Rosanne, Eva Marella (2022) Sistem Rekomendasi Film Menggunakan Metode Market Basket Analysis (MBA) Dengan Algoritma Apriori Dan Algoritma Frequent Pattern Growth (FP-Growth). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pertumbuhan jumlah film yang sangat pesat membuat penonton seringkali kesulitan dalam menentukan film yang akan ditonton.Salah satu cara untuk mengatasi permasalahan tersebut adalah dengan membangun sistem rekomendasi film.Sistem rekomendasi film dapat dibangun menggunakan metode Market Basket Analysis (MBA).MBA adalah suatu metode untuk mencari pola hubungan antar item dalam suatu data transaksi.Pada penelitian ini, sistem rekomendasi film dibangun menggunakan algoritma Apriori dan algoritma Frequent Pattern Growth (FP-Growth).Tujuan dari penelitian ini adalah membandingkan performa kedua algoritma tersebut dalam menghasilkan aturan asosiasi untuk rekomendasi film.Data yang digunakan dalam penelitian ini adalah dataset MovieLens 100K.Berdasarkan hasil analisis, kedua algoritma menghasilkan aturan asosiasi yang sama untuk nilai support dan confidence yang sama.Perbedaan mendasar terletak pada efisiensi waktu komputasi, dimana algoritma FP-Growth lebih cepat daripada algoritma Apriori karena tidak memerlukan pembangkitan candidate itemset yang besar.
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The rapid growth in the number of films makes viewers often have difficulty in determining which films to watch.One way to overcome this problem is to build a movie recommendation system.A movie recommendation system can be built using the Market Basket Analysis (MBA) method.MBA is a method for finding patterns of relationships between items in a transaction data.In this study, a movie recommendation system was built using the Apriori algorithm and the Frequent Pattern Growth (FP-Growth) algorithm.The purpose of this study is to compare the performance of these two algorithms in generating association rules for movie recommendations.The data used in this study is the MovieLens 100K dataset.Based on the analysis results, both algorithms produce the same association rules for the same support and confidence values.The fundamental difference lies in the efficiency of computational time, where the FP-Growth algorithm is faster than the Apriori algorithm because it does not require generating large candidate itemsets.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSSt 518.1 Ros s-1 2022 |
| Uncontrolled Keywords: | Algoritma Apriori. FP-Growth. MBA. Rekomendasi Film. Apriori Algorithm. FP-Growth. MBA. Movie Recommendation. |
| Subjects: | H Social Sciences > HA Statistics |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | Mr. Marsudiyana - |
| Date Deposited: | 10 Jun 2026 08:40 |
| Last Modified: | 10 Jun 2026 08:40 |
| URI: | http://repository.its.ac.id/id/eprint/133704 |
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