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All Journal Imajiner: Jurnal Matematika dan Pendidikan Matematika Pelita Eksakta Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Mosharafa: Jurnal Pendidikan Matematika Leibniz: Jurnal Matematika UNP Journal of Statistics and Data Science Mathematical Journal of Modelling and Forecasting
Tessy Octavia Mukhti
Universitas Negeri Padang
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Factors Affecting Turnover Intention: A Survival Analysis Approach with the Stratified Cox Model Reihan Dani Eka Saputra; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): 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/vol4-iss2/475

Abstract

The phenomenon of employee resignation or turnover in Indonesia has reached a critical point that threatens operational stability and organizational competitiveness in the global market. The primary challenge faced by human resource practitioners is a reliance on static statistical models that fail to capture the temporal dimension and the evolving dynamics of risk. Conventional linear or logistic regression models often cannot accommodate censored data and may violate the proportionality assumption when applied to complex categorical variables such as profession. This study aims to model the determinants of turnover intention—including age, gender, and mode of transportation—by employing a more adaptive survival analysis approach. The main focus of the research is the application of a stratified Cox Proportional Hazards model to address violations of the Proportional Hazards assumption for the profession variable. Based on an analysis of 1,129 observations, the study identifies how turnover risk varies significantly across profession strata. We developed and compared two model configurations—with and without interaction terms—using the Akaike Information Criterion (AIC). While the non-interaction model proved most optimal for overall prediction (AIC: 5124.104), the interaction model revealed nuanced dynamics across professional strata. Key findings indicate that age generally increases turnover risk by 6.3% per year (HR: 1.063), and walking to work provides a protective effect, reducing risk by 13.6% (HR: 0.864) compared to bus usage. However, professional context significantly modulates these effects: in the 'Manage' stratum, age serves as a stabilizer (HR: 0.822), whereas male teachers face a risk 200.8% higher than their female counterparts (HR: 3.008). Furthermore, car usage in the 'Consult' stratum leads to a dramatic 423.5% increase in turnover risk (HR: 5.235). These results underscore the necessity of strata-specific retention strategies that prioritize workplace accessibility and demographic inclusivity. This study provides a robust data-driven framework for organizations to maintain workforce stability amidst the evolving labor landscape in Indonesia.
Regresi Data Panel dengan Kesalahan Standar Driscoll-Kraay: Analisis Kejahatan dan Indikator Sosial Ekonomi di Sumatera Barat (2017–2024) Andini Diva Luthfiyah; Dhio Ervandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): 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/vol4-iss2/479

Abstract

Criminal behavior is a complex social issue that threatens public safety and hinders regional development. In Indonesia, the crime rate varies across provinces and is influenced by multiple socioeconomic and structural factors. In West Sumatra Province, fluctuations in crime risk over time highlight the need for a deeper analysis of its determining factors. Understanding these factors is essential for the government to formulate effective and targeted crime prevention policies. This study aims to analyze the determinants of crime risk in West Sumatra Province using panel data from 2017 to 2024, covering 19 districts and cities, allowing for a more robust and comprehensive evaluation of both temporal and cross-sectional variations. The variables examined include the open unemployment rate, poverty rate, percentage of youth not in employment, education, or training (NEET), and the COVID-19 pandemic as a dummy variable. Panel data regression analysis was employed, and the results indicate that the most appropriate model is the Random Effects Model (REM). The findings show that the open unemployment rate and the pandemic variable have a significant effect on crime risk at the 5% significance level, while the poverty rate is significant at the 10% level. These results provide valuable insights for policymakers in addressing the root causes of crime in West Sumatra through employment generation, poverty alleviation, and preparedness for crisis situations.
Spatial Analysis of Open Unemployment Rate in West Java Province Using the Spatial Autoregressive Model Zulfadly Harman Harahap; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): 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/vol4-iss2/482

Abstract

Unemployment remains a major socio-economic issue in West Java Province. The Open Unemployment Rate (OUR) is affected not just by local regional elements but also by the circumstances of adjacent regions, showing that spatial interdependence exists.The research aims to analyze the spatial pattern of OUR in West Java and identify the influencing factors using the Spatial Autoregressive (SAR) approach. The study uses cross-sectional secondary data from all regencies and cities in West Java for the year 2023. Moran’s I findings indicate a positive spatial dependence, suggesting that regions with high OUR are typically surrounded by regions with similarly high unemployment rates. According to the analysis using the Lagrange Multiplier test, the SAR model was chosen. Estimation results show that population growth rate and government expenditure significantly affect OUR. Additionally, the spatial lag coefficient shows a positive and significant value, suggesting spatial spillover effects. These findings highlight the importance of incorporating spatial perspectives in formulating regional employment policies.
Application of the Cox Proportional Hazards Model to Analyze Survival Times in Women with Breast Cancer Rahmadani; Vinna Sulvia; Fathina Nafisa; Septrina Kiki Arisandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): 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/vol4-iss2/485

Abstract

Breast cancer is still claimed to be one of the most number causes of cancer-related mortality all round the world, highlighting the importance of identifying factors that influence patient survival time. Variations in clinical outcomes among patients indicate the need for appropriate statistical methods to evaluate prognostic factors. This studi aims to analyze factors affecting the survival time by applying the Cox Propotional Hazard (Cox PH) model. The data consist of breast cancer patient record with several predictor variabel, including age at diagnosis, type of breast surgery, chemotherapy, hormone therapy, Nottingham Prognostic Index, and tumor size. The analysis procedure includes testingthe propotional hazards assumption and assessing parameter significance using the likelihood ratio test for simultaneous affect and also the test of wald for partial effect. The resuls show that the propotional hazards assumption is satisfied, indicating that the Cox PH model is appropriate for the data. Simultaneous testing reveals that at least one predictor significanly affect survuval time, while partial testing identifies type of surgery, chemotherapy as significant factors. The hazard ratio estimates indicate that patients undergoing mastectomy have a lower risk of death compared to those receiving breast-conserving surgery. Conversely, chemotherapy and hormone theraoy are associated with a higher risk of death, wich may reflect the more severe clinical conditions of patients receiving these treatments. In conclusion, the Cox PH model provides a reliable approach for identifying key factors influetncing breast cancer survival and offers important implications for clinical decision-making and treatment planning.
Application of Extreme Learning Machine Algorithm (ELM) in Forecasting Inflation Rate in Indonesia Yonggi Septa Pramadia Yonggi; Zamahsary Martha; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/194

Abstract

One indicator to determine the economic stability of a country can be seen from the inflation rate of a country. Inflation is an economic symptom in the form of a general increase in prices or a tendency to increase the prices of goods and services in general and continuously. In an effort to anticipate the impact of inflation in the future, an analysis is needed to find out how the development of the inflation rate is by forecasting. Extreme Learning Machine (ELM) is a feed-forward artificial neural network (ANN) algorithm with one hidden layer called Single Hidden Layer Neural Networks (SLFNs). Based on the research, forecasting the inflation rate in Indonesia using the Extreme Learning Machine algorithm obtained the best architecture  (12,48,1) with a MAPE value of 11%. These results show good forecasting because the resulting MAPE is relatively low.
Metode Density Based Spatial Clustering of Applications with Noise (DBSCAN) dalam Mengelompokkan Provinsi di Indonesia Berdasarkan Kasus Kriminalitas Tahun 2022 Syifa Miftahurrahmi; Zilrahmi; Nonong Amalita; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/203

Abstract

Based on Central Statistics Agency 2023 data, in 2022 there was a significant increase in the number of crime cases in Indonesia compared to 2021, from 239,481 cases to 372,965 cases. The increase in the number of criminal acts occurred along with community activities that began to loosen up after the Covid-19 pandemic. The types of crimes that occur in Indonesia themselves vary, ranging from murder, theft, drug-related crimes, and others. This research will cluster provinces in Indonesia based on crime cases with certain types of crimes in 2022 using the Density Based Spatial Clustering of Applications with Noise (DBSCAN) method. The results of the study are expected to help the government and police in an effort to deal with crime in Indonesia. Clustering using the DBSCAN method produces 2 clusters with a silhouette coefficient value of 0,68. The resulting cluster is cluster 0 with noise category consisting of 5 provinces with a high number of crime cases, while cluster 1 consists of 29 provinces with a low number of crime cases.
Comparison of Linear Discriminant Analysis with Robust Linear Discriminant Analysis Fitri Hayati Fitri; Dodi Vionanda; Yenni Kurniawati; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/206

Abstract

Discriminant analysis is a multivariate method for dividing things into discrete groups and assigning new objects to existing categories. A discriminant function, which is a linear combination of independent variables used to categorize things into two or more groups or categories, is the result of discriminant analysis. The independent variables in a linear discriminant analysis must be multivariate normally distributed, and the covariance matrices for each group must be equal. In linear discriminant analysis, it is also essential to identify outliers because their existence in the data set can undermine the assumptions made by the method and lead to incorrect classification results. Therefore, in discriminant analysis, handling outliers with robust approaches is required. One such robust method in discriminant analysis is the Minimum Covariance Determinant (MCD), which is highly effective in dealing with outliers and relatively easier to apply compared to other robust methods. The aim of this study is to compare the classification results of linear discriminant analysis with robust linear discriminant analysis on the dataset of diabetes patients at RSUD Padangsidimpuan in 2023. The results obtained from this dataset indicate that linear discriminant analysis achieved an accuracy of 85,71%, while robust linear discriminant analysis achieved an accuracy of 80,95%. These findings suggest that the use of liniar discriminant analysis and robustt linear discriminant analysis can yield different results depending on the characteristics of the data and the number of outliers in the dataset.
Mixed Geographically Weighted Regression Modeling of Gender Development Index in Indonesia Nikma Hasanah; Dodi Vionanda; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/207

Abstract

The Gender Development Index (GDI) is one of the primary measures of gender equality in the field of human development. Indonesia's GDI statistics for 2023 show the development gap between men and women. Using Mixed Geographically Weighted Regression (MGWR), a blend of regression and Geographically Weighted Regression (GWR) models, to identify the factors influencing GDI is one approach to closing the gap. The results showed that when it came to value selection using the Akaike Information Criterion (AIC), the MGWR model outperformed the GWR model. Population with health complaints and adjusted per capita expenditure were found to be globally influential factors, while female participation in parliament, open unemployment rate, and labor force participation rate were found to be locally influential factors by the MGWR model with Adaptive Kernel Bisquare weights.
Implementation of the Fuzzy C-Means Clustering Method in Grouping Provinces in Indonesia based on the Types of Goods Sold in E-commerce Businesses in 2022 Bimbim Oktaviandi; Tessy Octavia Mukhti; Yenni Kurniawati; Zamahsary Martha
UNP Journal of Statistics and Data Science Vol. 2 No. 3 (2024): 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/vol2-iss3/210

Abstract

The internet facilitates e-commerce by enabling efficient transactions and building consumer trust. With internet users in Indonesia reaching 204 million in 2022, it is crucial to Cluster provinces based on the types of goods and services sold online to design effective marketing strategies. The Fuzzy C-Means (FCM) method is used for Cluster analysis, allowing objects to have different membership degrees in multiple Clusters and providing accurate Cluster center placement. This study applies Fuzzy C-Means to Cluster 34 provinces in Indonesia based on the sale of goods/services in e-commerce in 2022, aiming to provide insights into market preferences and assist companies in developing more effective strategies. The results show that the method forms two Clusters. By evaluating standard deviation values and ratios, Fuzzy C-Means proves effective in Clustering provinces in Indonesia based on e-commerce sales data. Cluster validation reveals a standard deviation ratio of 0.14, indicating clear and significant Cluster separation.
Classification of Poor Households in Padang City Using the Naïve Bayes Algorithm with Synthetic Minority Oversampling Technique anice kartika; Dina Fitria; Syafriandi Syafriandi; Tessy Octavia Mukhti
UNP Journal of Statistics and Data Science Vol. 2 No. 4 (2024): 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/vol2-iss4/241

Abstract

Poverty is a condition where a person is unable to meet minimum basic needs or a condition caused by the influence of development policies that have not been able to reach all levels of society. In Indonesia, the government has designed various programs to overcome poverty, but these programs are often not on target. One method to improve the effectiveness of the program is through proper classification of poor and non-poor households. This study uses the Naïve Bayes classification method which is popular in data mining to predict data categories based on the probability distribution of its features. However, challenges arise when the data is unbalanced between different classes. To overcome this, the Synthetic Minority Oversampling Technique (SMOTE) method is used to balance the data. Based on the analysis that has been carried out To determine the performance of Naïve Bayes using SMOTE and without SMOTE in classifying poor households in Padang City in 2023, classification using the Naïve Bayes method without SMOTE produced an accuracy value of 98%, precision of 0%, and recall of 0%. Meanwhile, the classification using the Naïve Bayes method with SMOTE produces an accuracy value of 90%, precision of 87%, and recall of 92% and the results of the criteria for poor households in Padang City in 2023 using Naïve Bayes can be seen from the results that the probability of poor households is much greater than that of non-poor households, therefore the data is classified as  group of households that are classified as poor.
Co-Authors Adinda Putri Adrianingsih, Narita Y. Afifah Nabilah Aisyah Novriani Andini Diva Luthfiyah anice kartika Annisa Ramadhani Azizah Apriyerni Berliana Nofriadi Bimbim Oktaviandi Bunga Miftahul Barokah Bunga Nafandra Devni Prima Sari Dhio Ervandi Dicha Putri Yeni Dina Fitria Dina Fitria, Dina Dinda Putri Adilla Dodi Vionanda Dodi Vionanda Dony Permana Ervi Dayana Putri Fadhilah Fitri Fadhilah Fitri Fajri Juli Rahman Nur Zendrato Fathina Nafisa Fauzan Al Hamdani Siregar Fauzan Al-Hamdani Siregar Fauzan Arrahman Fauzan Gustiandra Fenni Kurnia Mutiya Figo Rahmatullah Fitri Hayati Fitri Fitri, Fadhilah Herlena Purnama Sari Khairani, Putri Rahmatun Khairanisa Salsabila Luthfiyah, Andini Diva Muhammad Arief Rivano Nikma Hasanah Nonong Amalita Nurul Mulya Nurul Mulya Syahwa Olga Afrilly Putri Olivin Adelia Huqmi Padafani, Lekison Permana, Dony Putri fajriyanti nur Rahmadani Rahmat Kurniawan Rahmatul Annisa Rahmika Alya Reihan Dani Eka Saputra Rizki Akbar Rudi Anggara Salma, Admi Sari Agustin Sari, Nilam N. Sari, Widia Kemala Sepniza Nasywa Septrina Kiki Arisandi Septrina Kiki Arisandi Siti Nurhaliza Sonia Ardhi Sri Wahyuni Suci Rahmadani Suliswati, Yeni Syafriandi Syafriandi Syafriandi Syafriandi Syifa Miftahurrahmi Taslim, Fauziah Vinna Sulvia Wafiq Alya Aufa Yenni Kurniawati Yoli Marda Novi Yonggi Septa Pramadia Yonggi Yusra, Zulmi Yusra Zamahsary Martha Zilrahmi, Zilrahmi Zulfadly Harman Harahap