Sistem Rekomendasi Tempat Magang Dengan Metode Latent Semantic Indexing (Studi Kasus: TempatMagang.Com)
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Susanto, Heri
Yosrita, Efy
Yudho, Satrio
Yosrita, Efy
Yudho, Satrio
Subject
Teknik Informatika
Datestamp
2022-09-23 03:04:01
Abstract :
At present there is no system that handles the process of finding an internship at the PLN Technical College. This research was conducted to make it easier for STT-PLN students to find an internship. This system will provide internship recommendations in accordance with the expertise possessed by students. 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 student vacancies and resumes / CVs, which will then be searched for cosine similiarity scores from vacancies and resumes / CVs. The result is an internship recommendation that is suitable for the student's expertise. From the test results using the Latent Semantic Indexing method obtained 85.05% accuracy results with an average execution time of 0.05 seconds and testing with mean squared error (MSE), the error value obtained 14.95%.
At present there is no system that handles the process of finding an internship at the PLN Technical College. This research was conducted to make it easier for STT-PLN students to find an internship. This system will provide internship recommendations in accordance with the expertise possessed by students. 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 student vacancies and resumes / CVs, which will then be searched for cosine similiarity scores from vacancies and resumes / CVs. The result is an internship recommendation that is suitable for the student's expertise. From the test results using the Latent Semantic Indexing method obtained 85.05% accuracy results with an average execution time of 0.05 seconds and testing with mean squared error (MSE), the error value obtained 14.95%.
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