Comparative Study of Search Engine Optimization Algorithms On Retrieval Time and Precise Data Entry for Websites

Abstract

A search engine is a complex software that a finder visits to numerous websites and their pages to find important data. It is a main source to find content on the World Wide Web. Search Engine Optimization (SEO) methods have been invented to make user searching smoother. Although search engines are smart, sometimes they also provide irrelevant data. As a result, researchers have found a solution to this problem by implementing the SEO techniques. SEO techniques are used to make websites more visible as well as to produce organic search results. Thus, this study proposed an implementation of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) for SEO problems on online shopping websites and educational websites. These algorithms are evaluated based on retrieval time and precise data entry by using precision and recall. Moreover, PageSpeed Insights is used to check the speed index of the websites. The outcome of the research found that PSO outperformed ANN for both shopping website and educational website. PSO have the minimal retrieval time that is 0.04 seconds for online shopping website and 0.10 seconds for educational website. As for precision and recall, online shopping websites have been proven to have the highest precision score of 0.67 and 0.63 recall score. The simulation analysis results show that in future researchers should concentrate more on determining the significance of each SEO approach and determining the best blend for various sectors.