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All Journal Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Buguh: Jurnal Pengabdian Kepada Masyarakat
Taufiqotul Bariyah
Universitas Internasional Semen Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Batik pattern recognition using convolutional neural network Mohammad Arif Rasyidi; Taufiqotul Bariyah
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (739.961 KB) | DOI: 10.11591/eei.v9i4.2385

Abstract

Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.
Classification of handwritten Javanese script using random forest algorithm Mohammad Arif Rasyidi; Taufiqotul Bariyah; Yohanes Indra Riskajaya; Ayunda Dwita Septyani
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3036

Abstract

The ability to read and write Javanese scripts is one of the most important competencies for students to have in order to preserve the Javanese language as one of the Indonesian cultures. In this study, we developed a predictive model for 20 Javanese characters using the random forest algorithm as the basis for developing Javanese script learning media for students. In building the model, we used an extensive handwritten image dataset and experimented with several different preprocessing methods, including image conversion to black-and-white, cropping, resizing, thinning, and feature extraction using histogram of oriented gradients. From the experiment, it can be seen that the resulting random forest model is able to classify Javanese characters very accurately with accuracy, precision, and recall of 97.7%.
Co-Authors Ayunda Dwita Septyani Bahri Rizaldi Catur Wulandari Doni Setio Pambudi Hanesti, Elsi Mersilia Ludito Woro Indrio Mohammad Arif Rasyidi Ngatini Yohanes Indra Riskajaya