Implementasi Business Intelligence untuk Prediksi Produksi Perikanan Budidaya Berbasis Web Dashboard Visualisasi - Vistiyawati, Vanessa; Budy Santoso, Cahyono (Institut Teknologi Garut, 2025)
Aquaculture plays an essential role in supporting food security and meeting the protein needs of the population, particularly in urban areas such as Jakarta. However, data management in aquaculture production is often still performed manually, making analysis and prediction difficult. This study aims to design a web-based visualization dashboard integrated with Business Intelligence implementation to predict aquaculture production in the Jakarta region. The research employs the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, which consists of six main stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Aquaculture production data were processed through cleaning and integration stages, followed by the application of predictive models using Random Forest and Linear Regression algorithms, with Python as the data processing tool. The prediction and analysis results are visualized in an interactive web-based dashboard for easy access and interpretation. Evaluation results indicate that the predictive models used were able to provide an overview of production trends with a satisfactory level of accuracy. The contribution of this research lies in the integration of predictive methods with interactive web-based visualization, which has rarely been applied in the context of urban aquaculture, offering a new approach to supporting strategic decision-making. Through this dashboard, stakeholders can obtain more comprehensive information to enhance strategic decisions in aquaculture management in Jakarta.