Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks
Helpdesk Menggunakan K-Nearest Neighbor
Dublin Core
Title
Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks
Helpdesk Menggunakan K-Nearest Neighbor
Helpdesk Menggunakan K-Nearest Neighbor
Subject
helpdesk, term weighting, text classification, tf-abs, tf-idf
Description
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.
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.
Creator
Riza Adrianti Supono1
, Muhammad Azis Suprayogi2
, Muhammad Azis Suprayogi2
Publisher
Universitas Gunadarma
Date
25-10-2021
Contributor
Fajar Bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Riza Adrianti Supono1
, Muhammad Azis Suprayogi2, “Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks
Helpdesk Menggunakan K-Nearest Neighbor,” Repository Horizon University Indonesia, accessed May 23, 2025, https://repository.horizon.ac.id/items/show/8926.
Helpdesk Menggunakan K-Nearest Neighbor,” Repository Horizon University Indonesia, accessed May 23, 2025, https://repository.horizon.ac.id/items/show/8926.