PEMODELAN PREDIKTIF TRAFIK WEBSITE BERDASARKAN VOLUME KONTEN: PENDEKATAN REGRESI Web performance, content strategy, linear regression model, page view analysis, digital content optimization

Abstract

In today's digital landscape, a website's performance serves as a key metric of an institution’s online presence and communication strategy. This research focuses on forecasting website performance by analyzing the relationship between the number of published articles and the volume of page views using a simple linear regression approach. Monthly data was obtained from the official website of the Faculty of Science and Technology at Universitas Jambi, comprising content publication frequency and corresponding traffic. The analysis reveals a strong positive correlation, where each additional published article contributes to a notable increase in page views. The regression model yields a coefficient of 103.75 with an R² value of 0.7278, indicating that over 72% of traffic variation is attributable to content volume. These results emphasize the importance of consistent content production in enhancing web visibility and provide valuable insights for content strategy development.