<![CDATA[Palang Merah Indonesia (PMI) Kudus Regency faces an imbalance of blood stock across blood types, frequently causing difficulties in meeting blood demand during emergencies. Manual donor data management has been the primary factor behind slow donor recruitment processes. This study develops a machine learning-based prediction system using the Random Forest algorithm to identify blood donor volunteers with a high likelihood of donating again. The system is built as a web application using the CodeIgniter framework and Python, integrated with a Geographic Information System (GIS) via Leaflet.js for spatial visualization of donor distribution. System development follows the CRISP-DM approach, using historical donor data from PMI Kudus Regency as model training material. Evaluation results demonstrate strong model performance with an accuracy of 87.45% and a ROC-AUC of 0.912, while a cross-validation ROC-AUC of 0.897 confirms model consistency without overfitting. The system generates a ranked list of potential donors based on the highest probability values, organized by sub-district and blood type, thereby accelerating PMI officers' response in fulfilling blood demand. ]]>
Copyrights © 2025
