Dimas Ariyanto

This project analyzed patient clinical data to identify risk factors for heart disease using Extreme Learning Machine (ELM). With a dataset that includes 12 health features, the project aims to find patterns that can aid early detection.

This project predicts whether a customer will approve a personal loan based on demographic and behavioral data using Support Vector Machine (SVM) with RBF kernel. The model handles class imbalance with SMOTE and evaluates performance with accuracy, precision, recall, and confusion matrix.

This project predicts gold prices based on historical market data using Bi-LSTM. The model is trained with price and volume features, and evaluated using MAPE to measure prediction accuracy.