Analisis perbandingan Algoritma Support Vector Machine, Naive Bayes dan Regresi Logistik untuk Memprediksi Donor Darah - Hendriyana Hendriyana; Ichwanul Muslim Karo Karo; Sri Dewi (LPPM STT Terpadu Nurul Fikri, 2022)
Blood supplies and stocks are urgently needed. Regular donations from healthy volunteers are the only way to keep up with the blood supply. This research aims to develop and evaluate a machine-learning algorithm to predict whether a volunteer will donate or not. The machine learning algorithms are Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). This study also applies the process of normalizing data with a Z-score to standardize the dataset scale. The dataset is sourced from the Hsin-Chu City Blood Transfusion Service, Taiwan, and stored in the UCI repository. The evaluation methods are accuracy, precision, recall, and F-1 score. The research results with the Naïve Bayes algorithm were 89.90%, Logistic Regression 82.59%, and SVM 94.79%. The normalization process using the Z-Score method contributes positively to improving the performance of the classification model. Based on this performance, it provides predictive results for volunteers who will return to donate blood to offer blood reserves to those in need.