Penerapan Algoritma Text Mining Dan TF-IDF Untuk Pengelompokan Topik Skripsi Pada Aplikasi Repository STMIK Budi Darma
-
Herlina Sari
*
Universitas Budi Darma, Medan,
Indonesia
- Guidio Leonarde Ginting Universitas Budi Darma, Medan, Indonesia
- Taronisokhi Zebua Universitas Budi Darma, Medan, Indonesia
- Mesran Mesran Universitas Budi Darma, Medan, Indonesia (*) Corresponding Author
Abstract
Thesis is a scientific work that must be written by students as a requirement for the final project of education. For students who want to write a thesis, for example, students on the STMIK Budi Darma campus are required to first find a topic for the title to be submitted. The way to find thesis topic references can be done by accessing the repository application. The title of the thesis has different topics, so it takes a grouping of thesis topics. Classification or grouping of thesis titles in the repository application is very important, because with the grouping of thesis titles it will make it easier to find thesis topic information that can be used as a reference in further research. Therefore, this study aims to create a repository application that is able to group theses. This research uses three methods, namely Text Mining, TF-IDF, and cosine similarity. The thesis abstract data will be processed by Text Mining to produce sentences that represent the thesis, then weighted using TF-IDF and find the level of similarity using cosine similarity. processed. So if the percentage is only 73%.
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References
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Pages: 414-432
Copyright (c) 2021 Herlina Sari, Guidio Leonarde Ginting, Taronisokhi Zebua, Mesran Mesran
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