ANALISIS REKOMENDASI FILM MENGGUNAKAN ITEM-BASED COLLABORATIVE FILTERING DAN K-NEAREST NEIGHBOR (KNN)

SHAFIS, DHINKIS RAMADHANI POETRA (2025) ANALISIS REKOMENDASI FILM MENGGUNAKAN ITEM-BASED COLLABORATIVE FILTERING DAN K-NEAREST NEIGHBOR (KNN). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Recommendation systems have become a crucial element in helping users find information or products that match their preferences, especially in entertainment fields such as movies. This study implements an item-based collaborative filtering method combined with the K-Nearest Neighbor (KNN) algorithm to develop a movie recommendation system that is adaptive and responsive to user rating patterns. The dataset used, obtained from the Kaggle platform, includes approximately 110,000 ratings provided by users for more than 62,000 different movie titles. The model evaluation process focused on a single data split scenario, where 80% of the data is used for training and 20% for testing, and analyzed three variations of K values (10, 15, and 20). The evaluation results showed that the combination of K = 10 with a data ratio of 80%:20% produced the best performance, as indicated by an MAE value of 0.1820 and an RMSE of 0.0845. These findings indicate that the item-based approach using KNN is capable of providing fairly accurate and relevant rating predictions. Additionally, the filter feature based on movie genres also contributes to increased relevance and personalization in the recommendation system, thereby improving the overall user experience. Keywords: Collaborative Filtering, Genre, K-Nearest Neighbor, MAE, RMSE Sistem rekomendasi telah menjadi elemen yang sangat penting dalam membantu pengguna menemukan informasi atau produk yang sesuai dengan preferensi mereka, terutama dalam bidang hiburan seperti film. Penelitian ini mengimplementasikan metode penyaringan kolaboratif berbasis item yang dikombinasikan dengan algoritma K-Nearest Neighbor (KNN) untuk mengembangkan sistem rekomendasi film yang adaptif dan responsif terhadap pola penilaian pengguna. Dataset yang digunakan, yang diperoleh dari platform Kaggle, mencakup sekitar 110.000 data peringkat yang diberikan oleh pengguna untuk lebih dari 62.000 judul film yang berbeda. Proses evaluasi model difokuskan pada skenario pemisahan data tunggal, di mana 80% data digunakan untuk pelatihan dan 20% untuk pengujian, serta menganalisis tiga variasi nilai K (10, 15, dan 20). Hasil evaluasi menunjukkan bahwa kombinasi K=10 dengan rasio data 80%:20% menghasilkan performa terbaik, yang ditunjukkan oleh nilai MAE sebesar 0.1820 dan RMSE sebesar 0.0845. Temuan ini mengindikasikan bahwa pendekatan berbasis item yang menggunakan KNN mampu memberikan prediksi peringkat yang cukup akurat dan relevan. Selain itu, fitur filter berdasarkan genre film juga berkontribusi pada peningkatan relevansi dan personalisasi dalam sistem rekomendasi, sehingga meningkatkan pengalaman pengguna secara keseluruhan. Kata kunci: Collaborative Filtering, Genre, K-Nearest Neighbor, MAE, RMSE

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 151
NIM/NIDN Creators:	41521010113
Uncontrolled Keywords:	Collaborative Filtering, Genre, K-Nearest Neighbor, MAE, RMSE
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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 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 > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma 700 Arts/Seni, Seni Rupa, Kesenian > 790 Recreational and Performing Arts/Olah Raga dan Seni Pertunjukan > 791 Public Performances/Pertunjukan Umum > 791.4 Motion Pictures, Radio, Television/Gambar Gerak, Radio, Televisi > 791.43 Motion Pictures, Movies, Cinema/Bioskop, Movie, Film Hiburan, Sinema
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	15 Aug 2025 05:20
Last Modified:	15 Aug 2025 05:20
URI:	http://repository.mercubuana.ac.id/id/eprint/96815

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