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Gamelan is an ensemble music originated from Indonesia. Gamelan have been made manually but had 7 tonnes for Pelog scale or 5 tonnes for Slendro scale. It is need to classify gamelan tones for digital musical processing for various objectives like learning, teaching, or composing. Fractal analysis can be used to classify physical phenomena like musical tones signal. One of fractal method was Higuchi that is suitable in computing fractal dimension for time series data as musical signals. In this paper, we propose the Higuchi fractal dimension to distinguish gamelan signal tones. Our experiment show that Demung Pelog tones can be classified properly until 82.85%. This result encourages to classify gamelan tones not only for other instruments but also for Slendro scale.

Plant recognition based on digital leaf image has received as particular attention in computer vision and intelligence system, due its important implication in automatic plant identification. Plant species have the unique leaf characteristics such as the shape, texture, margin, and colour, which different each other. This study presents a novel method for automation plant recognition using Generalized Relevance Learning Vector Quantization (GRLVQ). GRLVQ is a competitive based learning algorithm which is integrating features extraction and classification phases. The experimental result shows that GRLVQ has better performance than the predecessor algorithm.