Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor

Abstract

Distribution of tickets to the destination unit is a very important function in the helpdesk application, but the process of distributing tickets manually by admin officers has drawbacks, namely ticket distribution errors can occur and increase ticket completion time if the number of tickets is large. Helpdesk text classification becomes important to automatically distribute tickets to the appropriate destination units in a short time. This study was conducted to compare the performance of helpdesk text classification at the Directorate General of State Assets of the Ministry of Finance using the K-Nearest Neighbor (KNN) method with the TF-ABS and TF-IDF weighting methods. The research was conducted by collecting complaint documents, preprocessing, word weighting, feature reduction, classification, and testing. Classification using KNN with parameters n_neighbor (k) namely k=1, k=3, k=5, k=7, k=9, k=11, k=13, k=15, k=17, and k=19 to classify 10,537 helpdesk texts into 8 categories. The test uses a confusion matrix based on the accuracy value and score-f1. The test results show that the TF-ABS weighting method is better than TF-IDF with the highest accuracy value of 90.04% at 15% and k=3.