cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Etis Sunandi
Contact Email
esunandi@unib.ac.id
Phone
6281295949261
Journal Mail Official
jsds_statistika@unib.ac.id
Editorial Address
Jl. WR. Supratman Kelurahan Kandang Limun Kota Bengkulu
Location
Kota bengkulu,
Bengkulu
INDONESIA
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. 2 No. 2 (2023)" : 5 Documents clear
Earthquake Clustering Using the CLARA Method and Modeling Using the Inhomogeneous Spatial Cox Processes Method in the Ambon Region: Earthquake Clustering Using the CLARA Method and Modeling Using the Inhomogeneous Spatial Cox Processes Method in the Ambon Region Meiwidian, Muhamad Iqbal; Crisdianto, Riki; Rini, Dyah Setyo
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.v2i2.30249

Abstract

Earthquakes are natural events whose time and place cannot be predicted. Ambon is the largest city in the Maluku Islands region and is the center of development and the capital of Maluku Province. This research will group earthquake events, analyze the characteristics of earthquake events, create earthquake zones and map them using CLARA cluster analysis, and create modeling that will look at the risk of earthquake events in a location based on distance to faults and subduction zones using the Inhomogeneous Neyman-Scott Cox Process. The data used is data on earthquake events in the Ambon region obtained from the United States Geological Survey (USGS) catalog from January 1926 to December 2022, with a depth of ≤360.1 Km and a magnitude of ≥4 Mw. Grouping earthquake events in the Ambon area using CLARA cluster analysis obtained 2 groups of earthquake clusters with an optimal silhouette score of 0.7430. The model obtained in this earthquake research is not good because it is based on the K-function value plot of the original data which is far from the modeling K-function value plot.
Modeling the Open Unemployment Rate of Regency/City in West Java Province in 2021 using Spatial Autoregresive Moving Average and Spatial Durbin Model hermalia, Lia
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.v2i2.30380

Abstract

The Open Unemployment Rate is an important indicator to see the non-absorption of labor by the labor market. According to statistic indonesia, in August 2021, the Open Unemployment Rate in Indonesia was 6.49% or around 9.1 million people a population of 50 million, West Java Province has a high unemployment rate, reaching 9.82%. When examined, the open unemployment rate in West Java tends to cluster higher in the west and lower in the east, indicating that there are spatial factors in the data. Therefore, an analysis was conducted involving the variables of Labor Force Participation Rate, Expected Years of Schooling, and Expenditure per capita as independent variables in measuring their influence on the Open Unemployment Rate, the methods used Spatial Autoregressive Moving Average and Spatial Durbin Model. The result shows that both methods are significant in all tests conducted, then the best method is chosen by comparing the AIC value, it is obtained that the best method in modeling the Open Unemployment Rate in West Java Province is the Spatial Durbin Model with Rsquared of 81.32%. Indicating that the independent variables have a significant effect of 81.32% while 18.68% is influenced by other variables not examined.
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%.
Poverty Modeling in Indonesia using Geographically and Temporally Weighted Regression (GTWR) Supianti, Filo
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.v2i2.32644

Abstract

Poverty is a big problem that must be resolved by the government and the people of Indonesia. Various programs are designed and implemented to alleviate poverty in Indonesia. Research is needed to find out what factors influence the problem of poverty. One statistical method that can be used to analyze this effect is the geographically and temporally weighted regression (GTWR) method. This method combines the effects of spatial and time simultaneously. The formation of the model begins with determining the weighting matrix. In determining the weighting matrix, a fixed kernel function is used where the bandwidth value for each location and time of observation is the same. Weighting matrix with kernel functions used are gaussian, bi-square, exponential and tricube kernel functions. The selection of the best model is done by comparing the GTWR model from each of the weighting matrices of the four kernel functions. The best model is determined by looking at the largest R2 value and the smallest AIC. Based on the results of the data processing, the GTWR model with the weighting matrix of the exponential kernel function has the largest R^2=71,05% value and the smallest AIC=718,5934. Variables that have a significant effect on the model differ in each location and time of observation. Significant predictor variables were determined by comparing the values of t and values t in statistic . The predictor variable is significant when t values  are bigger than values t in statistic. The results of data analysis show that the variable life expectancy (UHH) has an influence in most provinces in Indonesia in each year of observation.
An Analysis of Factors Contributing to Extended Study Duration Among Students of the Faculty of Mathematics and Natural Sciences, University of Bengkulu Using Binary Logistic Regression Wahyuliani, Indah; Novianti, Pepi
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.v2i2.41287

Abstract

Logistic regression is a statistical method used to analyze the relationship between a dichotomous dependent variable and one or more independent variables, which may be numerical or categorical. In this study, binary logistic regression is applied to identify the factors influencing the study duration of students in the Faculty of Mathematics and Natural Sciences at the University of Bengkulu. These factors include both internal and external elements, such as cumulative GPA (Grade Point Average), gender, parents’ occupation, scholarship status, and university admission pathway. The results show that GPA significantly affects the length of study, with an odds ratio of 1102.13, indicating that each one-unit increase in GPA greatly increases the likelihood of graduating on time. This study suggests the use of additional statistical techniques, such as bootstrapping, to enhance parameter estimation accuracy and recommends reporting effect sizes, such as odds ratios, for a more comprehensive interpretation of the relationship between independent and dependent variables.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2025) Vol. 3 No. 2 (2024) Vol. 3 No. 1 (2024) Vol. 2 No. 2 (2023) Vol. 2 No. 1 (2023) Vol. 1 No. 2 (2022) Vol. 1 No. 1 (2022)