cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tessy Octavia Mukhti
Contact Email
tessyoctaviam@fmipa.unp.ac.id
Phone
+6282283838641
Journal Mail Official
tessyoctaviam@fmipa.unp.ac.id
Editorial Address
LPPM Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Barat, Kota Padang, Sumatera Barat 25131
Location
Kota padang,
Sumatera barat
INDONESIA
UNP Journal of Statistics and Data Science
Published by Universitas Negeri Padang
ISSN : -     EISSN : 2985475X     DOI : 10.24036/ujsds
Core Subject : Science, Education, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences
UNP Journal of Statistics and Data Science is an open access journal (e-journal) launched in 2022 by Department of Statistics, Faculty of Science and Mathematics, Universitas Negeri Padang. UJSDS publishes scientific articles on various aspects related to Statistics, Data Science, and its application. Articles can be in the form of research results, case studies, or literature reviews. All papers were reviewed by peer reviewers consisting of experts and academicians across universities.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 202 Documents
 16   17   18   19   20 
Process Capability Analysis of OPC Cement Production Using Statistical Process Control and IMR Method: Blaine Test Evaluation Alya Aufa, Wafiq; Yenni Kurniawati; Admi Salma; Darwas
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/379

Abstract

The main challenge in cement production at PT Semen Padang is maintaining consistent product quality, particularly the fineness of cement particles measured by the Blaine test. Variations in raw materials and the production process can cause fluctuations in quality, which affect the performance of the final product. Therefore, it is crucial to monitor and control process stability and capability to consistently meet product specifications. Based on the Statistical Process Control (SPC) analysis using Individuals and Moving Range (I-MR) control charts on 28 observations of Ordinary Portland Cement (OPC) Blaine values from February 2025, one out-of-control point was detected on the Moving Range chart between observations 16 and 17, indicating a significant variation. However, all points on the Individuals chart remained within control limits, suggesting that the individual process values were still under control. After revising the outlier data, the process was confirmed stable. Process capability analysis showed a Cp value of 2.17 and a Cpk value of 1.98, indicating that the production process is not only statistically stable but also highly capable of meeting quality specifications. Therefore, despite some variation between data points, the cement production process at PT Semen Padang can be considered stable and capable. Nevertheless, periodic evaluations are recommended to maintain consistent product quality and provide strategic recommendations for the Quality Assurance division in implementing data-driven quality control.
Comparison of Nadaraya-Watson Method with Local Polynomial in Modeling HDI and Poverty Relationship in Java Island Novi, Yoli Marda; Fadhilah Fitri; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/380

Abstract

Poverty remains a critical issue in Indonesia, with the number of poor people reaching 24.06 million in September 2024. The Human Development Index (HDI), which indicates the level of human resource quality, is one of the factors influence poverty. This analysis focuses on the correlation involving HDI also this number of poor people in districts/cities in Java Island by comparing two kernel regresokesion methods, namely Nadaraya-Watson Estimator and Local Polynomial Estimator. Nonparametric regression was chosen thus it does not necessitate this presumption of a certain form of connection among variables, so it is more flexible in capturing complex relationship patterns. Secondary data from Statistics Indonesia (BPS) in 2024 was used in this study. Initial exploration shows, the data distribution does not have a clear pattern, so nonparametric methods are more suitable for use. Modeling is done using the optimal bandwidth obtained through the dpill function in R software. The analysis results show that the local polynomial estimator produces smoother regression curves and lower MSE values. In addition, comparison of different polynomial degrees shows that higher polynomial degrees tended to improve model performance. Among the tested polynomial degrees, the local polynomial with degree five (p=5) produced the lowest MSE value and the highest coefficient of determination. Therefore, the local polynomial estimator with degree 5 is the best method for modeling the relationship between the HDI and poverty levels in Java in 2024
Penerapan Partial Least Squares dan Pendekatan Robust dalam Analisis Diskriminan untuk Data Berdimensi Tinggi Rahmadina Adityana; Vionanda, Dodi; Permana, Dony; Fitri, Fadhilah
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/396

Abstract

Classical discriminant analysis, namely linear discriminant analysis and quadratic discriminant analysis, is generally known to suffer from singularity problems when exprerienced with high-dimensional data and is not robust to outliers that make the data not multivariate normally distributed. This research focuses on investigating the classification performance of discriminant analysis on high-dimensional data by applying two approaches, namely the Partial Least Square (PLS) dimension reduction approach as a solution to high-dimensional data and a robust approach with the Minimum Covariance Determinant (MCD) estimator technique that is robust to outliers. The data used for this study is Lee Silverman Voice Treatment (LSVT) data. PLS forms five optimal latent variables that represent predictor variable information. Based on the assumption test of covariance homogeneity between groups, the test statistic value is greater than the chi-square table or the p-value is smaller than the significance level, which means that the assumption is unfulfilled, so quadratic discriminant analysis is applied. The evaluation results showed that the quadratic discriminant analysis analysis model with the MCD approach on the PLS transformed data was able to achieve 81% accuracy, 71% precision, 86% recall, and 77% F1-score. These values indicate that both approaches are able to maintain the efficiency of discriminant analysis classification performance on high-dimensional and multivariate non-normally distributed data.
Comparison of Kernel and Spline Nonparametric Regression (Case Study: Food Security Index of Jambi Province 2023) Rosa Salsabila Azarine; Septrina Kiki Arisandi; Fadhilah Fitri; Yenni Kurniawati
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/397

Abstract

Food security is one of the issues that plays an important role in national development, especially in regions with varying levels of economic welfare such as Jambi Province. One of the main factors affecting food security is food expenditure, which reflects the economic capacity of households to access food. The complex and non-linear relationship between Food Security Index (FSI) and Food Expenditure requires a flexible modeling approach in the analysis. This study aims to compare the performance of nonparametric regression Kernel ans Spline regression methods, namely the Nadaraya-Watson Estimator (NWE) and Local Polynomial Estimator (LPE) for Kernel Regression as well as Smoothing Spline and B-Spline for Spline Regression. The analysis was conducted using secondary data obtained from the Food Security and Vulnerability Map (FSVA) of 2023, with a total of 141 subdistricts in Jambi Province. The response variable is the Food Security Index (FSI), while the predictor variable is Food Expenditure. Model evaluation was conducted using the Mean Squared Error (MSE) and the coefficient of determination (R²). The results showed that the NWE method had the best performance with the smallest MSE value of 24.47690 and the highest R² value of 0.3332, meaning that approximately 33.32% of the variation in FSI could be explained by Food Expenditure. The LPE method showed nearly comparable performance, while Smoothing Spline and B-Spline exhibited higher prediction error rates. Therefore, the NWE method can be recommended as an effective nonparametric regression approach for modeling the relationship between food expenditure and food security.
Comparison of Nadaraya-Watson and Local Polynomial Methods in Analyzing the Relationship Between Consumer Price Index and Inflation in South Kalimantan Salwa Hifa Fadilah; Fadhilah Fitri; Fenni Kurnia Mutiya
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/401

Abstract

This study compares the performance of two nonparametric regression methods, namely Nadaraya-Watson and Local Polynomial, in analyzing the relationship between the Consumer Price Index (CPI) and inflation in South Kalimantan Province. Nonparametric approaches were chosen for their greater flexibility in capturing nonlinear relationships that conventional parametric models may fail to explain. The data were obtained from the Central Statistics Agency (BPS) for the period from January 2022 to December 2024, with missing values in the inflation variable handled through mean imputation. The optimal bandwidth was selected using the direct plug-in method (dpill).Visually, the Nadaraya-Watson method produced a more fluctuating curve that is highly sensitive to local variations, while the Local Polynomial method yielded a smoother and more stable curve. Quantitatively, the Local Polynomial method demonstrated better performance with lower MSE (0.1839), MAE (0.3507), and a higher R² (0.1811) compared to Nadaraya-Watson. These findings indicate that the Local Polynomial method is more effective in balancing curve flexibility and stability. This study also addresses a methodological gap by highlighting the relevance of nonparametric approaches in regional economic analysis. Future research is encouraged to explore alternative bandwidth selection methods and different kernel functions to improve estimation accuracy.
Applying Robust Spatial Autoregressive Model to Analyze the Determinants of Open Unemployment in West Java Berliana Nofriadi; Suci Rahmadani; Sepniza Nasywa; Tessy Octavia Mukhti; Yenni Kurniawati
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/402

Abstract

Open unemployment is a critical macroeconomic challenge in developing regions like West Java, Indonesia, where spatial disparities and data anomalies complicate traditional analysis. This study addresses these limitations by employing a Robust Spatial Autoregressive (RSAR) model with M-Estimator, integrating spatial dependence and outlier resilience to enhance estimation accuracy. Using 2024 district-level data from Indonesia’s Central Bureau of Statistics (BPS) and Open Data Jabar, the research examines determinants such as labor force participation, education, and regional GDP. The methodology begins with Ordinary Least Squares (OLS) to identify initial predictors, followed by spatial diagnostics (Moran’s I, Lagrange Multiplier tests) to confirm spatial autocorrelation. A customized Queen contiguity weight matrix captures neighborhood effects, while robust M-Estimation mitigates outlier distortions. Results reveal that the RSAR model achieves superior explanatory power (R² = 0.8626) compared to OLS and standard Spatial Autoregressive (SAR) models, with labor force participation (X₄) emerging as a significant negative predictor of unemployment. Spatial effects (ρ = 0.337) though modest, underscore the importance of inter-regional dynamics. The study concludes that RSAR offers a more reliable framework for regional labor analysis, combining spatial rigor with robustness against data irregularities. Policy-wise, the findings advocate targeted interventions to boost labor participation and address localized disparities, emphasizing the need for spatially informed, outlier-resistant methodologies in economic planning.
Comparison of Expectation-Maximization (EM) Algorithm and Kmeans for District/City Clustering in West Sumatera Province Based on Breadfruit Production Mayrita, Mayrita Addila Putri; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/403

Abstract

Breadfruit (Artocarpus altilis) is an important food source that is highly nutritious and plays a strategic role in West Sumatra Province. However, challenges such as pests, diseases and marketing constraints affect its cultivation and productivity. This study employed K-means and expectation-maximisation (EM) clustering methods to categorise regions according to their breadfruit cultivation characteristics. The elbow method identified three optimal clusters for K-means and seven for EM. Evaluating the quality of the clusters using the silhouette coefficient produced values of 0.47 and 0.37 for EM and K-Means respectively, indicating that EM produced tighter, more distinct clusters. These results suggest that EM is a more effective method for describing the variation in breadfruit production in West Sumatra. With this in mind, the research is expected to inform strategic decision-making aimed at increasing the productivity and added value of breadfruit crops in the area..
Panel Data Model Selection and Significant Determinants of New Family Planning Participants in West Sumatra Diah Triwulandari; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/404

Abstract

Population issues in Indonesia are not limited to poverty, urbanization, population explosion, or high birth rates, but also include how small families can improve and maintain their quality of life. The main objective of the Family Planning program is to create happy and prosperous families with an ideal number of children. The West Sumatra Provincial Health Office report (2023) emphasizes that increasing the number of new family planning acceptors is an important priority to support the success of maternal, child, and family planning health programs, in line with the 2020–2024 RPJMN policy direction. Therefore, this study aims to develop the best panel data model and identify the factors that significantly influence the number of new family planning participants in West Sumatra Province. The secondary data used were obtained from the Statistics Indonesia (BPS) publication entitled West Sumatra Province in Figures from 2021 to 2024. The observation units in this study were 19 districts/cities in West Sumatra Province with a time series from 2020 to 2023. The results indicate that the best-selected model is the random effect model, with the number of couples of reproductive age proven to have a significant effect on the number of new family planning participants. The R-square value of 53.11% indicates that the model can explain 53.11% of the variation in the dependent variable, while the remaining 46.89% is influenced by other factors not included in the model.  
PayPal Usage in Indonesia with k-Nearest Neighbor Algorithm Amannia zeze; Muhammad Ravi Azzaki; Dodi Vionanda
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/405

Abstract

The development of information and digital technology has had a significant impact on the financial sector. In Indonesia, digital payment technologies such as PayPal, Gopay, Shopeepay, OVO, and DANA have become an integral part of the modern payment system. Since the implementation of the national electronic clearing system, RTGS, and ATMs in 2005, transactions have become increasinglyconvenient. This study analyzes user sentiment toward PayPal in Indonesia to understand user experience and provide insights for service development, marketing strategies, and brand reputation management. Review data from the PayPal app was collected from Google Plat via web scrapping and processed to yield 597 clean data points. Initial sentiment was categorized into positive, neutral, and negative, wordcloud visualization displayed positive and negative sentiment, while neutral sentiment was analyzed numerically. Automatic labeling was performed using the NLTK library based on rating values, above 3 positive, below 3 negative, and exactly 3 neutral. The results showed 146 positive reviews, 451 negative reviews, and a few neutral reviews. Sentiment classification using the K-Nearest Neighbor (K-NN) method yielded adequate accuracy, indicating that PayPal's acceptance in Indonesia is largely influenced by users' negative experiences. These findings provide a foundation for developing strategies to improve service quality and update PayPal's operational policies in the Indonesian market.
Factors Affecting Households Program Keluarga Harapan Recipients in West Sumatra: Binary Logistic Regression Analysis Ardhi, Sonia; Dodi Vionanda; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/406

Abstract

Poverty is still a complex issues in Indonesia. Poverty rate in West Sumatra province has increased over the past 3 years. One of the government's initiatives to address poverty is the Program Keluarga Harapan (PKH), which is a social protection program that provides conditional cash transfers to poor and vulnerable Keluarga Penerima Manfaat (KPM) on condition that they are registered in the Data Terpadu Kesejahteraan Sosial (DTKS). Although PKH has a positive impact on poverty alleviation and enhanced access to health, education, and social welfare, the implementation still faces major challenges such as data inaccuracies, particularly in targeting accuracy. Therefore, an analysis is needed to determine the factors that significantly affects PKH recipient households in West Sumatra Province. This research used variables from the DTKS variable group contained in SUSENAS 2024 using two stages one phase stratified sampling method with 11,600 observations consisting of 1,790 receiving PKH and 9,810 not receiving PKH. The dependent variable is PKH recipient status (Yes = 1, no = 0). Data were analyzed using binary logistic regression with a significance level of 5%. Based on the results of the analysis, it can be concluded that floor area of ​​the house, age of the household head, household size, education level of the household head, and floor material of the house have a significantly effect on PKH recipient households. Household size has the most influence on PKH receipt with a 40,3% probability of receiving PKH.

Page 18 of 21 | Total Record : 202

 16   17   18   19   20 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science Vol. 3 No. 1 (2025): UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): UNP Journal of Statistics and Data Science Vol. 2 No. 2 (2024): UNP Journal of Statistics and Data Science Vol. 2 No. 1 (2024): UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 4 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 3 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 2 (2023): UNP Journal of Statistics and Data Science Vol. 1 No. 1 (2023): UNP Journal of Statistics and Data Science More Issue