Optimizing Book Genre Classification through AI on a Web Platform

In the rapidly evolving digital era, the exponential growth of online book collections poses challenges in efficiently classifying literature according to genre. Manual classification methods are often time-consuming, subjective, and inconsistent, necessitating the adoption of advanced, automated approaches. This study aims to develop and implement an Artificial Intelligence (AI)-based genre classification system integrated into a web platform to enhance the accuracy, efficiency, and user experience in book discovery. Leveraging Machine Learning (ML) algorithms—particularly Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, and Deep Learning—alongside Natural Language Processing (NLP) techniques such as tokenization, stemming, and TF-IDF, the system analyzes book descriptions and synopses to determine the most appropriate genre. The research follows a qualitative and literature study approach, utilizing a dataset sourced from Kaggle, with preprocessing steps to remove noise and convert text into numerical representations. Experimental results demonstrate that the SVM model achieved the highest accuracy, precision, recall, and F1-score compared to other tested algorithms, effectively handling high-dimensional and non-linear data. The developed web application features an interactive dashboard, real-time classification, and a hybrid recommendation system. This work confirms the feasibility and advantages of AI-driven genre classification for large-scale digital libraries and online bookstores. While limitations such as data imbalance and overlapping genre semantics remain, the findings provide a strong foundation for future research employing larger, more diverse datasets and advanced deep learning architectures to further improve classification performance.