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All Journal Jurnal Komputer dan Teknologi (JUKOMTEK)
Wildan Jazuli
Universitas Mitra Bangsa
Computer Science & IT Library & Information Science

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OPTIMASI KINERJA DATABASE MENGGUNAKAN TEKNIK INDEXING DAN CACHING Wildan Jazuli; Raka Hikmah Ramadhan; Robbi Kharisma Ar Rasyid
Jurnal Komputer dan Teknologi Vol 3 No 2 (2024): JUKOMTEK JULI 2024
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v3i2.683

Abstract

The database is a critical component of an information system, serving as the medium for data storage and management. As the volume of stored data increases, database performance may degrade due to longer response times when executing queries. Therefore, optimization techniques such as indexing and caching are required to improve data access efficiency. This article discusses the concepts, implementation, and benefits of indexing and caching techniques in optimizing database performance. The method used in this study is a quantitative experimental approach, which involves measuring query execution times before and after applying indexing and caching on a relational database system using MySQL and Redis.
PENINGKATAN AKURASI SISTEM REKOMENDASI PRODUK MENGGUNAKAN ALGORITMA ANT COLONY OPTIMIZATION Zaenuddin; Wildan Jazuli; Devi Wulandari; Rini Fath Marsya
Jurnal Komputer dan Teknologi Vol 3 No 2 (2024): JUKOMTEK JULI 2024
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v3i2.684

Abstract

Abstract: Product recommendation systems play a crucial role in helping users discover productsthat align with their preferences, particularly on e-commerce platforms. However, the mainchallenge lies in improving recommendation accuracy to ensure that the suggested items are trulyrelevant. This study proposes the application of the Ant Colony Optimization (ACO) algorithmto enhance the accuracy of recommendation systems. ACO is a metaheuristic algorithm inspiredby the behavior of ants in finding the shortest path to a food source, which is adapted here tosearch for optimal product combinations based on users’ interaction history. Experimental resultsshow that integrating ACO with a collaborative filtering-based approach improvesrecommendation accuracy by up to 34% compared to conventional methods. These findingscontribute to the development of more intelligent and adaptive recommendation systems.
Co-Authors Devi Wulandari Raka Hikmah Ramadhan Rini Fath Marsya Robbi Kharisma Ar Rasyid Zaenuddin