PENERAPAN SISTEM INTELIJENSIA BISNIS DAN K-MEANS CLUSTERING UNTUK MEMANTAU PRODUKSI TANAMAN OBAT

Abstract

Indonesia has biodiversity including medicinal plants. The medicinal plant business can be a profitable
business prospect because it has high export opportunities. Based on the benefits obtained, the production of
medicinal plants needs to be considered by monitoring and evaluating production results to increase productivity,
especially for areas with low production levels. The Department of Food Crops and Horticulture of West Java
Province through the website https://opendata.jabarprov.go.id has provided production datasets for each type of
medicinal plant, but it has not yet become a dataset with various types of medicinal plants. The purpose of this
study was to design an integrated data storage model in the form of a data warehouse, grouping medicinal plant
production areas using data mining and designing data visualization in business intelligence systems. Business
intelligence system design was carried out through several stages, namely system requirements analysis,
identification of data and information needs, data warehouse design, data warehouse filling, data mining
processes, data visualization, and system performance evaluation. The results of the research were the application
of a data warehouse using a dimensional model with a star schema; a grouping of production areas using the KMeans algorithm with optimal k=3 and the number of elements produced in each cluster is 24 regions in cluster
0, 1 region in cluster 1, and 2 regions in cluster 2; a business intelligence system is implemented using a dashboard
to show information on the amount of production and display the results of grouping potential areas for producing
medicinal plants.
Keywords: business intelligence, data warehouse, dimensional models, visualization, medicinal plants