Chatbot Rekomendasi Buku Perpustakaan STT-PLN dengan Metode Latent Semantic Indexing
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Saragih, Rama Desi W.
Yosrita, Efy
Yudho, Satrio
Yosrita, Efy
Yudho, Satrio
Subject
Teknik Informatika
Datestamp
2022-09-23 03:02:56
Abstract :
In STT-PLN library book search system, book search process is only based on string matching so the results of the book search are still not in accordance with what visitors are searching for, because the system does not know the contents of the book. This research was conducted to provide book recommendations that are most suitable for those sought by users on a chatbot basis. Chatbot has a conversation with the user and retrieves book information sought by the user. The methods applied to build this system are Text Pre-processing and Latent Semantic Indexing (LSI). Text pre-processing is used to prepare documents before they are processed by the Latent Semantic indexing (LSI) method. Text pre-processing includes tokenizing, filtering, and stemming. Whereas Latent Semantic Indexing (LSI) is used to search for topics in books which are then made into book recommendations that are most suitable for what is sought. From the test results using the Latent Semantic Indexing method obtained an accuracy of 69.05% with an average execution time of 1.01 seconds and testing with mean squared error (MSE), the error value obtained is 30.95%.
In STT-PLN library book search system, book search process is only based on string matching so the results of the book search are still not in accordance with what visitors are searching for, because the system does not know the contents of the book. This research was conducted to provide book recommendations that are most suitable for those sought by users on a chatbot basis. Chatbot has a conversation with the user and retrieves book information sought by the user. The methods applied to build this system are Text Pre-processing and Latent Semantic Indexing (LSI). Text pre-processing is used to prepare documents before they are processed by the Latent Semantic indexing (LSI) method. Text pre-processing includes tokenizing, filtering, and stemming. Whereas Latent Semantic Indexing (LSI) is used to search for topics in books which are then made into book recommendations that are most suitable for what is sought. From the test results using the Latent Semantic Indexing method obtained an accuracy of 69.05% with an average execution time of 1.01 seconds and testing with mean squared error (MSE), the error value obtained is 30.95%.
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