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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Statistics and Data Science
Alwansyah, Muhammad Arib
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Journal : Journal of Statistics and Data Science

 1 
Survival Analysis of Students Not Graduated on Time Using Cox Proportional Hazard Regression Method and Random Survival Forest Method Arib, Muhammad Arib Alwansyah
Journal of Statistics and Data Science Vol. 2 No. 1 (2023)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v2i1.24312

Abstract

Higher education is a place to educate the next generation of the nation in terms of academic and non-academic. Basically every college tries to maximize the graduation of its students, both in quantity and quality. The undergraduate education program is targeted to complete 8 semesters of study or can also be taken in less than 8 semesters and a maximum of 14 semesters. Many factors are thought to affect the length of student study, both internal and external factors. Based on the factors that are thought to affect the length of study of the student, it is necessary to conduct research to determine what factors have a significant effect on the length of study of the student. The method that can be used to determine these factors is survival analysis using cox proportional hazard regression and random survival forest. Factors that affect the length of study using cox proportional hazard regression is GPA, while by using the random survival forest method, the factors that influence the length of study of students are GPA, gender, and part time. Based on the comparison using the C-Index method, random survival forest is a suitable method to use in the data because the C-Index error value is 26.9% which is smaller than the cox proportional hazard which is 27.8%.
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.
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.
Sentiment Analysis of Twitter User’s Perceptions of the Campus Merdeka Using Naïve Bayes Classifier and Support Vector Machine Methods Salsabilla, Intan; Alwansyah, Muhammad Arib; Nugroho, Sigit; Agwil, Winalia
Journal of Statistics and Data Science Vol. 2 No. 2 (2023)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v2i2.30577

Abstract

The Campus Merdeka program is being implemented by the government to realize autonomous and flexible learning in tertiary institutions to create a learning culture that is innovative, not restrictive, and the needs of students. The Campus Merdeka provides added value and is attractive and provides various responses from the public both directly and on different social media platforms. One of the social media platforms is Twitter. Therefore, research was conducted on the community's response to the Campus Merdeka program on Twitter social media. Twitter documents in the form of community response tweets to the Campus Merdeka program are classified into two categories, namely positive responses and negative responses. The method used in this study is the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) with a Polynomial Degree 2 kernel. The highest level of accuracy resulting from this research is 73.5% with a parameter value of  of 0.5, a constant value  is 0.5, with training data of 309 documents for training data and 132 documents for test data. The accuracy results obtained for the Naïve Bayes Classifier method are 65.9% and for the Support Vector Machine method, an accuracy is 73.5%.
Co-Authors Agwil, Winalia Bayubuana, Muhammad Gabdika Bayubuana Karuna, Elisabeth Evelin Nur Afandi Nurul Hidayati rachmawati, Ramya Rini, Dyah Setyo Salsabilla, Intan Sigit Nugroho