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All Journal JSAI (Journal Scientific and Applied Informatics) Idealis : Indonesia Journal Information System Jurnal Algoritma Journal of Elektronik Sistem InformasI
Budy Santoso, Cahyono
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Journal : Jurnal Algoritma

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Implementasi Business Intelligence untuk Prediksi Produksi Perikanan Budidaya Berbasis Web Dashboard Visualisasi Vistiyawati, Vanessa; Budy Santoso, Cahyono
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2902

Abstract

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.
Pengembangan Dashboard Prediksi Penggunaan Transportasi Umum Berbasis Business Intelligence dan Random Forest di Jakarta Ahmad, Alifio Fikra Ahmad; Budy Santoso, Cahyono
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2941

Abstract

This study applies the concept of Business Intelligence (BI) to predict and visualize trends in public transportation usage in Jakarta to support data-driven decision making. Secondary data from the Satu Data Jakarta portal was analyzed using the Random Forest algorithm due to its ability to process complex variables with accurate prediction results (R² = 0.978). The results show that TransJakarta, MRT, and KRL have stable passenger trends, while LRT, KCI Commuter Bandara, ships, and school buses are more volatile. These results are visualized in a web-based dashboard that facilitates fleet planning and public transportation operational policies. This research contributes to the application of BI in the transportation sector by presenting a prediction model that supports data-driven policy formulation.
Co-Authors Ahmad, Alifio Fikra Ahmad Asmaidin, Asmaidin Bhagawanta, Bajra Faizul Anwar Ramdhani Ghaniy, Rajib Hafid Roihan Harry Vadilan Sianturi Muhammad Mujiburochman Rachma, Mutia Raihan Cikal Herlambang Reyner Shaquille Rachim Vistiyawati, Vanessa