MOVIE RECOMMENDER SYSTEM USING DECISION TREE METHOD
MUHAMMAD BILAL RAFIF AZAKI
Informasi Umum
Kode
23.04.2498
Klasifikasi
006.31 - Machine Learning
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Machine - Learning, Using Mis,
Dilihat
371 kali
Informasi Lainnya
Abstraksi
<p>In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the grouplens.org website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.</p>
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Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | MUHAMMAD BILAL RAFIF AZAKI |
| Jenis | Perorangan |
| Penyunting | Z K Abdurahman Baizal |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2023 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
