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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. 2 No. 1 (2023)" : 5 Documents clear
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%.
Applied Different Pixel Selection in METRIC Model for Estimating Spatial Daily Evapotranspiration of Oil Palm in East Kalimantan Province, Indonesia Dhohir, Nur Muhammad Abdul; June, Tania
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.v2i1.24805

Abstract

Determination of evapotranspiration (ET) plays a key role in managing water in oil palm plantations. Several energy balances models have been developed for mapping evapotranspiration regionally. Subsequently, this study aims to estimate daily evapotranspiration in oil palm plantation using the METRIC model, where pixel selection used and corrected by hot and cold pixels. The climate data were collected from ERA-5 Reanalysis and Landsat 8 was used for spatial analysis. The result depicts the means ± standard deviation of ET without pixel selection (with pixel selection), specifically for oil palms age of 4, 6, 7, 8, 9, 11, 12 and 13 years were 3.19 ± 1.62 mm d-1, 3.31 ± 1.14 mm d-1, 4.01 ± 0.96 mm d-1, 4.84 ± 0.87 mm d-1, 6.29 ± 0.43 mm d-1, 5.72 ± 0.44 mm d-1, 6.43 ± 0.23 mm d-1 and 6.21 ± 0.33 mm d-1 (4.22 ± 0.49 mm d-1, 3.99 ± 0.22 mm d-1, 2.96 ± 0.34 mm d-1, 3.14 ± 0.33 mm d-1, 4.22 ± 0.49 mm d-1, 3.99 ± 0.22 mm d-1, 4.26 ± 0.24 mm d-1 and 4.18 ± 0.30 mm d-1), respectively. We have found more accurate ET determination with pixel selection (higher coefficient of determination).
Goodness Test of Adaptability to Model of Technical Changes and Test of Forecasting Accuracy susiawati, susiawati; Kurniawan, Budi
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.v2i1.27257

Abstract

The technical coefficient input-output as an element of the technical coefficient matrix (A) is estimated to have good forecasts for the next several periods . By substituting the final demand (F) for the period into the Input Output (IO) model in the equation the total output for the period will be obtained from the forecasting results. The total output of forecasting results is then compared with the actual total output to see the magnitude of the deviation. In the regression equation, the coefficient of determination is a measure of “goodness of fit” which states how well the regression line explains the independent variable with the dependent variable. The test is carried out by regressing the technical coefficient of input-output in the year against the technical coefficient in the nth year in a simple linear regression equation . This test was conducted to see the validity of the technical coefficients in forecasting the IO model. This research is an empirical study that uses data from the Jambi Province Input Output Tables in 1998, 2007 and 2016, each of which has been collected in a common set to see the comparability between observation periods. The results show that the technical change model is quite well used for forecasting according to the assumption that the technical coefficient level is constant during the planning period. Meanwhile, the estimated output deviation tends to be higher than that of the actual data.
A Panel Data Regression Analysis for Economic Growth Rate In Bengkulu Province Supianti, Filo
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.v2i1.27258

Abstract

Panel data is a combination of time series data and cross section data. The analytical method used for panel data is panel data regression. One of the advantages of analysis using panel data regress One of the indicators to measure the development of the production of goods and services in an economic area in a given year against the value of the previous year which is calculated based on GDP/GRDP at constant prices is Economic Growth. The dependent variable in this study is the growth rate of GRDP. The independent variable in this study is IPM, TPAK, TPT. This study uses panel data regression analysis with the Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). The data processing in this study uses the R Studio application.
Modeling Social Media Use and Anxiety Levels With Students’ Sleep Quality: Ordinal Logistic Regression ., Annisa Agustina
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.v2i1.27259

Abstract

The study tries to model sleep quality using ordinal logistic regression since the response variable is in the form of categorical data. The purpose of this study was to identify factors related to students' sleep quality based on social media usage variables and anxiety levels. One hundred and fifty students of SMAN 1 Tualang, Riau are selected with snowball technique and participated online.  The result showed that there is a correlation between social media usage and anxiety over sleep quality. Social Media Usage Dependence degree on Sleep Quality was 59.3% and Anxiety level dependence degree on Sleep Quality was 65.3%. Ordinal logistical regression analysis showed that students who were inactive in social media had a good sleep quality, a rate of 0.462 times compared to students who were active in social media. Meanwhile, students with mild anxiety levels had a good sleep quality of 0.369 times compared to moderate anxiety levels.

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