IDENTIFYING POTENTIAL CREDIT CARD PAYMENT DEFAULTS USING GMDKNN WITH LOF AS OUTLIER HANDLING

In classifying data, accuracy results are greatly influenced by outliers. The presence of outliers can cause a low level of accuracy in the classification process. The Generalised Mean Distance K-Nearest Neighbor (GMD-KNN) algorithm is a classification technique that shows advantages in terms of flexibility and responsiveness to attribute variations. This research aims to classify credit card data between current and bad payments by handling outliers using the Local Outlier Factor (LOF). The data used is 30,000 credit card transaction data taken from the UCI Machine Learning Repository. This research method uses several stages, namely data collection, data pre-processing carried out to detect and clean outliers with LOF, classification process with GMD-KNN, and evaluation to calculate the accuracy of classification results. As a result, the model shows the best performance at 80%:20% data sharing ratio with k=5 value, achieving 77.60% accuracy, 74.97% precision, 82.57% recall, 78.58% F1-Score, and 77.48% G-Mean.