Perbandingan Penerapan Relational Database dan Graph Database dalam Sistem Rekomendasi Film
DOI:
https://doi.org/10.61769/telematika.v18i2.608Keywords:
comparison database, recommendation system, graph database, Neo4j, relational database, PostgreSQL, content based filtering, collaborative filtering, hybrid filteringAbstract
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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