A Comprehensive Evaluation of CatBoost and LightGBM Algorithms for Honorarium Prediction on Categorical Datasets with Class Imbalance - Slamet Widodo; Fandy Setyo Utomo; Berlilana (Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto, 2025)
Determining income, including honoraria in the academic environment, is often done manually and subjectively, necessitating a predictive model to objectively determine the honorarium amount. However, the development of the prediction model faces challenges due to the dataset's characteristics, which include categorical data and an imbalanced class distribution. This research aims to evaluate the predictive performance and computational resource efficiency of the CatBoost and LightGBM algorithms in predicting honorariums. The dataset used includes 58,332 actual honorarium data of employees from higher education institution