Elta Sonalitha, Bambang Nurdewanto, Anis Zubair, Salnan Ratih Asriningtias, Kukuh Yudhistiro, Irfan Mujahidin
The classification of artistic expertise in an area on products and actors of art greatly affects the progress of artistic life. One method of classifying cultural data is the taxonomic method. In the taxonomic method, an art product can be categorized into several domains. For example, the product of Kawung (Indonesian) batik cloth can be included in the domains of fashion, philosophy, and fine arts. An example from the taxonomy of art actors, for example, an artist can have various expertise in music, dance, fine arts, or others. The source of information used to classify this research is the big data of art actors in Malang, Indonesia. Big data is obtained from art actors directly who provide input from the instrument about the suitability of the art field with the expertise possessed by each of them. Individual artists generally have more than one artistic skill which can be classified taxonomically and ranked using fuzzy clustering. The purpose of ranking with fuzzy clustering is to determine the weight of artistic skills starting from the level just done to the most proficient to do. To achieve accurate weighing results, a taxonomy application for mapping and data analysis of artists and works of art was created. This research discusses functional testing (black-box testing) of the taxonomy application of mapping and data analysis on web-based artists and artworks. © 2020 IEEE.
University of Merdeka Malang, Electrical Engineering, Malang, Indonesia; Brawijaya University, Department of Vocational Education, Malang, Indonesia; University of Merdeka Malang, Information Technology, Malang, Indonesia