Article Per Year (5 Year)
p-Index From 2021 - 2026
4.425
P-Index
This Author published in this journals
All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) UNP Journal of Statistics and Data Science
Tessy Octavia Mukhti
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Social Sciences

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search
Journal : UNP Journal of Statistics and Data Science

 1   2   3   4 
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.
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%.
Grouping of Provinces in Indonesia Based on Active Family Planning Participants Using Modern Methods Using Fuzzy C-Means Ramadhani, Annisa; Tessy Octavia Mukhti; Yenni Kurniawati; Zamahsary Martha
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-iss2/365

Abstract

Indonesia’s rapid population growth presents a significant challenge to national welfare and public health. One of the key strategies implemented by the government to address this issue is the Family Planning (FP) program, which emphasizes the use of modern contraceptive methods. However, the utilization of these methods remains uneven across provinces. This study aims to cluster Indonesian provinces based on the number of active participants using modern contraceptive methods in 2023 by applying the Fuzzy C-Means (FCM) clustering algorithm. FCM was selected due to its ability to handle overlapping data characteristics, allowing for a more flexible and representative analysis. The clustering results reveal two main clusters: Cluster 1, which consists of provinces with high levels of active modern contraceptive users, and Cluster 2, which includes provinces with low participation levels. These findings are expected to serve as a reference for more targeted policy formulation to enhance the equity and effectiveness of the FP program across the country.
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.
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.
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.
Peramalan Konsentrasi PM2.5 di Kota Medan Menggunakan Metode ARIMAX dengan Faktor Meteorologi sebagai Variabel Eksogen Fauzan Arrahman; Tessy Octavia Mukhti; Dony Permana; Fenni Kurnia Mutiya
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-iss4/429

Abstract

Particulate Matter 2.5 (PM2.5) is a fine particle measuring less than 2.5 micrometers which is dangerous for human health because it can penetrate the respiratory system and cause cardiovascular disorders. High PM2.5 concentrations reflect a decline in air quality, so forecasting efforts are needed to support pollution control and environmental policies. This study aims to forecast daily PM2.5 concentrations in Medan City using the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method by considering meteorological factors as exogenous variables. The data used consist of PM2.5 concentrations and average temperature, humidity, rainfall, and wind speed data for the period from June 1, 2024 to June 10, 2025. The analysis results show that the best model is ARIMAX (4,1,0) with exogenous variables of average temperature and rainfall, where temperature has a positive effect and rainfall has a negative effect on PM2.5. This model meets the assumptions of white noise and residual normality, with a MAPE value of 20.635%, indicating a fairly good level of forecasting accuracy. The forecasting results show PM2.5 concentrations in the range of 19–26 µg/m³ with a downward trend at the end of June 2025, indicating improved air quality in Medan City. Thus, the ARIMAX method with meteorological factors is considered effective in modeling and forecasting PM2.5 dynamics in urban areas.
Co-Authors Adinda Putri Aditya, Muhammad Fadhil Aditya Admi Salma Afifah Nabilah AL Rezki Ivansyah Andini Diva Luthfiyah Anggara, Rudi Annisa Ramadhani Ardhi, Sonia Arssita Nur Muharromah Atus Amadi Putra Azizah Apriyerni Baehaqi Berliana Nofriadi Bimbim Oktaviandi Bunga Miftahul Barokah Bunga Nafandra defal aditya defran Dewi Febiyanti Dina Fitria Dina Fitria, Dina Dodi Vionanda Dony Permana Ervi Dayana Putri Fadhilah Fitri Fajri Juli Rahman Nur Zendrato fajriyanti nur, Putri Fauzan Arrahman Fenni Kurnia Mutiya Fikra, Hidayatul Fitri, Fadhilah Fitri, Fitri Hayati Hariati Ainun Nisa Herlena Purnama Sari Ihsan Dermawan Inna Auliya kartika, anice Martia Rosada Muhammad Arief Rivano Mulya, Nurul Nikma Hasanah Nonong Amalita Novi, Yoli Marda Nurul Mulya Syahwa Olga Afrilly Putri Putri Yeni, Dicha Rahmatul Annisa Rahmika Alya Rivani, Putri RR. Ella Evrita Hestiandari Sari Agustin Sepniza Nasywa Septrina Kiki Arisandi Siregar, Fauzan Al-Hamdani Siti Nurhaliza Sri Wahyuni Syafriandi Syafriandi Wafiq Alya Aufa Yenni Kurniawati Yoli Marda Novi Yonggi, Yonggi Septa Pramadia Zamahsary Martha Zilrahmi, Zilrahmi