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Tessy Octavia Mukhti
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LPPM Universitas Negeri Padang, Jalan Prof. Dr. Hamka, Air Tawar Barat, Kota Padang, Sumatera Barat 25131
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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 17 Documents
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Search results for , issue "Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science" : 17 Documents clear
Peramalan Harga Beras di Kota Padang untuk Tahun 2025 Menggunakan Jaringan Syaraf Tiruan dengan Metode Backpropagation Nisa, Farras Luthfyah; Dony Permana; Denny Armelia
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/381

Abstract

Rice is a staple food commodity in Indonesia that significantly influences economic stability and food security. In Padang City, rice price fluctuations frequently occur due to high dependence on external supply sources and limited local production, highlighting the need for a reliable predictive system. This study aims to forecast the monthly average retail price if rice in Padang City for the year 2025 using an Artificial Neural Network (ANN) based on the Backpropagation algorithm. The forecasting model is developed using historical rice price data from January 2017 to December 2024. In addition to building the forecasting model, this study evaluates the model’s accuracy in capturing the complex and nonlinear patterns of rice price fluctuations. The forecasting results are expected to serve as a valuable reference for local policymakers, market participants, and consumers in making strategic decisions to anticipate future price volality.
Klasterisasi Kabupaten/Kota Berdasarkan Faktor-Faktor yang Mempengaruhi Kemiskinan di Sumatera Barat Menggunakan Metode K-Medoids Hardi, Afifah; Dony Permana; Denny Armelia
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/382

Abstract

Poverty remains a significant issue in Indonesia, particularly in West Sumatra Province, where regional disparities persist despite a national decline in poverty rates. This study aims to classify the 19 regencies/cities in West Sumatra based on key socioeconomic indicators to support more targeted and effective poverty alleviation policies. Using a quantitative descriptive approach, the research applies the K-Medoids clustering method to group regions according to four indicators: Gross Regional Domestic Product (GRDP) per capita, Human Development Index (HDI), Open Unemployment Rate (OUR), and Gini Ratio. Secondary data for the year 2024 were obtained from the official website of the Central Bureau of Statistics of West Sumatra. Prior to clustering, data standardization using Z-score transformation was performed, and multicollinearity was tested using the Variance Inflation Factor (VIF). The silhouette method indicated that the optimal number of clusters is four. The clustering analysis revealed four distinct groups: (1) underdeveloped areas with low income and human development but high inequality; (2) moderately developed areas with stable unemployment and low income inequality; (3) urbanized areas with high income and human development but also high unemployment and inequality; and (4) a single metropolitan area with high economic and human development and moderate inequality. The findings highlight the importance of region-specific strategies in addressing poverty, considering the diverse economic and social conditions across regions. The results can serve as a basis for designing equitable and effective socioeconomic development policies.
Fuzzy C-Means Based Clustering of Central Java’s Regencies and Cities Using Economic Welfare Indicators 2023 Winda Fariza, Winda Fariza; Syafriandi Syafriandi; Fadhira Vitasya Putri; Eujeniatul Jannah
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/414

Abstract

This study aims to cluster the regencies and cities in Central Java Province based on economic welfare indicators using the Fuzzy C-Means (FCM) method. The motivation for this research arises from the evident disparities in development outcomes across regions in Indonesia, particularly in Central Java. Several areas in this province continue to experience high poverty rates, low income, and poor human development despite improvements in labor force participation in others. Five key indicators were used: Labor Force Participation Rate (TPAK), Open Unemployment Rate (TPT), Percentage of Poor Population (PPM), Average Net Income (RPB), and Human Development Index (HDI). The data, obtained from Badan Pusat Statistik (2023), were standardized and analyzed using the FCM algorithm with optimal clusters determined via the elbow method. The clustering results show three distinct regional groupings: Cluster 0 includes areas with relatively high HDI and income despite lower labor participation and higher poverty; Cluster 1 comprises urbanized areas with high labor participation but lower HDI; and Cluster 2 represents the most disadvantaged areas with low income, high unemployment, and poor development outcomes. These findings offer a valuable foundation for targeted policy interventions and strategic regional development planning. Fuzzy C-Means proves to be an effective approach for uncovering nuanced regional profiles in socio-economic development.
Stratified Cox Regression Approach to Identifying Prognostic Factors for Survival in Breast Cancer Patients Ervandi, Dhio; Novriani, Aisyah; Luthfiyah, Andini Diva; Siregar, Fauzan Al Hamdani; Mukhti, Tessy Octavia
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/418

Abstract

The most common type of cancer that affects women is Breast cancer. In 2022, 2.3 million women were diagnosed with breast cancer, and 670,000 deaths were recorded globally. By 2040, it is estimated that breast cancer will increase by 40%, reaching 3 million annually with the number of deaths increasing by 50% to 1 million in 2020. This highlights breast cancer as a serious threat to world health. This study utilized secondary data from METABRIC or the Molecular Taxonomy of Breast Cancer International Consortium obtained from the website www.kaggle.com/datasets/raghadalharbi/breast-cancer-gene-expression-profiles-metabric/data. The independent variables analyzed were, Age at Diagnosis (X­­1), Surgery Type (X­­2), Chemotherapy (X­­3), Hormone Therapy (X­­4), Tumor Size (X­­5), Radio Therapy (X­­6), Pam50. The dependent variables were Survival Time (Overall Survival Month) and Patient Status. In this study, we used the Stratified Cox model to predict the predictor variables of survival time. The total number of patients used was 18886, with 1080 censored patients and 788 uncensored patients. The Stratified Cox model without interaction revealed that the patients who underwent breast-conserving surgery had a 1.35 times higher risk of death compared to those who underwent mastectomy. Patients who received chemotherapy had a 2.01 times higher risk of death than those who did not, while patients who did hormone therapy had a 1.83 times higher risk of death than those who did not undergo this therapy.
An Examination of Determinants Affecting the Survival Duration Pediatric Brain Cancer Patients Through Stratified Cox Regression Analysis Siregar, Fauzan Al-Hamdani; Andini Diva Luthfiyah; Tessy Octavia Mukhti; Dony Permana
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/420

Abstract

Brain cancer is the second most common pediatric malignancy and the leading cause of cancer-related mortality in children. Pediatric brain tumors (PBTs) represent around 25% of all pediatric cancers and consist of clinically and biologically diverse subtypes, with an estimated incidence of 0.3–2.9 cases per 100,000 children annually. The high prevalence emphasizes the importance of identifying factors that influence patient survival. This study aims to identify and analyze the factors that significantly affect the survival duration of pediatric brain cancer patients by applying the Stratified Cox regression model. This study utilized secondary data from the Pediatric Brain Cancer database (www.cbioportal.org). Independent variables included cancer type, ethnicity, other medical conditions, sex, tumor type, and treatment type, while the dependent variables were survival time (OS Months) and patient status (OS Status). Data were analyzed using the Stratified Cox regression method. A total of 203 patients were observed, consisting of 39 uncensored cases (19.21%) and 164 censored cases (80.79%). The majority of patients were male (58.62%), diagnosed with low-grade glioma/astrocytoma (43.35%), classified as non-Hispanic or Latino (93.52%), had no additional medical conditions (51.72%), received new treatment (85.22%), and were categorized with primary tumor type (74.38%). Results from the stratified Cox model indicated that cancer type was a significant predictor of survival. Children with embryonal tumors were found to have 8.9 times greater risk of experiencing an event compared to those with CNS cancer types, whereas children with high-grade glioma/astrocytoma had a 24.85 times higher risk compared to the CNS cancer group.
Logit and Complementary Log-Log Modeling in the Case of Factors Affecting Heart Failure Disease MAWARNI, IGA; Asyifa Dwi Ayshah; Dhiyaa Fitri Yafe; Fadhilah Fitri
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/421

Abstract

Heart failure is one of the leading causes of morbidity and mortality globally. Heart disease is a disease caused by plaque that builds up in the coronary arteries that supply oxygen to the heart muscle. Research on heart failure disease aims to find out what factors affect heart failure disease and how much influence it has. This test was conducted using logistic regression method with logit modeling and complementary log-log modeling in analyzing data of 918 patients with heart failure disease. This study also takes which modeling is the best. The results of this analysis indicate that Age, Gender, Blood Sugar, and Chest Pain have significant effects on the likelihood of Heart Failure. Specifically, higher blood sugar levels and the presence of chest pain were found to increase the probability of heart failure, while gender and age showed varying effects across different age groups. Based on the model comparison, the Logit model demonstrated better fit and predictive accuracy than the Complementary Log-Log model, as reflected by its lower AIC value 897.43.
Evaluation of Prognosis and Duration of Survival in Breast Cancer Patients Using the Cox PH Model Meliza, Dela; Tessy Octvia Mukhti; Riza Sasmita; Celsy Aprotama; Rahmat Kurniawan
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/422

Abstract

Breast cancer is the leading cause of cancer-related deaths among women in Indonesia. Late detection and delayed treatment contribute significantly to this high mortality rate, as many patients seek medical care only after reaching advanced stages. Early detection through Breast Self Examination (BSE) and timely intervention can improve survival rates and quality of life. This study aims to evaluate the survival duration and influencing factors for breast cancer patients using clinical and genomic data from the METABRIC dataset, encompassing 1.980 primary breast cancer cases. The study employs survival analysis using Kaplan-Meier curves, Log-rank tests, and Cox proportional hazards regression to analyze the data. Results indicate significant differences in survival rates based on type of surgery and chemotherapy, while age at diagnosis shows no significant effect. The Cox proportional hazards model reveals that patients undergoing mastectomy have a 0.725 lower risk of death compared to those not undergoing the procedure, and patients receiving chemotherapy have a 1.869 higher risk of death. The findings underscore the importance of early and appropriate treatment in improving survival outcomes. This study contributes to the understanding of factors influencing breast cancer survival, aiding in better clinical decision-making and patient management strategies. Keywords: Breast Cancer, Cox Regression, Kaplan-Meier, Survival Analysis, Treatment Factors.
Metode DBSCAN dalam Pengelompokan Provinsi di Indonesia Berdasarkan Rasio Tenaga Kesehatan dan Tenaga Medis pada Tahun 2023 Maharani, Listia; Martha, Zamahsary; Permana, Dony; Zilrahmi
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/423

Abstract

Health is a fundamental right of every citizen. This right is realized in the form of health services. Good health services have an adequate ratio of health and medical personnel. However, in reality, there are still many provinces that have a shortage of health and medical personnel. Therefore, clustering is carried out to make it easier for the government to group provinces that have similarities in terms of the ratio of health and medical personnel in Indonesia in 2023. Density Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the clustering methods used. Using the DBSCAN method, two clusters were obtained with a silhouette coefficient value of 0.49. Cluster 0 is called noise because the observation points in group 0 are outliers. Cluster 0 consists of provinces with a higher ratio of healthcare and medical personnel than cluster 1.
Modeling Infant Mortality in West Pasaman Regency With Negative Binomial Regression to Overcome Overdispersion Vinna Sulvia; Fitri Mudia Sari; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/424

Abstract

Infant mortality serves as a vital indicator of public health and an essential benchmark of development progress. Although the general trend shows a decline, several sub-districts in West Pasaman Regency continue to report relatively high infant mortality rates, raising concerns about the effectiveness of current health services. This study seeks to examine the determinants of infant mortality using count data regression models. The data were obtained from the publication West Pasaman Regency in Figures 2025 by Statistics Indonesia (BPS), consisting of one response variable, the number of infant deaths, and five independent variables: the percentage of Low Birth Weight (LBW), the proportion of deliveries assisted by medical personnel, the proportion of pregnant women enrolled in the K4 program, the number of health workers, and the number of health facilities. The initial analysis employed a Poisson regression model, which assumes equidispersion, but the results revealed evidence of overdispersion. To address this issue, negative binomial regression was adopted as an alternative approach. Model evaluation using the Akaike Information Criterion (AIC) and the Likelihood Ratio Test confirmed that the negative binomial regression provided a better fit than Poisson regression. The results indicate that the percentage of LBW and the number of health facilities significantly influence infant mortality. Low birth weight (LBW) had a positive association with infant mortality, consistent with theory, while the positive effect of health facilities differed from expectations, possibly due to issues of quality, distribution, or reverse causality. 
Penalized Spline Regression Modeling on the Human and Cultural Development Index (IPMK) for 2022 Mila, Sarmilah; Fadhilah Fitri; Musthafa Imran
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (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-iss4/425

Abstract

Human and cultural development is a multidimensional phenomenon whose relationship with socioeconomic factors is often complex and nonlinear, making it challenging to model with conventional parametric approaches. This study aims to model the influence of socioeconomic variables on the Human and Cultural Development Index (IPMK) across 34 provinces in Indonesia in 2022 using the nonparametric Penalized Spline (P-spline) regression method within a Generalized Additive Model (GAM) framework. Secondary data from the Central Statistics Agency (BPS) were used, with predictor variables including School Participation Rate (APS), percentage of access to safe drinking water, Gini Ratio, per capita expenditure, average years of schooling (RLS), and open unemployment rate (TPT). Initial data exploration via scatterplots confirmed nonlinear relationship patterns between the predictor variables and IPMK. The best model was obtained using a first-order cubic spline with 10 knot points, selected based on the minimum Generalized Cross Validation (GCV) criterion. The modeling results demonstrated excellent performance, with an Adjusted R² value of 0.842 and a Deviance Explained of 92.3%. Significance analysis indicated that access to safe drinking water, per capita expenditure, average years of schooling, and the open unemployment rate significantly influence IPMK. Visual interpretation of the significant spline curves revealed informative relationship patterns, such as the diminishing returns effect of per capita expenditure. This study concludes that the P-spline approach is effective and interpretable for modeling complex nonlinear relationships in development data, providing a richer evidence base for policy formulation.

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