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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. 1 No. 2 (2022)" : 5 Documents clear
Price Prediction Using ARIMA Model of Monthly Closing Price of Bitcoin Pratama, Apriliyanus Rakhmadi
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.22689

Abstract

The rising of bitcoin’s user as a digital currency and investments causing an instability and an uncertainty in price movement and increasing the risk of trading, therefore in this study we try to forecast the future value of bitcoin price using ARIMA Models. 2 candidate models are selected by the lowest value of AIC and using the performance indicators ME, RSME, MAE, MPE, and MAPE conclude ARIMA (1,1,0) are the best ARIMA model, then the next 5 months future price forecasted using the best model. While ARIMA (1,1,0) is the best model, the model failed to follow price movement as shown in the forecasted price.
Integration Cluster and Path Analysis Based on Science Data in Revealing Stunting Incidents Marchamah, Mamlu’atul; Fernandes, Adji Achmad Rinaldo; Solimun; Wardhani, Ni Wayan Surya; Putri, Henida Ratna Ayu
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.23570

Abstract

The purpose of this research is to utilize big data to explore the factors that influence the prevalence of stunting in Wajak Regency, model these factors using integrated cluster analysis and` path analysis model, and develop an information system for stunting incidence modeling. This study uses a descriptive and explanative approach, namely using Discourse Network Analysis, cluster analysis, path analysis, and integration of cluster and path analysis. The sample of this research is children under five in Wajak District who were selected using stratified random sampling. The distance measure that has the highest model goodness value in modeling using the integration of cluster analysis with path analysis is the Mahalanobis distance measure. The cluster analysis with Mahalanobis distance produces 3 clusters where cluster one is a toddler who has a low stunting category, cluster two is a group of toddlers who has a moderate stunting category, and cluster three is a group of toddlers who has a high stunting category. The originality of this study is the application of Discourse Network Analysis analysis to obtain new variables followed by a comparison of three distances namely euclidean, manhattan, and mahalanobis in modeling using cluster integration and parametric paths.
Measles Disease Analysis in Bengkulu Province Using Zero Inflated Poisson Regression and Zero Inflated Negative Binomial Regression Azagi, Ilham
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.24028

Abstract

Zero Inflated Poisson regression (ZIP) and Zero Inflated Negative Binomial (ZINB) regression were used if there was overdispersion and no multicollinearity in the data. This study aims to analyze measles in Bengkulu Province using the ZIP and ZINB regression models. Among them are selecting the best model, seeing the influential variables from the best model, and predicting the results of the best model. The data used is one dependent variable, namely the number of measles cases (Y) in each puskesmas and six independent variables (X) namely the percentage of measles immunization, the amount of malnutrition, the percentage of exclusive breastfeeding, the percentage of vitamin A, the percentage of proper sanitation, and the percentage of healthy house. The results of this study, the ZIP regression model formed is a discrete model for , namely ln()=-5.042-0.007X1-0.014X3+0.094X4 and a zero inflation model for , namely logit()= -3.656+0.101X4-0.054X6, while the ZINB regression model formed is a discrete model for , namely ln()=-9,289+0.120X4 and a zero inflation model for , namely logit()=- 17.841+0.205X4. The AIC value of the ZINB regression model is 255.249, which is smaller than the AIC value of the ZIP regression model of 331.467, so the ZINB regression model is better to use. The influential variable in this study is the percentage of vitamin A administered. There is not much difference between predicted results and the actual data.
Achievement Cluster of Covid-19 Vaccination at the South Bengkulu Health Center Using Agglomerative Hierarchical Clustering Sari, Devni Prima; Sumita, Nurmaya
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.24082

Abstract

The concerns of many people and the lack of vaccine information are significant obstacles to achieving the Covid-19 vaccination target. The government and health groups must be ready to provide correct vaccine information to reduce public doubts. To evaluate the vaccine implementation, this is necessary to cluster the area regarding the achievement of the vaccination target. Clustering this area can be done using the Agglomerative Hierarchical Clustering method. In this study, clustering was carried out using Covid-19 vaccination data at the South Bengkulu Health Center involving six variables. Three clusters were formed for the clustering process: the first dose of Covid-19 vaccination, the second dose of Covid-19 vaccination, and the first Booster vaccination. Each cluster is represented by low, medium, and high clusters
Modeling of Tuberculosis Cases in Sumatera Region using Poisson Inverse Gaussian Regression -, She Asa Handarzeni
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.24453

Abstract

In the Sumatra Region, tuberculosis (TB) is a disease that needs special attention because it tends to increase every year. Based on health theory, there are many factors that cause TB, but it is not easy to determine which factors have a significant effect. Therefore, in this study an analysis was carried out that could model, predict, and determine the factors causing TB disease in the Sumatra Region. The data used is data on TB cases in the Sumatra Region in 2018 taken from the Publication of the Central Statistics Agency. Poisson regression is an analysis that is suitable for modeling count data such as TB disease data. The assumption of Poisson regression is that the mean and variance of the response variables must be equal (equidispersion). However, the TB case data in the Sumatra Region in 2018 has an average value that is smaller than the variance (overdispersion) so it cannot be solved by Poisson regression. To overcome this problem, we need a method that can overcome overdispersion, namely Poisson Inverse Gaussian (PIG) ​​regression. From the results of the analysis using PIG regression, it can be concluded that the factors that have a significant effect on TB cases in the Sumatra Region are the percentage of the male population (X1), the percentage of the productive age population (X2), the percentage of households with a floor area of ≤ ​​19m2 (X3), and the percentage of households that have access to proper sanitation (X4), where the model formed is Based on the model, the predicted results of TB cases in the Sumatra Region had an average of 596.04178 where the lowest cases occurred in Pringsewu of 154.8943 and the highest cases occurred in Bukittinggi of 2719.59400.

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