MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology)
Vol 10, No 2 (2018): MATICS

Identifikasi Kemiripan Teks Menggunakan Class Indexing Based dan Cosine Similarity Untuk Klasifikasi Dokumen Pengaduan

Iriananda, Syahroni Wahyu (Unknown)
Muslim, Muhammad Aziz (Unknown)
Dachlan, Harry Soekotjo (Unknown)



Article Info

Publish Date
22 Mar 2019

Abstract

Report handling on "LAPOR!" systemdepends on the system administrator who manually reads every incoming report [3]. Read manually can lead to errorsin handling complaints [4] if the data flow is very large and grows rapidly it can take at least three days and sensitive to inconsistencies [3]. In this study, the authors propose a model that can measure and identify the similarity of document reports computerized that can identify the similarity between the Query (Incoming) with Document (Archive). In this study, the authors employed term weighting scheme Class-Based Indexing, and Cosine Similarity to analyze document similarities. CoSimTFIDF, CoSimTFICF and CoSimTFIDFICF values are defined as feature sets for the text classification process using the KNearestNeighbor (K-NN) method. The optimum resultevaluation with preprocessing employ Stemming and the bestresult of all features is 75% training data ratio and 25% testdata on the CoSimTFIDF feature that is 84%. Value k = 5has a high accuracy of 84.12%

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Journal Info

Abbrev

saintek

Publisher

Subject

Computer Science & IT

Description

MATICS is a scientific publication for widespread research and criticism topics in Computer Science and Information Technology. The journal is published twice a year, in March and September by Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Maulana ...