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Etis Sunandi
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esunandi@unib.ac.id
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jsds_statistika@unib.ac.id
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Jl. WR. Supratman Kelurahan Kandang Limun Kota Bengkulu
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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 36 Documents
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Forecasting A Weekly Red Chilli Price in Bengkulu City Using Autoregressive Integrated Moving Average (ARIMA) and Singular Spectrum Analysis (SSA) Methods Putriasari, Novi; Nugroho, Sigit; Rachmawati, Ramya; Agwil, Winalia; Sitohang, Yosep O
Journal of Statistics and Data Science Vol. 1 No. 1 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Red chili occupies a strategic position in the Indonesian economic structure because its use applies to almost all Indonesian dishes. Therefore, controlling the price of red chili is a necessity to maintain national economic stability. The purpose of this research is to forecast a red chili weekly price using ARIMA and SSA based on the weekly data of chili prices from January 2016 - December 2019 sourced from Statistics Indonseia (BPS) Branch Office of Bengkulu Province. The data have been analyzed using software R. Based on MAPE, ARIMA K (2,1,2) provides the best forecasting with value 0.49% while SSA 10.64%.
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.
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.
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.
Forecasting Export Value of Bengkulu Province Through Pulau Baai Harbour with ARIMA, ANN, and Hybrid ARIMA-ANN Approach Lestari, Wina Ayu; Nugroho, Sigit; Widodo, Fanani Haryo Widodo
Journal of Statistics and Data Science Vol. 3 No. 1 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i1.41289

Abstract

Forecasting is a process of predicting future events based on past event data. One of the time series models that can be used for forecasting is the Autoregressive Integrated Moving Average (ARIMA). The advantages of ARIMA are in the accuracy and flexibility of its forecasting in representing several different types of time series, but the main limitation is the linear form of the model which causes ARIMA to be unable to capture non-linear patterns in the data. An alternative model for time series modeling is Artificial Neuron Network (ANN). ANN can overcome the weaknesses of ARIMA, but cannot handle linear and nonlinear patterns of the data simultaneously. As an effort to improve forecasting accuracy, Hybrid ARIMA-ANN is carried out by taking advantage of the supremacy of ARIMA and ANN. This study aims to obtain the best model for forecasting the export value of Bengkulu Province, a model generated by the time series data of export values issued by Pulau Baai Harbour from January 2014 to June 2022. The result shows that the best model for predicting the export value of Bengkulu Province is the ARIMA-ANN hybrid model with MAAPE of 0.5289 and MASE of 0.7664.

Page 4 of 4 | Total Record : 36

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