Abstract. An interactive system could be provided for batik customers with the aim of helping them in selecting the right batiks. The system should manage a collection of batik images along with other information such as fashion color type, the contrast degree, and motif. This research aims to find methods of clustering and classifying batik images based on fashion color, contrast and motif. A color clustering algorithm using HSV color system is proposed. Two algorithms for contrast clustering, both utilize wavelet, are proposed. Six algorithms for clustering and classifying batik images based on group of motifs, employing shape-and texture-based techniques, are explored and proposed. Wavelet is used in image pre-processing, Canny detector is used to detect image edges. Experiments are conducted to evaluate the performance of the algorithms. The result of experiments shows that fashion color and contrast clustering algorithms perform quite well. Three of motif based clustering and classification algorithms perform fairly well, further work is needed to increase the accuracy and to refine the classification into detailed motif.

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In response to education regulations for quality assurance (QA), universities in Indonesia strongly require an integrated management information system (MIS), such as Academic MIS (AMIS). In developing AMIS, the main issues that must be addressed are the urgent need for implementing university QA standards (even though departments have already implemented their best practices for years and show reluctance to change), changing requirements, and the need for a quick delivery system despite the fact that AMIS is very large in scope. This paper contributes to modeling AMIS, which is suitable for universities in Indonesia. This research has been conducted at one of the best private universities, Parahyangan Catholic University. Having measured the quality of AMIS using several key business measures, results indicated that the proposed model successfully resolved the issues at stake.