Y. Pristyanto

Stock-Out Prediction for FMCG Products Using CatBoost: An Empirical Comparison of Class-Imbalance Strategies with Native Categorical Handling and Probability Ensembling

Stock-out is a critical problem in fast-moving consumer goods (FMCG) supply chains because it directly causes lost sales and reduced customer satisfaction, yet its prediction is hindered by severe class imbalance. This study presents an empirical comparison of three class-imbalance strategies for CatBoost-based stock-o...

Automatic Chord Recognition Using CNN-LSTM Based on Mel Spectrogram

Cross-Dataset Evaluation of Boosting Models for Hypertension Prediction

It is demonstrated that dataset characteristics critically determine model effectiveness and that among the evaluated boosting algorithms, CatBoost delivered the strongest overall predictive performance.

Comparison of Machine Learning Algorithms for Sentiment Analysis of Trans Jogja on Social Media

The experimental results demonstrate that the Support Vector Machine (SVM) consistently outperformed the other algorithms across different data split ratios, indicating that it is the most effective algorithm for sentiment classification of Trans Jogja users on the X platform.