-value of 0.099 (significant at the 10% level), while the traditional matrix produced a non-significant p-value of 0.7965. These results demonstrate that spatial clustering extends the scope of spatial interaction beyond adjacent regions to include those with similar characteristics. The findings highlight the effectiveness of this method in providing a more nuanced and robust framework for analyzing spatial dependencies in economic growth.
ZERO INFLATED POISSON REGRESSION MODELS TO ANALYZE FACTORS THAT INFLUENCE THE NUMBER OF MEASLES CASE IN JAVA Weni Utomo, Candra R. W. S; Efendi, Achmad; Wardhani, Ni Wayan Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp721-732

Abstract

Measles is an infectious disease that often occurs in children and is caused by the measles virus (morbillivirus) which can cause death. Thus, it is important to identify the factors that cause measles. The number of measles cases is used as response variable in the form discrete data so that Poisson Regression is commonly used. However, some assumptions are sometimes not met, such as overdispersion and excess zero so that can use Zero Inflated Poisson Regression to meet these assumptions. Because the model can overcome two common characteristics that are often found in count data, which are excess zero and overdispersion. The purpose of this study was to determine the factors that influence the number of measles cases in East Java. The data in the study used secondary data obtained from the Central Statistics Agency (BPS). The predictor variables used were the number of population, percentage of vaccination, percentage of poor people, and percentage of adequate sanitation. The results showed that the data is overdispersed because the variance is greater than the mean. There were four predictor variables, The -value of the total population variable is <0.01, the percentage of vaccinations is 0.914, the percentage of poor people <0.01 and the percentage of proper sanitation is 0.014 so it can be concluded that the percentage of vaccinations has no effect on the number of measles cases and the other three variables affect the number of measles cases in East Java. The best model of affect the number of measles cases in East Java is Zero Inflated Poisson with AIC value 326.24. The ZIP model for measles case in East Java is .
INTEGRATION OF HIERARCHICAL CLUSTER, SELF-ORGANIZING MAPS, AND ENSEMBLE CLUSTER WITH NAÏVE BAYES CLASSIFIER FOR GROUPING CABBAGE PRODUCTION IN INDONESIA Maghfiro, Maulidya; Wardhani, Ni Wayan Surya; Iriany, Atiek
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1057-1070

Abstract

The purpose of this study is to evaluate and compare different clustering techniques, including hierarchical cluster analysis (using complete linkage, average linkage, and single linkage methods), Self-Organizing Maps (SOM) clustering, and ensemble clustering, within the framework of integrated cluster analysis combined with Naïve Bayes analysis, specifically applied to cabbage production in Indonesia. The data utilized in this study are on cabbage production from various districts and cities in Indonesia, obtained from the 2023 publications of the Central Statistics Agency (BPS). The variables used in this study are cabbage harvest, cabbage production, area height, and rainfall. The data size used is 157 districts/cities in Indonesia. This research is a quantitative analysis employing integrated cluster analysis combined with Naïve Bayes. Cluster analysis is used to obtain classes in each district/city. Different clustering methods, including hierarchical clustering, Self-Organizing Map (SOM), and ensemble clustering, are compared to determine the best approach for grouping districts based on cabbage production. Naïve Bayes analysis is then used to classify cabbage production in Indonesia and identify the optimal clusters. This comparison aims to find the most effective clustering method for improving grouping accuracy and understanding cabbage production patterns. The best method for classifying cabbage production in Indonesia is the ensemble clustering approach integrated with Naïve Bayes, resulting in three distinct clusters: high, medium, and low production clusters.
Integration Cluster and Path Analysis Based on Science Data in Revealing Stunting Incidents Marchamah, Mamlu’atul; Fernandes, Adji Achmad Rinaldo; Solimun; Wardhani, Ni Wayan Surya; Putri, Henida Ratna Ayu
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.23570

Abstract

The purpose of this research is to utilize big data to explore the factors that influence the prevalence of stunting in Wajak Regency, model these factors using integrated cluster analysis and` path analysis model, and develop an information system for stunting incidence modeling. This study uses a descriptive and explanative approach, namely using Discourse Network Analysis, cluster analysis, path analysis, and integration of cluster and path analysis. The sample of this research is children under five in Wajak District who were selected using stratified random sampling. The distance measure that has the highest model goodness value in modeling using the integration of cluster analysis with path analysis is the Mahalanobis distance measure. The cluster analysis with Mahalanobis distance produces 3 clusters where cluster one is a toddler who has a low stunting category, cluster two is a group of toddlers who has a moderate stunting category, and cluster three is a group of toddlers who has a high stunting category. The originality of this study is the application of Discourse Network Analysis analysis to obtain new variables followed by a comparison of three distances namely euclidean, manhattan, and mahalanobis in modeling using cluster integration and parametric paths.
STATISTICAL MODELING OF TOURISM INVESTMENT DECISIONS IN INDONESIA USING SEMIPARAMETRIC APPROACH Pratama, Yossy Maynaldi; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya; Nurjannah, Nurjannah; Solimun, Solimun
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0529-0536

Abstract

The tourism potential in Indonesia is very large considering that Indonesia consists of tens of thousands of separate islands. Indonesia has many diverse landscapes, with all its nature wealth and biodiversity in it is an attraction for investors who want to invest in Indonesia. The existence of relationships between variables that are linear and nonlinear, where no nonlinear pattern is known, requires a semiparametric approach. This study aims to apply a semiparametric approach to model people's investment decisions in tourism in Indonesia. The data used is in the form of respondents from investors who invest in tourism in Indonesia from the 2022 National Competitive Basic Research (PDKN) as many as 100 respondents. This study uses the semiparametric path analysis method to model tourism investment decisions in Indonesia. The results show that regulatory variables and investment interest variables have a significant and positive effect on investment decision variables. A diversity coefficient of 60.2% indicates that data diversity can be explained by 60.2% with models, while other variables outside the study explain the remaining 38.8%. In other words, the regulatory variable (X) and the investment interest variable (Y1) can influence the investment decision variable (Y2) by 60.2%.
ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS Sovia, Nabila Ayunda; Wardhani, Ni Wayan Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1237-1248

Abstract

Image classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges during model training and evaluation, potentially leading to biases in favor of the majority pests and reduced accuracy in identifying and classifying the less common ones. This research aims to enhance the classification performance for multiclass data specific to cabbage pests. We propose an ensemble learning approach that combines Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Bagging methods. To address the imbalance issue inherent in cabbage pest data, we employ the Adaptive Synthetic Sampling (ADASYN) resampling technique. The CNN acts as the primary image identifier and classifier for various cabbage pests. Subsequently, the CNN model is integrated into SVM and Bagging models to mitigate the challenges of imbalanced data in pest classification. The research outcomes demonstrate that our ensemble approach, in conjunction with the ADASYN resampling technique, achieves an impressive accuracy rate of 97%, signifying its potential for improved cabbage pest detection and classification.
Co-Authors Abdila, Naufal Shela Achmad Efendi Al Jauhar, Hafizh Syihabuddin Ali Djamhuri Angga Dwi Mulyanto Atiek Iriany Azizah, Laila Nur Chairunissa, Abela Dewi Kurnia Sari Eni Sumarminingsih Fernandes, Adji Fernandes, Adji Achmad Rinaldo Hamdan, Rosita Henny Pramoedyo Iriany , Atiek Maghfiro, Maulidya Marchamah, Mamlu’atul Nurjannah Nurjannah Pratama, Yossy Maynaldi Putri, Henida Ratna Ayu Rahma Fitriani Rohma, Usriatur Rozy, Agus Fachrur Shofa, Elvo Ramadhan Solimun Solimun, Solimun Sovia, Nabila Ayunda Suci Astutik Weni Utomo, Candra R. W. S Wigbertus Ngabu Zuhdi, Muhammad Rizal