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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 36 Documents
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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.
Sensitivity Analysis in Optimizing Coffee Production Profit Using Linear Programming with Simplex Method (Case Study: Komocha Coffee Home Industry) Anjanni, Chyntia Meininda
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.v3i1.32817

Abstract

Bengkulu Province is the third largest coffee producing province in Indonesia, which is mostly dominated by the Robusta coffee type. One of the businesses engaged in the coffee production process is the Komocha coffee home industry. However, the industry has profit constraints that are not yet optimal. One method that can be used in solving optimization problems is linear programming (simplex method). The purpose of this research is to optimize the profit of coffee production and determine the results of sensitivity analysis using linear programming with simplex method. Based on the calculation results, the profit per production is IDR 2,061,836 by producing 101 pcs of bitter melon seed variant coffee, 60 pcs of premium variant and 54 pcs of regular variant. The results of the sensitivity analysis of the Komocha coffee home industry are that it can produce coffee with a minimum raw material usage limit of 28 kg and a maximum of 32 kg. Limits for packaging costs are at least IDR 430,717.9. Then, for minimum labor costs of IDR 239,038.5 per person and for minimum machine working hours of 34 hours and minimum operational costs of IDR 2,482,139 per production.
Control Chart of T² Hotelling on Quality Control Activities of Crude Palm Oil (CPO) at PT Cipta Graha Garwita, Seluma Regency, Bengkulu Province Pangesti, Riwi Dyah; Alus Ahmad Suhaimi; Etis Sunandi; Istiqomah Rabithah Alam Islami
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.v3i1.36217

Abstract

PT Cipta Graha Garwita (CGG) is a palm oil producer focused on product quality, especially crude palm oil (CPO) for both food and non-food applications. Despite CGG's good reputation, variability in quality characteristics such as Free Fatty Acid (FFA) and moisture can affect the final quality of CPO. This study aims to apply a statistical quality control system to monitor and improve the consistency of CPO quality using T² Hotelling control charts. Statistical quality control methods ensure that products meet standards by reducing variability. One such tool is the T² Hotelling control chart, effective for monitoring multivariate variables using mean vectors and variance-covariance matrices. This study involves steps from data collection, testing multivariate normality assumptions, calculating T² Hotelling control charts, to determining control limits. Testing for multivariate normality assumptions showed the data met normal distribution criteria. The first and second stage T² Hotelling control charts identified several out-of-control observations. These out-of-control observations were excluded, and further analysis showed that after their removal, all data were within statistical control limits. This study recommends further analysis to determine the causes of out-of-control observations using Ishikawa diagrams and process capability evaluation to ensure consistent product quality.
Factors Affecting The Open Unemployment Rate in West Sumatra Province Using Spatial Autoregressive (SAR) Adellia, Clara Febby; Sari, Devni Prima
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.v3i2.36514

Abstract

This paper proposes a Spatial Autoregressive (SAR) model to analyze the significant factors affecting the open unemployment rate in West Sumatra during 2023. The main advantage of the method is its ability to accurately capture spatial interactions between neighboring regions, such that it can provide a comprehensive understanding of regional unemployment patterns efficiently. By introducing the K Nearest Neighbor (KNN) weighting matrix and spatial lag parameter to the model, the effect of regional proximity on unemployment rates is more accurately captured. The viability of the SAR model is assessed by analyzing its ability to produce the lowest Akaike’s Information Criterion (AIC) value, indicating its suitability for modeling regional unemployment patterns. The result indicates that the SAR model is more effective than the multiple linear regression model in capturing regional unemployment patterns, with an AIC value of 52.756. The factors that influence the open unemployment rate are gross regional domestic product, labor force participation rate and the percentage of poor people.
Analysis of the Quality of Health Service at the Air Haji Hearth Center Using the Ordinal Logistics Regression Method Soleha, Annisa; Sari, Devni Prima
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.v3i2.36516

Abstract

Improving the quality of public services has become a major concern in government agencies as an effort to provide optimal public services. The quality of service can be affected by various factors. Therefore, it is necessary to conduct an analysis to find out the relationship between factors that affect service quality and service quality itself. Efforts are made to analyze the relationship between factors that affect service quality and service quality itself by using the ordinal logistic regression method in analyzing the relationship between influencing factors and influencing factors. This type of research is applied research that begins with theoretical analysis and data collection then ordinal logistic regression analysis. Based on the results of data analysis, it was found that the variables that significantly affected the quality of service were direct evidence variables, guarantee variables, and empathy variables. This research is useful for the Air Haji health center in an effort to improve the quality of health services.
Enhancing Bus Scheduling Efficiency in Rajshahi City, Bangladesh Through Linear Programming Apporach Ripon, Md. Baki Billah; Hassan, Md Maruf
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.37195

Abstract

Efficient bus scheduling is significant for optimizing transportation systems, particularly in densely populated areas like Rajshahi division in Bangladesh. This research focuses on minimizing the no of buses needed for operations in Rajshahi Bus Terminal by introducing a rescheduling strategy using Linear Programming Approach. The study divides the day into eight time periods, with four overlapping shifts per day. By strategically overlapping shifts, we aim to utilize existing resources more efficiently and reduce the number of buses needed. Utilizing mathematical modeling formulation of Linear Programming and optimization techniques, an optimal rescheduling method is introduced to minimize the number of buses required ensuring efficient service delivery and passenger satisfaction. Furthermore, it evaluates the effectiveness of the proposed approach through quantitative analysis and compares it with the remained scheduling system in terms of bus utilization and overall efficiency. The outcomes of this research contribute to the optimization of bus transportation systems in urban areas, offering insights into effective resource allocation. By implementing the suggested re-scheduling strategy at Rajshahi city Bus Terminal, public transportation networks can be made more sustainable and cost effective. This could serve as a model for other transportation hubs in Bangladesh and beyond.
Machine Learning Approach to Automated Early Warning System for Medical Vital Signs Monitoring Nevani, Claudia; Sigit Nugroho; Winalia Agwil
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.37437

Abstract

Precise and timely detection of deteriorating vital signs is an important aspect of patient safety and clinical intervention. The current standard of monitoring systems lacks automated early warning systems, instead using manual observation to make judgments. This manual approach can lead to delays in detecting critical changes in a patient's condition. We present a novel approach to developing an automated early warning system for vital signs using a hybrid method that combines LSTM (Long Short Term Memory) and XGBoost (Extra Gradient Boost), both methods offer robust predictive modeling that is able to capture the complex and often non-linear relationships inherent in physiological data. This research believes that using a novel technique that combines LSTM and XGBoost advances predictive systems in healthcare-based technology as well as laying the groundwork for even further innovations in early warning systems. The early warning system will evaluate vital signs such as respiratory rate, SpO2 levels, heart rate, body temperature, and pulse which can recognize and predict early signs of clinical deterioration, allowing early intervention and may save a patient’s life. This research will use error metrics such as MAPE, MAE (Mean Absolute Error), MSE, RMSE, and MAD to compare the predicted actual values.
Application of Tobit Regression on Household Expenditure on Egg and Milk Consumption in Bengkulu City Claudia Nevani; Sigit Nugroho; Winalia Agwil
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.40336

Abstract

Regression analysis is a statistical method used to examine the functional relationship between two or more independent variables and a dependent variable. One of the regression methods designed to handle censored data or data with significant zero values is Tobit regression. This study aims to model household expenditures on egg and milk consumption in Bengkulu City using Tobit regression and to identify the factors influencing these expenditures. The data were obtained from the 2022 National Socioeconomic Survey, with a total sample of 1,170 households. The Tobit regression model was chosen because most household expenditure data had zero values, indicating censored data characteristics. This study identified several factors affecting expenditures on egg and milk consumption, such as the household head's education level, the number of household members, and the household head's employment sector. The results showed that the education level of the household head (elementary, junior high, and high school), the number of household members, and the household head's employment in agriculture and trade sectors had significant impacts on household expenditures for egg and milk consumption. The education level of the household head and their employment sector had a negative relationship, while the number of household members showed a positive relationship with these expenditures. The Tobit regression model successfully modeled household expenditures with adequate accuracy, as indicated by a Mean Absolute Percentage Error (MAAPE) of 1.38%.
Comparison of Poverty Clustering Results based on Distance Measurement with the Complete Linkage Method in Indonesia Anggraini, Fira; Devni Prima Sari
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.40554

Abstract

Every year, population growth in Indonesia increases and has the potential to trigger poverty.Poverty indicators include the number of poor people, per capita expenditure, humandevelopment index, average years of schooling, and unemployment. The clustering of regionsis necessary for the government to be more effective in development. One of the methodsused is cluster analysis, a statistical technique that groups objects based on similarcharacteristics. This research compares the results of clustering poverty in Indonesia'sRegency/City in 2023 using the complete linkage method, which is based on the farthestdistance. The distances analyzed include Euclidean, Square Euclidean, Manhattan, andMinkowski, resulting in two clusters at each distance. Minkowski proved to be the bestdistance with the smallest standard deviation ratio, which was 1.518 for cluster 1 and 2.225for cluster 2, compared to the other distances. These results show that the Minkowski methodis superior in clustering poverty areas in Indonesia.  
The ROCK Ensemble Cluster Method for People's Welfare Analysis A Mixed Data Approach : Metode Ensemble Cluster untuk Analisis Kesejahteraan Rakyat Pendekatan Data Campuran Arsilla Uswatunnisa; Devni Prima Sari
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.40859

Abstract

This study clusters districts/cities in West Sumatra based on public welfare indicators using the Ensemble Cluster Method with the ROCK algorithm. This approach handles mixed data, where numeric data is clustered with Hierarchical Agglomerative Clustering, while categorical data uses ROCK. The clustering results are combined through Cluster Ensemble to improve accuracy. Secondary data from BPS 2023 includes eight indicators of people's welfare. Clustering was validated using Compactness (CP). Results showed five optimal clusters, with a CP value of 0.44. Cluster 1 has the greatest welfare challenges, while Cluster 5 shows the highest welfare. These findings can be used as a basis for formulating more targeted regional development policies.

Page 3 of 4 | Total Record : 36

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