UTS -Three assignments for the Disaster Data and Information Management | Madya Ashari - F5522630020

This presentation summarizes and analyzes three assignments for the Disaster Data and Information Management course at Universitas Tadulako. The presentation covers: Disaster logistics prioritization using a Decision Tree in RapidMiner to classify aid distribution posts into low, medium, and high priority. Exploratory analysis of the Medellín Landslides dataset using Python, focusing on class imbalance, category interpretation, skewed features, and outliers. Classification of US avalanche fatality records using Decision Tree and Random Forest, with five-fold stratified cross-validation in Python and RapidMiner. The analysis explains why high accuracy alone can give a misleading picture of model performance. It discusses minority-class detection, precision, recall, F1 scores, and the importance of interpreting data within its disaster management context. Presenter: Madya Ashari Student ID: F5522630020 Program: Master of Information Technology University: Universitas Tadulako Course: Manajemen Data dan Informasi Kebencanaan (MDIK) Presentation language: English This video presents coursework findings and their limitations as a basis for further analysis. #DisasterDataAnalytics #DataMining #DisasterManagement