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All Journal Building of Informatics, Technology and Science Bulletin of Computer Science Research
Joko Priambodo
Universitas Pamulang, Tangerang Selatan
Computer Science & IT

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Comparison of XGBoost and Random Forest for Prediction of Male Fertility Status Based on Semen Analysis Parameters Kecitaan Harefa; Joko Priambodo
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10264

Abstract

Male infertility is a major reproductive health problem that contributes to approximately half of infertility cases among couples of reproductive age. Accurate evaluation of male fertility status commonly relies on semen analysis, including parameters such as semen volume, sperm concentration, motility, morphology, and vitality. However, manual interpretation of these parameters remains time-consuming and is highly dependent on clinical expertise. This study aims to compare the performance of the XGBoost and Random Forest algorithms in predicting male fertility status based on semen analysis parameters. The study employed a secondary dataset consisting of 1,000 semen analysis records with 11 predictor variables and one target variable representing fertility status. Data preprocessing included categorical encoding, data cleaning, and an 80:20 train–test split before model development and evaluation using accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results showed that XGBoost outperformed Random Forest, achieving an accuracy of 99.00%, precision of 100.00%, recall of 96.88%, F1-score of 98.41%, and ROC-AUC of 99.86%, while Random Forest achieved an accuracy of 97.50%. Feature importance analysis identified Total Motility, Vitality, and Progressive Motility as the most influential predictors of male fertility status. The main contribution of this study is the direct empirical comparison of Random Forest and XGBoost under identical experimental settings using comprehensive semen analysis parameters, providing evidence on the relative effectiveness of ensemble learning algorithms for male fertility status prediction. Although the proposed model demonstrates excellent predictive performance, it was developed using secondary data and is intended to support, rather than replace, clinical decision-making. Future studies should validate the model using larger multicenter clinical datasets to improve its generalizability and practical applicability.
Implementation of the LightGBM–CatBoost Ensemble Method for Obesity Risk Classification in Productive Age Kecitaan Harefa; Joko Priambodo
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.930

Abstract

Obesity is a health problem that continues to increase among individuals of productive age and has the potential to reduce quality of life and work productivity. One of the main challenges in obesity risk assessment is the limitation of conventional methods in accurately identifying obesity risk when dealing with complex, multidimensional data that include both numerical and categorical variables. Therefore, an artificial intelligence–based approach is required to provide a more accurate and stable obesity risk classification. This study aims to implement and evaluate a LightGBM–CatBoost ensemble method for obesity risk classification with a focus on the productive age population. The dataset used in this study was obtained from the Kaggle platform and consisted of 2,111 individual records containing physical attributes, eating habits, physical activity, and lifestyle factors. Although the dataset is synthetic and balanced, the included attributes and age-related variables are representative of individuals within the productive age range, making it suitable for modeling obesity risk in this demographic context. The research stages include data preprocessing, separate training of the LightGBM and CatBoost models, model integration using a probability averaging ensemble technique, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that both LightGBM and CatBoost achieved accuracy levels above 95%, while the ensemble model demonstrated superior performance with an accuracy of 96.69% and more balanced evaluation metrics across all obesity risk classes. These findings confirm that the ensemble approach improves classification stability and accuracy compared to single models. Therefore, the LightGBM–CatBoost ensemble method is effective for obesity risk classification and has the potential to be further developed as a decision support system in the health sector.
Co-Authors Kecitaan Harefa