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All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknik Informatika (JUTIF)
Perdana, Am Akbar Mabrur
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Computer Science & IT

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Enhancing Batik Classification Leveraging CNN Models and Transfer Learning Perdana, Am Akbar Mabrur; Fajar B, Muhammad; Mappalotteng, Abdul Muis
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2535

Abstract

Batik is a traditional art originating from Indonesia and recognized by UNESCO. Batik motifs vary depending on the region of origin. The diverse batik motifs reflect the rich cultural heritage and unique traditions owned by each region in Indonesia. From Sabang to Merauke, each motif features a different story and values, depicting the beauty and diversity of nature and the lives of diverse local people. However, in the context of the modern era that continues to develop, batik motifs also experience renewal and creativity that always adapts to the times. As a result, the diversity of batik motifs is increasingly abundant in Indonesia. Thus, complicating efforts to identify and categorize batik motifs appropriately. Therefore, in the context of this study, we chose to combine the MobileNetV2 model with Transfer Learning to classify batik motifs. We used a dataset consisting of 3000 batik images and categorized them into three main classes, namely Kawung batik, Mega Mendung batik, and Parang batik. This approach not only leads to the introduction and understanding of traditional batik motifs but also applies the latest technology for a more in-depth and accurate analysis. The results of this model show an extremely high level of testing accuracy, reaching 0.9946%, and training accuracy of 0.8916%, and the time required by the model to train and test the entire dataset is 18 minutes 1 second. Future research can explore the integration of other technologies or new approaches to improve accuracy and efficiency in classifying batik motifs.
CLASSIFICATION OF RICE QUALITY LEVELS BASED ON COLOR AND SHAPE FEATURES USING ARTIFICIAL NEURAL NETWORK BASED ON DIGITAL IMAGE PROCESSING Asnidar, Asnidar; Perdana, Am Akbar Mabrur; Ilham, Muhammad Ryan; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.734

Abstract

Rice is the staple food of most Indonesians. In identifying the quality of rice, it can be seen from physical characteristics such as the color and shape of rice, because these characteristics can make an object can be identified properly and clearly. In general, what is done in determining the quality of rice by looking at its color and shape. But usually the human eye in identifying objects is sometimes less accurate which is influenced by several factors, such as age. So, several studies were conducted that tried to solve the problem by using digital image processing. However, the accuracy results obtained are still not accurate, because the datasets used in the previous study were relatively small, namely around 80 images, although the average level of accuracy obtained was quite high, but the number of datasets used was very small so that the level of accuracy was still inaccurate. Therefore, in this study, it is proposed that the title of classification of rice quality levels using JST based on digital image processing which divides rice into 3 classifications, namely, good, good enough, and not good where in this study using 330 digital images to produce a more accurate level of accuracy. In this study, there are several stages, namely, image retrieval, preprocessing, segmentation, morphological, feature extraction, and classification using artificial neural networks. Based on the research conducted, training accuracy was produced with an average accuracy of 98,75% while the test accuracy was produced with an average accuracy of 98,89%.
Co-Authors Abdul Muis Mappalotteng Andi Baso Kaswar Andi Baso Kaswar Asnidar Asnidar Ilham, Muhammad Ryan Muhammad Fajar B