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
 14   15   16   17   18 
Classification of Determining Factors for Eligibility of Extreme Poverty Social Assistance Recipients in Dumai City for 2024 Using CHAID Pajrini, Nurul Hasni; Fitria, Dina; Mukhti, Tessy Octavia
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/354

Abstract

Poverty is one of the goals of the Sustainable Development Goals (SDGs). Poverty is a condition in which an individual falls below the standard minimum value of basic needs, both food and non-food. One of the efforts by the Indonesian government to alleviate poverty is through fulfilling needs in various sectors. Although the distribution of social assistance has been successfully implemented, there are still issues in determining beneficiaries who are not properly targeted. Therefore, it is necessary to identify the significant factors influencing the eligibility of social assistance recipients. The application of the CHAID method in classifying the determining factors for eligibility of extreme poverty social assistance recipients in Dumai City for 2024 shows that the significant factors influencing the eligibility status of extreme poverty social assistance recipients in Dumai City for 2024 are house size and neighbors' testimonies. The classification model's accuracy in determining the eligibility factors for extreme poverty social assistance recipients in Dumai City for 2024 is 87.70%.
Clustering Regions in West Sumatera Based on the Special Protection Index for Children Using K-Means Clustering with Silhouette Coefficient Siti Nurhaliza; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 3 No. 1 (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-iss1/356

Abstract

Child protection is a crucial aspect of social development, especially in West Sumatra Province, which consists of 19 regencies/cities with diverse child protection characteristics. This study aims to cluster regencies/cities in West Sumatra based on the 2021 Child Special Protection Index (IPKA) using the K-Means Clustering method with the Silhouette Coefficient. Secondary data were obtained from the Office of Women's Empowerment and Child Protection, Population Control, and Family Planning (DP3AP2KB) of West Sumatra Province, covering variables such as the percentage of working children, internet access, education level, poverty, and child neglect. The results show that the K-Means method is effective in quickly and accurately grouping data into homogeneous clusters, while a Silhouette Coefficient value of 0.70 indicates a strong cluster structure and high-quality grouping.
Comparison of Cox Proportional Hazard Models with Interaction and Without Interaction in Heart Failure Patients Bunga Nafandra; Tessy Octavia Mukhti; Yoli Marda Novi; Nurul Mulya Syahwa; Olga Afrilly Putri
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/342

Abstract

 Heart failure is one of the disorders that attack the heart and is a major cause of morbidity and mortality. There is a 5% prevalence of heart failure in Indonesia in 2020. By utilizing survival analysis, this study aims to compare the Cox proportional hazard model with interaction and without interaction, and identify factors that significantly affect the survival time of heart failure patients. The research data is secondary data consisting of 299 heart failure patient data with several variables including high blood pressure, anemia status, and age. Through the stages of analysis that have been carried out, it is found that the variables of high blood pressure and age have a significant effect on the survival time of heart failure patients, while the anemia variable and the interaction between independent variables do not have a significant relationship with survival time. In addition, based on the AIC value, it is also found that the model without interaction is better than the model with interaction, which is characterized by a smaller AIC value in the model without interaction. Based on the best model, patients with high blood pressure have a 1.52 times higher chance of dying than patients without high blood pressure. In addition, the probability of death increased by 4.33% for every one-year increase in patient age. This study concludes that the model without interaction is more suitable for describing the relationship between independent variables and survival time in heart failure patients.
Cox-Stratified Model in Relationship Analysis between Employee Mental Health and Resignation Decision Sari Agustin; Tessy Octavia Mukhti; Suci Rahmadani; Afifah Nabilah; Wafiq Alya Aufa
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/350

Abstract

This study examines the relationship between employee mental health and turnover decisions using the Cox Stratified model. Utilizing secondary worker turnover data from Kaggle, the research investigates the impact of anxiety and self-control on job tenure. Results indicate that the Cox Proportional Hazard model significantly explains this relationship, with self-control emerging as a key factor negatively associated with turnover risk. Stratification of profession variables, which did not meet the proportional hazard assumption, revealed variations in survival rates across different professions. Professions requiring strong self-control, such as HR and sales, exhibited higher survival probabilities, whereas high-pressure professions like consulting andshowed lower survival rates. A reduced model confirmed the importance of self-control in employee retention. The findings suggest that interventions aimed at enhancing self-control could serve as an effective strategy for mitigating turnover, especially in high-stress occupations. Elevated job pressure can negatively impact employee mental well-being, potentially disrupting self-control and increasing anxiety levels. Future research could incorporate additional influential factors, such as job satisfaction, work environment, and social support, to further develop this research. Furthermore, the implementation of real-time data collection could enable continuous monitoring of mental conditions, behaviors, and relevant factors such as self-control and anxiety, providing dynamic insights over short time intervals.
Survival Analysis of Heart Failure Patients Using the Cox Proportional Hazard Model and Weibull Regression Rahmika Alya; Tessy Octavia Mukhti; Sri Wahyuni; Bunga Miftahul Barokah; Azizah Apriyerni
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/351

Abstract

Cardiovascular disesase is the leading cause of death globally, claiming around 17,9 million lives each year, accounting for 31% of all deaths worldwide. Hearth failure is a common event caused by cardiovascular disease. Hearth failure is one of the principal health issues with excessive mortality and morbidity costs. Heart failure is the main reason of mortality worldwide. This take a look ambitions to analyze the factors influencing the survival of heart failure patients using the Cox proportional hazard Cox (PH) model and the Weibull regression. The main purpose of this study is to provide information on the causes of heart failure deaths and what effects occur when having heart disease. It is hoped that the results of this study can provide the general public to be more careful in order to prevent heart failure disease. The data used are secondary data from Kaggle consisting of 299 patients with the variables anemia, diabetes, hypertension, gender and smoking status. The analysis showed that only hypertension significantly increased the risk of events in both models, whereas other variables were not statistically significant. The selection of the best model is based on the assumptions of proportional hazard, flexibility, and Akaike information criterion (AIC) values. The Cox-PH model was chosen as the model of choice because it is more flexible and does not require certain fundamental assumptions regarding risk distribution. This study provides important insight into the risk factors that influence the prognosis of heart failure patients.
Binary Logistic Regression to Factors Affecting Unmet Need for Limiting in East Java, Indonesia Sri Wahyuni; Yenni Kurniawati; Sepniza Nasywa; Ardiyatul Putri
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/353

Abstract

East Java, Indonesia's second most populated province, is anticipated to see significant annual population growth in the future, potentially resulting in a population explosion. The elevated birth rate facilitates the swift increase in population size. The unmet need for knowledge-based information among women of reproductive age has posed obstacles for the execution of family planning initiatives aimed at reducing birth rates. This study used binary logistic regression to identify the factors affecting the unmet demand for family planning among women of reproductive age in East Java Province in 2017.The investigation revealed that the woman's age, employment status, and husband's educational level significantly influenced the unmet need for constraint. Moreover, women aged 15-24 who are unemployed, lack schooling, have an illiterate partner, and reside in rural regions are more prone to experiencing an unmet need for contraception. Women aged 15-19 years compared to women aged 45-49 years were at 3,182 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women aged 20-24 years compared to women aged 45-49 years were at 1,316 times higher risk of having an unmet need for family planning compared to a met need for family planning. Women who did not work compared to women who worked were 1,311 times more likely to have an unmet need for family planning compared to a met need for family planning. The binary logistic analysis model that was formed provided a good accuracy of 92,135% in predicting
Application of K-Modes Clustering Method to Identify Low Birth Weight Factors in Central Sulawesi Province Aprotama, Celsy; Yenni Kurniawati; Muhammad Arief Rivano; Devi Yopita Sipayung
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/357

Abstract

Low birth weight (LBW) has long-term effects on maternal and child health, with a high prevalence in Central Sulawesi Province. This study aims to identify factors influencing the occurrence of LBW in the region using the k-modes clustering method. The data used in this research is derived from the 2017 Indonesian Demographic and Health Survey. The analyzed variables include the husband's education level, miscarriage rate, maternal smoking habits, child's gender, husband's occupation, type of residence, and wealth index. The analysis revealed two distinct clusters. The first cluster mainly consisted of husbands with a secondary education level or equivalent to junior high school, working in the agricultural sector, residing in urban areas, and having a medium wealth index. In contrast, the second cluster was dominated by husbands with only primary education or equivalent to elementary school, living in rural areas, and having a very low wealth index. The findings of this study emphasize the need for comprehensive efforts to improve education, enhance environmental conditions, and expand healthcare access to reduce poverty and lower the incidence of LBW in Central Sulawesi. This research also contributes to initiatives aimed at improving maternal and child health in the region.
Logit And Complementary Log-Log Modeling (Case Study: Factors Influencing Birth Control Use in Papua 2017) Sasmita, Riza; Yenni Kurniawati; Sri Wahyuni; Celsy Aprotama
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/358

Abstract

Research was conducted to determine the factors that influence the use of family planning in Papua Province in 2017. Indonesia has the 4th largest total population in the world, facing the challenge of a fairly high and uncontrolled population growth rate, which can have an impact on the welfare of the community, especially Papua Province. This study used secondary data from the 2017 SDKI. The population of this study was all women of childbearing age in the province of Papua. The research was conducted using logit logistic regression and cloglog logistic regression methods and took the best model to analyze the factors affecting family planning use in Papua Province. The results showed that the cloglog logistic regression model proved to be the best model based on AIC and accuracy. The accuracy of this cloglog logistic regression model is 78.54%. With the results of the cloglog logistic regression analysis, it was found that there was a relationship between region of residence, husband's education, and wife's education. The odds of a woman who has a husband with more than a junior high school education having an unmet need for family planning is 1.688 times higher than a woman who has a husband with less than a junior high school education. The odds of a woman with a junior high school education or above having an unmet need for family planning is 0.496 times higher than a woman with less than a junior high school education.
Mortality Trends in Heart Failure Patients : A Study Using Cox Regression Models: Tren Mortalitas pada Pasien Gagal Jantung: Sebuah Studi Menggunakan Model Regresi Cox Ervi Dayana Putri; Tessy Octavia Mukhti; Rahmatul Annisa; Adinda Putri; Sepniza Nasywa
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/359

Abstract

Heart failure is classified as a cardiovascular disease, which is the leading cause of death worldwide. In Indonesia, heart failure has a high mortality rate, which in 2019 became the second leading cause of death after stroke. One method that can be used to examine the factors affecting mortality in heart failure patients is the cox proportional hazards regression. Cox proportional hazards regression is one of the most commonly used methods for analyzing survival data to date. The study data consisted of 299 observations involving 5 predictor variables, such as age, serum creatinine, serum sodium, high blood pressure, and diabetes. The conclusion of the analysis indicates that the variables of age, serum creatinine, serum sodium, and high blood pressure are significant. High blood pressure and serum creatinine are the factors that most affect the death of heart failure patients. Patients with high blood pressure have a 56,71% higher risk of death than patients without high blood pressure, and every 1 mg/dL in creatinine in the blood, the risk of death for heart failure patients will increase by 29,77%.
Analisis Sentimen Program MSIB pada Aplikasi X (Twitter) Menggunakan Algoritma Naïve Bayes Husni, Nabila; Dodi Vionanda; Nur Leli; Syafriandi Syafriandi
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (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-iss2/361

Abstract

Certified Internships and Independent Studies (MSIB) is one of the programs of the Independent Learning-Independent Campus (MBKM) curriculum as a policy of the Kemendikbudristek. A government policy, especially in terms of education, will of course give rise to stigmas or feedback from the public regarding the policy. This research aims to find out public opinion regarding the MSIB program in the X (Twitter) application by sentiment analysis using the Naive Bayes Classifier algorithm. From this analysis, it was found that 84.6% of reviews had positive sentiments, while 16.4% of reviews had negative sentiments. Evaluation using the Naïve Bayes Classifier model shows that this model succeeded in classifying 85% of all data correctly, showing quite good performance in classifying the sentiment of these reviews.

Page 16 of 21 | Total Record : 202

 14   15   16   17   18 

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