Knowledge Engineering and Data Science
Vol 1, No 2 (2018)

Digit Classification of Majapahit Relic Inscription using GLCM-SVM

Tri Septianto (Sekolah Tinggi Teknik Surabaya)
Endang Setyati (Sekolah Tinggi Teknik Surabaya)
Joan Santoso (Sekolah Tinggi Teknik Surabaya)

Article Info

Publish Date
23 Aug 2018


A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition. Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.

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Journal Info





Computer Science & IT Engineering


Knowledge Engineering and Data Science (2597-4637), KEDS, brings together researchers, industry practitioners, and potential users, to promote collaborations, exchange ideas and practices, discuss new opportunities, and investigate analytics frameworks on data-driven and knowledge base systems. ...