Development and Design of an Insight Dashboard Using the Laravel Framework
DOI:
https://doi.org/10.59141/jiss.v7i7.2441Keywords:
dashboard insight, lpm itenas, laravel, data visualization, crisp-dm, Academic Information SystemsAbstract
The increasing demand for data-driven governance in higher education requires institutions to develop effective systems for managing, analyzing, and presenting academic information to support strategic decision-making. Conventional manual data management processes often create challenges related to data accuracy, accessibility, reporting efficiency, and institutional quality assurance. This research aims to develop an Insight Dashboard using the Laravel framework to support academic performance monitoring and improve data-based decision-making at the Quality Assurance Institute (LPM) of ITENAS Bandung. The research employed the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. The developed system integrates academic data processing and interactive visualization features to display key performance indicators, including research productivity trends, publication outputs, citation performance, indexed journal distribution, and academic unit comparisons. The results indicate that the dashboard successfully transforms institutional academic data into meaningful insights through dynamic filtering, automated calculations, and visualization components. The system presents important performance indicators, such as total publications, citation impact, publication quality levels, and research output trends, enabling stakeholders to conduct more accurate evaluations and formulate evidence-based strategies. In conclusion, the Insight Dashboard provides an effective solution for strengthening higher education quality management by facilitating continuous monitoring, improving information accessibility, and supporting sustainable data-driven institutional development. Future research may expand the system by incorporating predictive analytics and additional academic performance indicators.
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