Neighbor Weighted K-Nearest Neighbor for sambat online classification

Sambat Online is one of the implementation of E-Government for complaints management provided by Malang City Government. All of the complaints will be classified into its intended department. In this study, automatic complaint classification system using Neighbor Weighted K-Nearest Neighbor (NW-KNN) is poposed because Sambat Online has imbalanced data. The system developed is composed of three major phases including preprocessing, N-Gram feature extraction, and classification using NW-KNN. Based on the experiment results, it can be resumed that the NW-KNN algorithm is able to classify the imbalanced data well with the most optimal k-neighbor value is 3 and unigram as the best features by 77.85% precision, 74.18% recall, and 75.25% f-measure value. Compared to the conventional KNN, NW-KNN algorithm also proved to be better for imbalanced data problems with very slight differences.