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All Journal UNP Journal of Statistics and Data Science
Celsy Aprotama
Universitas Negeri Padang
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Application of K-Modes Clustering Method to Identify Low Birth Weight Factors in Central Sulawesi Province Celsy Aprotama; 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) Riza Sasmita; 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.
Evaluation of Prognosis and Duration of Survival in Breast Cancer Patients Using the Cox PH Model Dela Meliza; 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.
Co-Authors Dela Meliza Devi Yopita Sipayung Muhammad Arief Rivano Rahmat Kurniawan Riza Sasmita Riza Sasmita Sri Wahyuni Tessy Octvia Mukhti