PERBANDINGAN ALGORITMA CONTENT BASED FILTERING DAN COLLABORATIVE FILTERING UNTUK MEDETEKSI KONTEN PADA TIKTOK FAKULTAS ILMU KOMPUTER UNIVERSITAS MERCU BUANA.

RAMADHAN, MUHAMMAD RIZKY (2025) PERBANDINGAN ALGORITMA CONTENT BASED FILTERING DAN COLLABORATIVE FILTERING UNTUK MEDETEKSI KONTEN PADA TIKTOK FAKULTAS ILMU KOMPUTER UNIVERSITAS MERCU BUANA. S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

The popularity of TikTok as a short-video platform has rapidly expanded, especially among Indonesia's younger generation, including students of the Faculty of Computer Science (Fasilkom). However, the explosion of user-generated content has created a challenge for TikTok's algorithms in recommending relevant and engaging content. This study analyzes the effectiveness of two recommendation algorithms, Content-Based Filtering (CBF) and Collaborative Filtering (CF), in recommending Fasilkom TikTok content to be more relevant, engaging, and increase visibility. By using this approach, the research aims to help Fasilkom enhance branding, broaden reach, and optimize content strategy on TikTok. The analysis evaluates each algorithm’s performance in terms of relevance, user engagement, and its impact on follower growth and campus image. The results of this research are expected to provide insights into optimizing content recommendation algorithms to improve the effectiveness of Fasilkom's digital marketing strategy. Keywords: TikTok, Content-Based Filtering, Collaborative Filtering, content recommendation, algorithms Popularitas TikTok sebagai platform video pendek telah berkembang pesat, terutama di kalangan generasi muda Indonesia, termasuk mahasiswa Fakultas Ilmu Komputer (Fasilkom). Namun, ledakan konten yang dihasilkan pengguna membuat tantangan bagi algoritma TikTok untuk merekomendasikan konten yang relevan dan menarik. Penelitian ini menganalisis efektivitas dua algoritma rekomendasi, yaitu Content-Based Filtering (CBF) dan Collaborative Filtering (CF), dalam merekomendasikan konten TikTok Fasilkom agar lebih relevan, menarik, dan meningkatkan visibilitas. Dengan menggunakan pendekatan ini, penelitian bertujuan untuk membantu Fasilkom meningkatkan branding, memperluas jangkauan, dan mengoptimalkan strategi konten di TikTok. Analisis dilakukan dengan mengevaluasi kinerja masing-masing algoritma dalam hal relevansi, keterlibatan pengguna, serta dampaknya terhadap jumlah pengikut dan citra kampus. Hasil penelitian diharapkan dapat memberikan wawasan mengenai optimalisasi algoritma rekomendasi konten untuk meningkatkan efektivitas strategi pemasaran digital Fasilkom. Kata kunci: TikTok, Algoritma Rekomendasi, Content-Based Filtering (CBF), Collaborative Filtering (CF), Strategi Konten Digital

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 026
NIM/NIDN Creators:	41521010018
Uncontrolled Keywords:	TikTok, Algoritma Rekomendasi, Content-Based Filtering (CBF), Collaborative Filtering (CF), Strategi Konten Digital
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.7 Multimedia Systems/Sistem-sistem Multimedia > 006.75 Social Multimedia/Multimedia Social > 006.754 Online Social Network/Situs Jejaring Sosial, Sosial Media 300 Social Science/Ilmu-ilmu Sosial > 300. Social Science/Ilmu-ilmu Sosial > 302 Social Interaction, Interpersonal Relations/Interaksi Sosial, Hubungan Antarpersonal > 302.2 Communication/Komunikasi > 302.24 Content/Isi Komunikasi 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 540 Chemistry/Kimia > 542 Procedures, Equipment of Chemistry/Prosedur, Perlengkapan dan Alat-alat Kimia > 542.6 Filtering and Dialysis/Alat Penyaringan dan Penganalisa
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	07 Feb 2025 02:44
Last Modified:	07 Feb 2025 02:44
URI:	http://repository.mercubuana.ac.id/id/eprint/93979

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