AdaBoost Classification for Predicting Residential Habitation Status in Mount Merapi Post-Eruption Rehabilitation

This research paper explores the use of AdaBoost algorithm for predicting residential habitation status in aftermath Mount Merapi eruption. Using a dataset from Rehabilitation and Reconstruction Task Force, with 2516 instances 11 attributes, model was trained evaluated. The demonstrated robust performance an accuracy 92%, though it struggled correctly identifying all 'No Habited' instances. These findings underscore potential machine learning algorithms disaster management, particularly optimizing resource allocation expediting recovery times. Future should aim to improve model's on imbalanced datasets explore incorporation temporal dimensions more dynamic accurate predictions.