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

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 larg...

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Autores principales:	Riza Adrianti Supono, Muhammad Azis Suprayogi
Formato:	article
Lenguaje:	ID
Publicado:	Ikatan Ahli Indormatika Indonesia 2021
Materias:	helpdesk term weighting text classification tf-abs tf-idf Systems engineering TA168 Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/6c203faf34ea4306ad7fdbb0a8c24513
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spelling	oai:doaj.org-article:6c203faf34ea4306ad7fdbb0a8c245132021-11-16T13:16:12ZPerbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor2580-076010.29207/resti.v5i5.3403https://doaj.org/article/6c203faf34ea4306ad7fdbb0a8c245132021-10-01T00:00:00Zhttp://jurnal.iaii.or.id/index.php/RESTI/article/view/3403https://doaj.org/toc/2580-0760Distribution 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.Riza Adrianti SuponoMuhammad Azis SuprayogiIkatan Ahli Indormatika Indonesiaarticlehelpdeskterm weightingtext classificationtf-abstf-idfSystems engineeringTA168Information technologyT58.5-58.64IDJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 5, Pp 911-918 (2021)
institution	DOAJ
collection	DOAJ
language	ID
topic	helpdesk term weighting text classification tf-abs tf-idf Systems engineering TA168 Information technology T58.5-58.64
spellingShingle	helpdesk term weighting text classification tf-abs tf-idf Systems engineering TA168 Information technology T58.5-58.64 Riza Adrianti Supono Muhammad Azis Suprayogi Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor
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.
format	article
author	Riza Adrianti Supono Muhammad Azis Suprayogi
author_facet	Riza Adrianti Supono Muhammad Azis Suprayogi
author_sort	Riza Adrianti Supono
title	Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor
title_short	Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor
title_full	Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor
title_fullStr	Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor
title_full_unstemmed	Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor
title_sort	perbandingan metode tf-abs dan tf-idf pada klasifikasi teks helpdesk menggunakan k-nearest neighbor
publisher	Ikatan Ahli Indormatika Indonesia
publishDate	2021
url	https://doaj.org/article/6c203faf34ea4306ad7fdbb0a8c24513
work_keys_str_mv	AT rizaadriantisupono perbandinganmetodetfabsdantfidfpadaklasifikasitekshelpdeskmenggunakanknearestneighbor AT muhammadazissuprayogi perbandinganmetodetfabsdantfidfpadaklasifikasitekshelpdeskmenggunakanknearestneighbor
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Perbandingan Metode TF-ABS dan TF-IDF Pada Klasifikasi Teks Helpdesk Menggunakan K-Nearest Neighbor

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