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 K-Means 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.