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. 3 No. 1 (2024)" : 5 Documents clear
Application of Small Area Estimation for Estimation of Sub-District Level Poverty in Bengkulu Province: Comparison of Empirical Best Linear Unbiased Prediction (EBLUP) and Hierarchical Bayesian (HB) Methods Pratama, Auliya Yudha Pratama
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.v3i1.30367

Abstract

Poverty is an important problem facing the world. Various ways are done to eradicate poverty. In planning poverty alleviation, policy makers need detailed information down to the smallest area level that can be produced. Currently, the demand for estimation at the small area level is increasing, while the success of estimation using the indirect method in reducing the Relative Standard Error (RSE) is very dependent on data conditions and the selection of the right method. This study aims to compare the results of estimating the percentage of poor people using direct estimates with indirect estimates using the Small Area Estimation (SAE) technique such as Empirical Best Linear Unbiased Predictor (EBLUP) and Hierarchical Bayesian (HB) method using a case study of poverty data at the sub-district level of Bengkulu Province. The data used are from the Social and Economic Survey (Susenas) in March 2022 and the 2021 Village Potential Data Collection (Podes). There is one sub-district that was not sampled in the March 2022 Susenas. The average RSE value of the direct estimator is 47.014 and the average RSE of the EBLUP estimator is 39.40 and the HB estimator is 15.318. In addition, the SAE EBLUP and HB methods can reduce the mean and median values of RSE estimation results when compared with direct estimates. The RSE of the direct estimator is greater than the RSE of the indirect estimator.
Classification of Hypertension Patients in Palembang by K-Nearest Neighbor and Local Mean K-Nearest Neighbor Rosdiana, Rosdiana
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.v3i1.32381

Abstract

Classification is a multivariate technique for separating different data sets from an object and allocating new objects into predefined groups. Several methods that can be used to classify include the k-Nearest Neighbor (KNN) and Local Mean k-Nearest Neighbor (LMKNN) methods. The KNN method classifies objects based on the majority voting principle, while LMKNN classifies objects based on the local average vector of the k nearest neighbors in each class. In this study, a comparison was made on the results of classifying hypertensive patient data at the Merdeka Health Center in Palembang City with the KNN and LMKNN methods by looking at the accuracy and the smallest APER value produced. The results showed that by using the same proportion of training and testing data and choosing different k values, the results of classifying hypertension patient data at the Merdeka Health Center in Palembang City with the KNN and LMKNN methods resulted in the APER value or the same error rate and accuracy, namely sequentially equal to 0.0303 and 96.97%.
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.
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 1 of 1 | Total Record : 5

 1 

Filter by Year

2024 2024


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025) 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)