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All Journal Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research
Muhammad Hilmy Aziz
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Humanities Economics, Econometrics & Finance Education Social Sciences Other

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Sistem Rekomendasi Linimasa Facebook Berdasarkan Topik Kesukaan Pengguna Menggunakan Metode Content-Based Filtering & Term Frequency-Inverse Document Frequency Bayu Setiawan; Bagus Satrio Wicaksono; Muhammad Hilmy Aziz; Anggraini Puspita Sari
MISTER: Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 1 No. 3c (2024): JULI (Tambahan)
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v1i3c.2004

Abstract

This research aims to identify Facebook timeline recommendation systems based on user habits using Content-Based Filtering (CBF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. By utilizing historical user activity data, this system will customize content recommendations for individual users. The CBF method is used to compare the similarity of the content to be recommended with the user's habits, while TF-IDF is used to evaluate various keywords in the content. The results of this study show that the combination of CBF and TF-IDF methods can improve the accuracy of recommendation in Facebook timeline, resulting in a more convenient and relevant user experience.
Co-Authors Anggraini Puspita Sari Bagus Satrio Wicaksono Bayu Setiawan