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Tessy Octavia Mukhti
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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 202 Documents
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Grouping Regencies/Cities in West Sumatra Province Based on People’s Welfare Indicator Using Biplot Analysis Maya Ifra Shobia; 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-iss3/407

Abstract

The level of community welfare is a crucial reflection of the success of development in a region. Welfare is assessed based on eight aspects: poverty, employment, education, housing, consumption patterns, health, population, and other social factors. In West Sumatra Province, the level of community welfare still requires improvement across all indicators. The determination of community welfare levels can be achieved by reviewing all dimensions based on the linear relationships between districts/cities, thereby providing insights into the indicators that still need enhancement. This effort can assist the West Sumatra Provincial Government in formulating regional policies and programs for equitable distribution and improvement of community welfare across all districts/cities. The data used in this study are secondary data obtained from the West Sumatra Provincial BPS website in 2024. The grouping of districts/cities was conducted using Principal Component Analysis based on singular value decomposition biplot analysis. The analysis results formed four groups with distinct characteristics of community welfare indicators. The groups that need to be prioritized for improvement are groups 1 and 3, which exhibit low levels of community welfare. Group 2 consists of districts/cities with high community welfare characteristics in terms of population, education, and housing. Meanwhile, group 4 includes districts/cities with high community welfare characteristics regarding consumption patterns, poverty, and labor indicators.
Aplication Algorithm Learning Vector Quantization for Classification of Hypertention in Padang Laweh Health Center Harpidna, Riska Harpidna; Chairina Wirdiastuti; 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-iss3/408

Abstract

Hypertension is a health condition characterized by blood vessel disorders, in which there is a chronic increase in blood plessure of 140/90 mmHg. There are several factors that influence hypertension, including unhealthy eating patterns, lack of physical activity, smoking, stress and excess weight. Hypertension does not show clear symptoms, but it has the potential to cause other diseases such as heart failure, stroke, and premature death. Therefore, a study was conducted to classify the risk of hypertension based on hypertension diagnoses at the Padang Laweh Health Center, Dharmasraya Regency, using the Learning Vector Quantiazation (LVQ) Algorithm. The advantage of LVQ is its ability to achieve high accuracy in processing data with numerous numerical and categorical features. The analysis results show that the use of the Learning Vector Quantization Algorithm on the test data produces very good accuracy, namely 95.17% correct classification of hypertensive patients
Applications of Panel Data Analysis on Human Development Index Indicators in Districts/Cities of Lampung 2022 – 2024 Rahmad Wanizal Pastha; Zilrahmi; 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-iss3/411

Abstract

This paper aims to identify the determinants affecting the Human Development Index (HDI) in Lampung Province, Indonesia, during the periode 2022-2024 using panel data regression. Lampung consistenly ranks among the provinces with the lowest HDI scores in Sumatera, indicating developmental disparties across regions. The research employs secondary data from 15 districts/cities and includes variables such as life expectancy, expected years of schoolingm mean years of schooling, and expenditure per capita. Panel data regression models fixed effect, random effect, and common effect were evaluated using chow, hausman, and lagrang multiplier tests to select the most approriate model. The random effect model was chosen, supported by a high R-Squared value of 92,71% indicating strong explanatory power. The analysis found that life expectancy and mean years of schooling significantly influence HDI, while expected years of schooling and expenditure per capita were not statistically significant in this model. The analysis shows that ensuring equal opportunities in health and education significantly contributies to better human development. Future research is recomended to incorporate qualitative approaches and more recent variables to enrich the analysis.
Inflation Prediction In Indonesia Using Extreme Learning Machine and K-Fold Cross Validation Wahda Aulia Assara; Zamahsary Martha; Dony Permana; Dina Fitria
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-iss3/412

Abstract

Inflation rate forecasting is an important aspect in supporting economic policies and price control by the government. This study aims to evaluate the performance of the Extreme Learning Machine (ELM) algorithm in forecasting the inflation rate in Indonesia and provide inflation prediction results for 2025. The data used is historical data on Indonesia's inflation rate for the period 2003–2024. The analysis process begins with data normalization to ensure a uniform scale, followed by data partitioning using 10-Fold Cross Validation. The ELM model was built with 30 hidden neurons, a sigmoid activation function, and a regularization parameter of 0.8. The test results show that the ELM algorithm has superior performance. This is evidenced by the average MAPE value of 1.71%, RMSE of 0.0359, and coefficient of determination (R²) of 0.9833, indicating very high accuracy. The inflation prediction for January to December 2025 is in the range of 1.517%–1.761%, with an average approaching 1.663%, indicating a relatively stable pattern throughout the year. Based on these results, the ELM algorithm can be used as an effective alternative method for forecasting time series data, particularly in the context of inflation. This research is expected to serve as a reference for the government in establishing inflation control policies and for other researchers interested in applying artificial intelligence models to economic analysis.
Forecast Accuracy Comparison Between Holt’s Method and the Box-Jenkins Approach: The Case of Madiun City Labor Force Participation Rate Shobri, Muhammad Qolbi; Yan Aditya Pradana; Putri Balqis Al-Kubro; Nayla Desviona; Nila Destia Nasra
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-iss3/413

Abstract

Pacitan district recorded the highest Labor Force Participation Rate (LFPR) in Eastern Java Province. Meanwhile, Madiun city which is one of the largest cities in East Java Province, is ranked only 34 out of 39 cities in 2023. This condition raises concern for the local government, perticularly the Department of Manpower,in ensuring that the productive-age population can be optimally absorbed into the labor market. The LFPR is categorized as time series data, thus forecasting method are required to estimate its future trends.This Study compares the performance of the Double Exponential Smoothing Holt (DESH) method and the Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins approach in forecasting the LFPR of Madiun City. The empirical result show that the ARIMA (1,0,1) model provides better accuracy compared to DESH. The forecasting result  indiacte that the LFPR of Madiun City is project to reach 67,19% in 2024, 67,20% in 2025, and 67,21% in 2026, with  Mean Squared Error (MSE) of 14,48; Root Mean Square Error (RMSE) of 3,80 and Mean Absolute Percentage Error (MAPE) of 4,75%. These finding are expected to serve as reference for future research and practical input for policymakers in formulating strategies to improve labor LFPR in Madiun City.
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.

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