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All Journal Jurnal Kesehatan Vokasional JTAM (Jurnal Teori dan Aplikasi Matematika)
Alisiah, Rindiani Ahmada
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Computer Science & IT Education Health Professions Mathematics Nursing Public Health

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Performance Evaluation of Machine Learning Algorithms for AIDS-Infected Patient Classification Kurniawan, Ardi; Marthabakti, Citrawani; Putri, Larisa Mutiara; Suyono, Billy Christandy; Alisiah, Rindiani Ahmada
Jurnal Kesehatan Vokasional Vol 10, No 3 (2025): August
Publisher : Sekolah Vokasi Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jkesvo.107716

Abstract

Background: According to UNAIDS (2023), approximately 39.9 million people are living with HIV worldwide, with 1.3 million new cases and 630,000 AIDS-related deaths in 2023. This indicates that HIV/AIDS remains a serious global health threat. Machine learning methods have the potential to improve the accuracy of AIDS infection classification.Objective: This research is aimed to determine the best classification method based on prediction accuracy and to identify the method with the best performance for further analysis.Methods: This research used a quantitative approach by evaluating the performance of machine learning algorithms: Decision Tree, Random Forest, XGBoost, Naive Bayes, and Logistic Regression. Secondary data were obtained from the UCI Machine Learning Repository, comprising 2,000 observations of AIDS patients and 23 variables. Model evaluation used a confusion matrix to calculate accuracy, precision, recall, and F1-score. The best model, logistic regression, was further analyzed with parameter significance tests, odds ratios, and goodness of fit.Results: Logistic regression yielded an accuracy of 88.4%, precision and recall of 90%, and the highest F1-score. Variables significant to AIDS were: time, preanti, symptom, offtrt, and cd420. The model passed the Hosmer and Lemeshow test (p-value = 0.365) with a Nagelkerke R-Square of 0.642.Conclusion: Machine learning approaches, particularly logistic regression, support early detection of AIDS and data-driven medical decision-making.
Assessment of Dietary Intervention Effects on Food Intake in Mus musculus using Repeated Measures ANOVA Suliyanto, Suliyanto; Amelia, Dita; Arrofah, Aini Divayanti; Alisiah, Rindiani Ahmada; Anida, Nuzulia; Maulidya, Utsna Rosalin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i4.32467

Abstract

The prevalence of type 2 diabetes, metabolic syndrome, along with obesity that causes disturbances in the body's metabolic processes are the main triggers of chronic liver disease or in scientific language called Non-Alcoholic Fatty Liver Disease (NAFLD), getting out of control. This makes managing this disease an increasingly serious global health challenge. One of the main factors influencing this condition is a high-fat diet and an unhealthy lifestyle. Therefore, evaluation of high-fat diet programs on metabolic parameters such as food intake patterns is important as a preventive measure. This study aims to analyze the differences in food intake levels with seven different types of dietary treatments for 28 days, which were tested on mice (Mus musculus) which have physiological and biochemical characteristics that almost resemble humans. The method used was analysis of variance (ANOVA) for longitudinal data to evaluate the dynamics of food consumption across diet groups and observation periods. The results showed that the type of dietary treatment significantly influenced food intake patterns over time, indicating that diet composition plays a crucial role in shaping eating behavior. These findings highlight the importance of both diet type and treatment duration in influencing consumption patterns. However, since this study has not yet identified the most effective dietary regimen, future research is recommended to investigate diet types with high variability, while considering additional factors such as age, sex, and physiological characteristics, as well as extending the observation period to better understand long-term impacts.
Co-Authors Anida, Nuzulia Ardi Kurniawan Arrofah, Aini Divayanti Dita Amelia, Dita Marthabakti, CitraWani Maulidya, Utsna Rosalin Putri, Larisa Mutiara Suliyanto Suliyanto Suyono, Billy Christandy