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All Journal JUITA : Jurnal Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Ratna Sri hayati
Universitas Satya Terra Bhinneka
Computer Science & IT

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ENHANCING MACHINE LEARNING ALGORITHM PERFORMANCE FOR PCOS DIAGNOSIS USING SMOTENC ON IMBALANCED DATA Rofiqoh Dewi; Ratna Sri hayati; Alfa Saleh; Dahri Yani Hakim Tanjung; Abwabul Jinan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 1 (2025): JITK Issue August2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i1.6676

Abstract

Polycystic Ovarian Syndrome (PCOS) is one of the most frequently occurring endocrine disorders in women of reproductive age, distinguished by disruptions in hormonal regulation that can impact menstrual cycles, fertility, and physical appearance. Despite its high prevalence, PCOS is often diagnosed late and inaccurately, leading to inappropriate treatment and long-term health issues for patients. Machine learning can serve as an effective solution to enhance the accuracy of PCOS diagnosis. However, one of the primary challenges encountered is the class imbalance in the dataset, where the number of positive case data (PCOS) is often significantly lower than the negative case data. This imbalance can result in a biased model that is less effective in predicting the actual condition of patients. In this study, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC) method is recommended to address the issue of imbalanced data, thereby improving the performance and accuracy of the machine learning model employed. The evaluation matrix test results clearly demonstrate that the accuracy of each machine learning model improved after applying the SMOTENC method. Specifically, the accuracy of the K-Nearest Neighbors (KNN) algorithm increased from 81.6% to 89.8%, the Support Vector Machine (SVM) algorithm from 90.6% to 92.5%, the Naive Bayes algorithm from 70% to 82.3%, and the C4.5 algorithm from 99.6% to 99.7%. This research provides a substantial contribution to advancing the development of diagnostic methods thatare both more precise and efficient.
Comparative Evaluation of Ensemble Machine Learning Models for Child Stunting Prediction Using Routine Anthropometric Data in Indonesia Mikha Dayan Sinaga; Ratna Sri Hayati; Novriza Rahayu
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29709

Abstract

Child stunting remains a major public health challenge in Indonesia and continues to hinder progress toward the Sustainable Development Goals (SDGs), particularly in child health and nutrition. Early identification of at-risk children is therefore essential to support timely interventions. While previous machine learning studies on stunting prediction commonly incorporate socioeconomic, environmental, and behavioral variables, comparative evaluations based exclusively on routinely collected anthropometric indicators remain limited, particularly within Indonesian primary healthcare settings. This study evaluates the predictive performance of multiple machine learning models for stunting classification using only anthropometric and early-life growth indicators. A dataset consisting of 1,000 child records—including age, birth weight, birth length, current weight, current length, and breastfeeding status—was analyzed using Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Gradient Boosting algorithms. The dataset was partitioned using an 80:20 stratified train–test split, while five-fold cross-validation was applied during model development to improve robustness and reproducibility. Experimental results demonstrate that ensemble-based methods outperform single classifiers, with Gradient Boosting achieving the highest predictive performance (accuracy = 0.90, F1-score = 0.90, AUC = 0.93). Feature importance analysis reveals that birth length, birth weight, current weight, and age are among the most influential predictors of stunting risk. These findings suggest that machine learning models built solely on routinely collected anthropometric indicators can provide a practical, scalable, and data-driven approach for early stunting detection in Indonesian primary healthcare systems.
Co-Authors Abwabul Jinan Alfa Saleh Dahri Yani Hakim Tanjung Novriza Rahayu Rofiqoh Dewi Sinaga, Mikha Dayan