Web Mining for Enhanced Academic Visibility and Engagement Analysis Based on Visitor Data - Yuhefizar Yuhefizar; Roni Putra (Journal of Systems Engineering and Information Technology (JOSEIT), 2024)
With online platforms that transform scholarly communication, academic journals must strategically amplify their digital footprint. This study demonstrates the value of using web analytics and time series modeling to uncover nuanced online readership trends and rhythms. Using the case of the Review of Rekayasa Sistem dan Teknologi Informasi/System Engineering and Information Technology (RESTI) Journal website's 2023 visitor data, we employ visual and ARIMA time series analysis to delineate engagement patterns aligned with academic cycles. The results reveal pronounced seasonal fluctuations, with the participation peaking in October and November, coinciding with increased research dissemination. Fitting an ARIMA model to daily new visitor data indicates positive autocorrelations, suggesting that the engagement effects persist on days. The model provides a predictive baseline for evaluating outreach initiatives. The study offers strategic information on aligning content planning with reading engagement rhythms. At the methodological level, the integration of data mining, predictive modeling, and information retrieval techniques establishes a versatile framework for investigating evolving scholarly communication dynamics in the digital age. The study also emphasizes meticulous data preparation and model diagnostics. The analytical approach presented provides actionable intelligence on trends in the use of academic portals online. This has far-reaching implications for journals seeking to strategically enhance their digital presence amidst increasing competition. With the proliferation of electronic resources, these techniques will only grow in importance for assessing and amplifying the impact of online scholarly platforms.