Analisis Sistem Rekomendasi Berbasis Machine Learning: Tinjauan Literatur Sistematis

Afifah Khaerani Aziz; Faza Alameka

doi:10.56861/cognitech.v1i1.12

Authors

Afifah Khaerani Aziz Universitas Salakanagara
Faza Alameka Universitas Mulia

DOI:

https://doi.org/10.56861/cognitech.v1i1.12

Keywords:

Recommendation systems, machine learning, deep learning, personalization, model evaluation., Sistem rekomendasi, machine learning, deep learning, personalisasi, evaluasi model.

Abstract

This study is a systematic literature review that discusses the development of recommendation systems based on machine learning (ML) and deep learning (DL) in various domains, such as e-commerce, education, entertainment, and online content services. Recommendation systems play a crucial role in improving accuracy, relevance, and personalization by utilizing intelligent algorithms to analyze user preferences. Through an in-depth review of ten recent studies, this study identifies a variety of algorithmic approaches including collaborative filtering, content-based filtering, and hybrid methods. In addition, key challenges such as cold start issues, data scarcity, scalability, and user privacy are also reviewed. The findings show that deep learning is able to capture complex patterns and significantly improve recommendation performance. Evaluation of the recommendation system using metrics such as RMSE, MAE, F1-score, and R-squared is essential to ensure the effectiveness and sustainability of the model. This research emphasizes the importance of selecting algorithms that are appropriate to the characteristics of the domain and the needs of the user. The implications of this study are expected to be a guide for the development of ML and DL-based recommendation systems that are more adaptive, accurate, and user-centric in the future.

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