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JASDS: Journal of Applied Statistics and Data Science
Published by Universitas Brawijaya
ISSN : -     EISSN : 30484391     DOI : https://doi.org/10.21776/ub.jasds
Core Subject : Science, Education,
Computer Science & IT Mathematics
JASDS : Journal of Applied Statistics and Data Science (e-ISSN: 3048-4391) is a journal managed by Universitas Brawijaya , Malang, Indonesia, and associated with FORSTAT (Forum Pendidikan Tinggi Statistika) which is published twice a year (in March and October). The objectives of Journal of Applied Statistics and Data Science are to publish and disseminate high quality of original research papers about the application of statistics and data science in many areas, or case driven theoretical development of statistics and data sciences. The journal covers the following topics: Experimental Design, General Linear Model and Generalized Linear Model, Bayesian, Time Series, Spatial, Econometrics, Big Data, Machine Learning, Panel Model, Computational Statistics, Operation Research, Actuarial and Finance, Statistical Quality Control, and related topics. Upon its submission, the Editor in Chief decides on the suitability of the paper’s content for the aim and scope of JASDS. If the Editor in Chief considers the paper is suitable, then the paper will be sent for peer reviewing by two peer reviewers. Journal of Applied Statistics and Data Science maintains double anonymity, so neither the peer reviewers nor the author(s) can be identified by one another. The peer reviewers are the respectful scholars of the areas.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 31 Documents
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Application of Path Analysis to Determine the Effect of Self-Efficacy and The Role of Teachers on Work Readiness of Vocational High School Students Through Industrial Work Practice Experience
Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2025.002.01.3

Abstract

Path analysis is one of the statistical methods that is a further development of regression analysis. Path analysis aims to determine the direct and indirect effects of a variable on other variables. Vocational High Schools (SMK) are educational institutions that prepare students to enter the world of work. Therefore, SMK aims to encourage the development of students' learning skills, both in terms of knowledge, skills, and attitudes to support the development of their potential. The purpose of this study was to determine the effect of self-efficacy and the role of teachers on the work readiness of vocational students through industrial work practice experience. Data were obtained through distributing questionnaires with a Likert scale. The population in this study were students of SMK Islam Donomulyo grade XII in the 2022/2023 school year while the sample in this study were students of SMK Islam Donomulyo grade XII who had carried out industrial work practices. The results showed that the variables of self-efficacy and the role of the teacher had a significant effect on the variable of student work readiness through industrial work practice experience and the variable that had the greatest effect was the role of the teacher on student work readiness through industrial work practice experience.
T² Hotelling Control Chart with Bootstrap Method for Paper Production Quality Control
Journal of Applied Statistics and Data Science Vol. 1 No. 2 (2024): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2024.001.02.1

Abstract

A multivariate control chart is a control chart used when there is more than one quality characteristic in an inspection.  Hotellingcontrol chart is one of the statistical tools used to monitor multivariate shifts in process mean using the mean vector and covariance matrix.  Hotelling control chart with the bootstrap method represents an alternative approach to dealing with multivariate non-normal data. This approach does not require specific assumptions such as the multivariate normal assumption, which are typically required by other methods. This study uses the bootstrap method with a different algorithm, the aim is to find the smallest ARL value. The method was applied to secondary data on quality characteristics of paper pulp with three variables: pH, moisture content, and brightness. The results show that the bootstrap control chart on the statistical value has an ARL value of 6.24 while the bootstrap control chart on the observation sample has an ARL value of 8.33.
Mixed Geographically Weighted Regression Modeling on Gross Regional Domestic Product of Districts/Cities in Central Java
Journal of Applied Statistics and Data Science Vol. 1 No. 2 (2024): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2024.001.02.6

Abstract

Mixed Geographically Weighted Regression (MGWR) is a combined model between multiple linear regression models and GWR models. So that the Mixed GWR model will produce global parameter estimates and other parameters are local according to the location of observation. The purpose of this study is to form a Mixed Geographically Weighted Regression (MGWR) model with the best weights formed in modeling district / city GRDP data in Central Java and identify variables that significantly affect district / city GRDP in Central Java. Data on HDI, Percentage of Poor Population, TPT, TPAK, and UMR are used as predictor variables to explain district/city GRDP in Central Java obtained through the website of the Central Java BPS publication. Modeling uses the best weight obtained through the minimum Cross Validation (CV) value, namely the fixed tricube kernel function weight. The results of the model formed using the Mixed Geographically Weighted Regression (MGWR) method with the selected optimum weighting function fixed tricube are different for each district / city in Central Java and produce variables of the percentage of poor people, the open unemployment rate, the labor force participation rate and the regional minimum wage have a localized nature of a region that is significant to the model, then there are no global variables that are significant to the model.
Combined Conjoint Analysis and Cluster Analysis in Segmentation of College Student Housing Preferences: (Study of Statistics Students at Universitas Brawijaya Class of 2020-2023)
Journal of Applied Statistics and Data Science Vol. 1 No. 2 (2024): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2024.001.02.2

Abstract

Housing is a primary need for students who study outside their hometown. In choosing a place to live, students are faced with various choices. The aim of the research is to determine the main factors that influence students preferences in choosing a place to live using conjoint analysis and segmentation produced based on the results of preferences using cluster analysis. The research uses primary data taken using an online questionnaire from undergraduate students at the Department of Statistics, FMIPA UB Class of 2020 – 2023 who have chosen a place to live/rental housing. The number of samples was determined using a purposive sampling technique of 100 respondents. There are seven attributes used, namely Type of Residential, Distance to University, Rental Price, Residential Regulations, Residential Location, Residential Facilities, and Payment Period. The research results show that the most important attribute in determining preferences for choosing housing/rental housing by students is the Rental Price attribute with a level of less than IDR 1,000,000 per month. The combination of respondent level preferences is the type of boarding house, distance to campus close (around 1 – 3 km), rental price less than IDR 1,000,000 per month, residential regulations have no curfew, residential location is located in a rental residential area, complete residential facilities (Wi-Fi, private bathroom, parking, air conditioning, study table), and monthly payment period. After conducting cluster analysis, six segments were formed. The most important attributes in determining preferences for each segment are the residential facility attributes, rental price, distance to campus, type of housing, and residential regulations.
Implementation of ST-DBSCAN Algorithm to Cluster Regency / City in West Java Province Based on Natural Disaster Occurrence in 2020-2022
Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2025.002.01.4

Abstract

Natural disaster is an occurrence that threatens and disrupts the lives of living things caused by natural factors and causes environmental damage, casualties, and so on. One of the regions in Indonesia that experienced natural disasters with high intensity in the 2020-2022 timeframe is West Java Province. The purpose of this study is to group regency/city in West Java Province based on the occurrence of natural disasters in 2020-2022 and the characteristics of each cluster formed. This research uses spatio temporal data applied to the Spatio Temporal-Density Based Clustering of Application with Noise (ST-DBSCAN) algorithm using 3 variables, namely the incidence of floods, landslides, and tornadoes in West Java Province in 2020-2022. This study uses the parameters epsilon 1 of 10, epsilon 2 of 30, and minimum points of 3 using the K-NN algorithm to form clusters from the ST-DBSCAN algorithm. The results showed that 2 clusters and 11 noise points were formed with a silhouette coefficient value of 0.5176, where cluster 1 consisted of 66 regency/city, cluster 2 consisted of 4 regency/city and 11 regency/city were included in the noise.
Use of Hierarchical Clustering Method with Complexity Invariant Distance on Provincial Rice Prices in Indonesia
Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2025.002.01.5

Abstract

Cluster analysis of time series data used to group objects based on their characteristics or data patterns. Rice is one of the main food commodities for the people of Indonesia, so it is necessary to monitor and control rice prices by the government by considering the characteristics of each province. This study aims to determine the characteristics of each province based on data patterns in clusters formed based on rice prices using time series data cluster analysis. The data used is monthly data on rice prices in 34 provinces in Indonesia from January 2020 - December 2022. Clustering of time series data is done by hierarchical cluster analysis with agglomerative methods consisting of single linkage, complete linkage, average linkage, ward's method, and centroid method. The distance used for cluster analysis of time series data is Complexity Invariant Distance (CID). Determination of many clusters using the silhouette coefficient and measurement of accuracy using the cophenetic correlation coefficient. The results of clustering provinces in Indonesia based on rice prices resulted in 3 clusters. The first cluster consists of 1 province categorized as a cluster with high rice prices and the highest complexity value, the second cluster consists of 12 provinces categorized as a cluster with high rice prices and the lowest complexity value, and the third cluster consists of 21 provinces categorized as a cluster with low rice prices and low complexity value.
Multinomial Logistic Regression Analysis in Determining Factors That Affect Social Media Selection Among Students of Faculty of Mathematics and Natural Science, Universitas Brawijaya
Journal of Applied Statistics and Data Science Vol. 1 No. 2 (2024): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2024.001.02.3

Abstract

Logistic regression is a statistical analysis that focuses on explaining the relationship between categorical scale response variables that have two or more categories and one or more predictor variables. Multinomial logistic regression is a logistic regression analysis where the response variable has more than two categories. The multinomial logistic regression method does not require a classic assumption test but only requires a non-multicollinearity assumption test. The aim of this research is to form a multinomial logistic regression model and identify factors that influence social media selection among students at the Faculty of Mathematics and Natural Sciences, Universitas Brawijaya. The sampling technique used is proportional stratified random sampling and the sample size was 100 undergraduate students of Faculty of Mathematics and Natural Sciences, Universitas Brawijaya. The response variable consists of three categories, namely X (Twitter), Instagram, and TikTok, with category X (Twitter) as a reference. The results of partial parameter testing show that four variables give a significant effect in choosing Instagram over X (Twitter), namely gender, purpose of use, frequency of use, and influence of friends; meanwhile, three variables give a significant effect in choosing TikTok over X (Twitter), namely purpose of use, ease of use, and type of content. The model classification accuracy based on confusion matrix and 100% - APER value is 67%.
Ordinal Logistic Regression Analysis on Consumer Satisfaction Levels in Internet Usage
Journal of Applied Statistics and Data Science Vol. 1 No. 2 (2024): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2024.001.02.4

Abstract

Communication technology has advanced rapidly, enabling individuals to communicate more efficiently via the Internet. The increasing reliance on Internet services requires companies to thoroughly understand the factors influencing customer satisfaction to enhance service quality and sustain competitiveness. This research aims to analyze the level of consumer satisfaction in using internet services at PT X. The sampling technique used was stratified area random sampling with the number of samples taken as many as 90 active consumers. The results of partial ordinal logistic regression model parameter testing show that the variables of internet access speed, source of use, location and service quality have a significant effect. An internet access speed of up to 12 Mbps can increase consumer satisfaction by 42,194 times greater than an internet access speed of only 2 Mbps. Sources of internet use from recommendations from friends/relatives can increase consumer satisfaction by 12,210 times greater than sources of use from self-search. Strategic locations can increase consumer satisfaction by 9,718 times greater than non-strategic office locations. Then, good service quality can increase consumer satisfaction by 55,249 times greater than bad service quality.
Comparison of Distances in K-Medoids and CLARA Clustering Algorithms for Grouping Regencies/Cities in Java Island Based on Human Development Index Indicators
Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2025.002.02.1

Abstract

Cluster analysis is a multivariate analysis aimed at grouping objects based on specific shared characteristics of the objects under study. This research aims to determine the clustering results of regencies/cities in Java Island using the k-medoids and Clustering Large Applications (CLARA) methods with three distance measures: Euclidean, Manhattan, and Minkowski, based on the Human Development Index (HDI) indicators. The data used in this study is the 2023 HDI indicator data consisting of 4 variables and 119 regencies/cities. Based on the analysis, the combination of the CLARA method and Euclidean distance has the highest silhouette coefficient value of 0.380, resulting in clusters with members of 39, 26, 43, and 11 regencies/cities respectively. This indicates that, in this case, the CLARA method is better suited for use, aligning with the initial theory that this algorithm is more appropriate for data with a minimum of 100 observations. Governments in the regencies/cities of Java Island can use the clustering results to help design more targeted and effective development programs for each region.
Fraud Detection in Financial Statements Using Intervention Analysis: (Case Study: Financial Statement Bank Syariah Indonesia Period 2015–2022)
Journal of Applied Statistics and Data Science Vol. 1 No. 2 (2024): Journal of Applied Statistics and Data Science
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jasds.2024.001.02.5

Abstract

Fraud in financial statements poses significant risks as it is crucial for maintaining trust. Intervention Analysis examines key financial components like Accounts Receivable, Inventory, Sales, and Profit to identify anomalies and enhance fraud detection accuracy. Our study analyzes data from 2015 to 2022 to detect potential fraud in Bank Syariah Indonesia (BRIS) financial reporting. Steps include collecting data for each component, calculating descriptive statistics, visualizing trends and fluctuations, identifying intervention points affecting data, grouping outliers from intervention analysis, investigating significant changes or anomalies, and reporting findings. Outliers of Level Shift (LS) and Additive Outlier (AO) types indicate potential fraud. LS outliers suggest sharp data shifts, while AO outliers show deviations from the pattern. Significant increases in Accounts Receivable, Inventory, Sales, and Profit in 2019-2022 warrant further investigation. The presence of LS and AO outliers suggests potential fraud. Sharp shifts in data during specific years, notably in Accounts Receivable and Inventory (2019, 2020), and sudden spikes in Sales and Profit indicate suspicious activities warranting further investigation.

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