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Etis Sunandi
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Journal of Statistics and Data Science
Published by Universitas Bengkulu
ISSN : -     EISSN : 28289986     DOI : https://doi.org/10.33369/jsds
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other
Established in 2022, Journal of Statistics and Data Science (JSDS) publishes scientific papers in the fields of statistics, data science, and its applications. Published papers should be research-based papers on the following topics: experimental design and analysis, survey methods and analysis, operations research, data mining, machine learning, statistical modeling, computational statistics, time series, econometrics, statistical education, and other related topics. All papers are reviewed by peer reviewers consisting of experts and academics across universities and agencies. This journal publishes twice a year, which are March and October.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2024)" : 5 Documents clear
Factors Affecting The Open Unemployment Rate in West Sumatra Province Using Spatial Autoregressive (SAR) Adellia, Clara Febby; Sari, Devni Prima
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.36514

Abstract

This paper proposes a Spatial Autoregressive (SAR) model to analyze the significant factors affecting the open unemployment rate in West Sumatra during 2023. The main advantage of the method is its ability to accurately capture spatial interactions between neighboring regions, such that it can provide a comprehensive understanding of regional unemployment patterns efficiently. By introducing the K Nearest Neighbor (KNN) weighting matrix and spatial lag parameter to the model, the effect of regional proximity on unemployment rates is more accurately captured. The viability of the SAR model is assessed by analyzing its ability to produce the lowest Akaike’s Information Criterion (AIC) value, indicating its suitability for modeling regional unemployment patterns. The result indicates that the SAR model is more effective than the multiple linear regression model in capturing regional unemployment patterns, with an AIC value of 52.756. The factors that influence the open unemployment rate are gross regional domestic product, labor force participation rate and the percentage of poor people.
Analysis of the Quality of Health Service at the Air Haji Hearth Center Using the Ordinal Logistics Regression Method Soleha, Annisa; Sari, Devni Prima
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.36516

Abstract

Improving the quality of public services has become a major concern in government agencies as an effort to provide optimal public services. The quality of service can be affected by various factors. Therefore, it is necessary to conduct an analysis to find out the relationship between factors that affect service quality and service quality itself. Efforts are made to analyze the relationship between factors that affect service quality and service quality itself by using the ordinal logistic regression method in analyzing the relationship between influencing factors and influencing factors. This type of research is applied research that begins with theoretical analysis and data collection then ordinal logistic regression analysis. Based on the results of data analysis, it was found that the variables that significantly affected the quality of service were direct evidence variables, guarantee variables, and empathy variables. This research is useful for the Air Haji health center in an effort to improve the quality of health services.
The Disparity of Maternal and Neonatal Death Modeling in Sumatra Region Using Geographically Weighted Bivariate Negative Binomial Regression Bayubuana, Muhammad Gabdika Bayubuana; Nugroho, Sigit; Rini, Dyah Setyo; Alwansyah, Muhammad Arib
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.41285

Abstract

The Sumatra region occupies the second highest rank in terms of Maternal Mortality Rate (MMR) and Neonatal Mortality Rate (NMR) in Indonesia in 2020. Many factors are thought to have influenced these two cases, both directly and indirectly. So it is necessary to do an analysis to find out what factors influence MMR and NMR. The methods that can be used to determine these factors are Bivariate Negative Binomial Regression (BNBR) and Geographically Weighted Bivariate Negative Binomial Regression (GWBNBR). The results of the analysis show that the Deviance Information Criterion (DIC) in GWBNBR is smaller than BNBR, so GWBNBR is better than BNBR in modeling MMR and NMR in the Sumatra Region in 2020.
Analysis of Factors Influencing Young Voters in the 2024 Bengkulu Province Regional Head Election: A Quantitative Study Using Chi-Square and Fisher Exact Test Approaches Sonia, Gita; Wanti, Novelisa Syendra; Damayanti, Putri; M. Zidan Mezilano Y; Sunandi, Etis
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.41286

Abstract

Political participation of young voters has a strategic role in local democracy. This research aims to analyze the factors inhibiting young voters' participation in the 2024 Bengkulu regional election using Chi-Square analysis. This descriptive-analytic quantitative research involved 124 respondents aged 17-29 in the 2024 Bengkulu regional election, analyzing four variables: political knowledge, psychological factors, social environment, and technical constraints. Using the Fisher Exact test, the results showed no significant relationship between the variables and the voting decision. Interestingly, the participation rate of young voters is quite high, indicating the complexity of the dynamics of political participation of the younger generation. The research findings provide a new perspective on the dynamics of young voters' political participation, providing a foundation for the development of strategies to increase young people's political engagement in future elections.
A Panel Data Spatial Regression Approach for Modeling Poverty Data In Southern Sumatra Hidayati, Nurul; Karuna, Elisabeth Evelin; Alwansyah, Muhammad Arib
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.41288

Abstract

This research examines the use of spatial panel data regression approach to model poverty data in the Southern Sumatra region. The main objective of the study is to model poverty in the Southern Sumatra region using spatial panel data regression. Panel data from districts/cities in South Sumatra, Jambi, Lampung, Bengkulu, and Bangka Belitung during the 2015-2021 period were used in the analysis. The spatial panel models used in this study are panel SAR regression and panel SEM. The results show that the spatial panel data approach is better at explaining variations in poverty levels compared to non-spatial models. A significant spatial spillover effect was found, where the poverty level of an area is influenced by the conditions of its neighboring areas. The results of the analysis show that the best model to use in modeling the Poverty Percentage data in the Southern Sumatra region is the Spatial Autoregressive Fixed Effect (SAR-FE) model based on the smallest AIC and BIC values. Factors such as average years of schooling and life expectancy are proven to have a significant influence on the percentage of poverty in the SAR Fixed Effect model.

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