Perbandingan Penerapan Relational Database dan Graph Database dalam Sistem Rekomendasi Film

Authors

  • Jennifer Florentina Program Studi Informatika, Institut Teknologi Harapan Bangsa
  • Hans Christian Kurniawan Program Studi Informatika, Institut Teknologi Harapan Bangsa

DOI:

https://doi.org/10.61769/telematika.v18i2.608

Keywords:

comparison database, recommendation system, graph database, Neo4j, relational database, PostgreSQL, content based filtering, collaborative filtering, hybrid filtering, perbandingan database, sistem rekomendasi, pemfilteran berbasis konten, pemfilteran kolaboratif, pemfilteran hibrida

Abstract

Sistem rekomendasi dipakai di berbagai aplikasi, seperti e-commerce, media sosial, dan yang lainnya dalam membangun sistem rekomendasi yang membutuhkan database sebagai penyimpanan data. Pentingnya pemilihan database terhadap performa sistem membuat penelitian akan penerapan berbagai jenis database pada sistem rekomendasi meningkat termasuk dalam penelitian ini. Penelitian ini melakukan perbandingan latensi dan penggunaan memori antara relational database dan graph database pada sistem rekomendasi film. Indikator utama dalam penelitian ini adalah nilai threshold untuk batas nilai similarity serta teknik sistem rekomendasi yang digunakan. Terdapat 3 teknik yang digunakan yaitu teknik content-based filtering memakai Jaccard similarity, collaborative filtering memakai cosine similarity, dan hybrid filtering yang merupakan gabungan dari content-based filtering dan collaborative filtering. Database yang digunakan adalah PostgreSQL untuk relational database dan Neo4j untuk graph database. Berdasarkan pengujian di berbagai nilai threshold, didapatkan nilai latensi dan penggunaan memori pada kedua database yang dibandingkan. Pada teknik content-based filtering, PostgreSQL memiliki latensi waktu 120-150 detik dan penggunaan memori 119 - 120 MB, sedangkan Neo4j 6-7 detik dan 41-43 MB. Pada teknik collaborative filtering, PostgreSQL memiliki latensi waktu 3-4 detik dan penggunaan memori 119-120 MB, sedangkan Neo4j 4-5 detik dan 24-26 MB. Pada teknik hybrid filtering, PostgreSQL memiliki latensi waktu 125-150 detik dan penggunaan memori 119-120 MB, sedangkan Neo4j 9-11 detik dan 32-34 MB.

 

Recommendation systems are used in various applications, such as e-commerce, social media, and others in building recommendation systems that require databases as data storage. The importance of database selection on system performance has increased research on the application of various types of databases in recommendation systems, including this research. This research compares latency and memory usage between relational databases and graph databases in movie recommendation systems. The main indicators in this research are the threshold value for the similarity value limit and the recommendation system technique used. There are 3 techniques used, namely content-based filtering using Jaccard similarity, collaborative filtering using cosine similarity, and hybrid filtering which is a combination of content-based filtering and collaborative filtering. The database used is PostgreSQL for relational databases and Neo4j for graph databases. Based on testing at various threshold values, the latency and memory usage values of the two databases are compared. In the content-based filtering technique, PostgreSQL has a latency time of 120-150 seconds and memory usage of 119-120 MB, while Neo4j is 6-7 seconds and 41-43 MB. In the collaborative filtering technique, PostgreSQL has a latency time of 3-4 seconds and memory usage of 119 - 120 MB, while Neo4j is 4-5 seconds and 24 - 26 MB. In the hybrid filtering technique, PostgreSQL has a latency time of 3-4 seconds and a memory usage of 24 - 26 MB. In the hybrid filtering technique, PostgreSQL has a latency time of 125-150 seconds and memory usage of 119-120 MB, while Neo4j has 9-11 seconds and 32-34 MB.

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Published

2024-02-27

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Section

Articles