Data Mining UTS: Tasks 1-4 Summary and Analysis | Madya Ashari-F5522630020

This video presents a summary and analysis of four Data Mining assignments for my midterm examination in the Master of Information Technology program at Universitas Tadulako. The presentation covers: The Knowledge Discovery in Databases (KDD) framework and a proposed application for matching civil servants with suitable positions. Rain prediction using CRISP-DM, with a comparison of Decision Tree, Random Forest, and Logistic Regression. Exploratory analysis of the Rice Cammeo and Osmancik dataset, including grain characteristics and feature correlations. Classification of the imbalanced Oil Spill dataset using Decision Tree and Naive Bayes in RapidMiner and Python. The analysis discusses data preparation, model performance, precision, recall, F1 scores, and the consequences of prediction errors. It also explains the limitations of the reported results and the importance of interpreting them within each application’s context. Presenter: Madya Ashari Student ID: F5522630020 Program: Master of Information Technology University: Universitas Tadulako Course: Data Mining Assessment: Midterm Examination (UTS) Presentation language: English #DataMining #MachineLearning #UniversitasTadulako