ALANA, REYHAN (2023) IMPLEMENTASI ALGORITMA CONTENT BASED FILTERING DALAM SISTEM REKOMENDASI KOMIK. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Saat ini, minat pembaca komik di Indonesia semakin meningkat dan menjadi budaya yang populer. Dengan banyaknya komik yang dirilis tiap tahunnya, membuat calon pembaca kesulitan dalam menemukan komik yang sesuai dengan kriterianya, maka dari itu sistem rekomendasi menjadi fitur yang cukup penting dan memiliki peran dalam membantu calon pembaca. Data yang digunakan yaitu data komik yang berjumlah 1219 komik, dengan rincian 471 komik fisik yang diterbitkan oleh penerbit Elex Media Komputindo dan 748 komik digital yang dirilis di platform Line Webtoon Indonesia. Penelitian ini menggunakan algoritma Content Based Filtering karena hanya memanfaatkan data judul dan sinopsis dari komik tersebut. Metode Cosine Similarity digunakan untuk menghitung nilai kemiripan dari suatu data komik dengan kriteria yang telah dimasukkan dan dapat menguji data sebanyak 10 kali hingga sistem berhasil memberikan rekomendasi komik yang sesuai dengan rata-rata nilai precision sebesar 94.86%. Kata Kunci : Komik, Sistem Rekomendasi, CBF, TF-IDF, Cosine Similarity Currently, the interest of comic readers in Indonesia is increasing and has become a popular culture. With so many comics released each year, it makes it difficult for the readers to find comics that fulfill their criteria, therefore the recommendation system becomes a feature that is quite important and has a role in helping the readers. The data used is comic data totaling 1219 comics, with details of 471 physical comics published by Elex Media Komputindo publishers and 748 digital comics released on the Line Webtoon Indonesia platform. This research uses the Content Based Filtering algorithm because it only utilizes title and synopsis data from the comic. The Cosine Similarity method is used to calculate the similarity value of a comic data with the criteria that has been entered and can test the data 10 times until the system successfully gives suitable comic recommendations with an average precision score of 94.86%. Keywords: Comics, Recommendation System, CBF, TF-IDF, Cosine Similarity
| Item Type: | Thesis (S1) |
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| Call Number CD: | FIK/INFO 23 034 |
| NIM/NIDN Creators: | 41519210048 |
| Uncontrolled Keywords: | Komik, Sistem Rekomendasi, CBF, TF-IDF, Cosine Similarity |
| 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 |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 27 Sep 2023 04:37 |
| Last Modified: | 27 Sep 2023 04:37 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/81525 |
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