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All Journal Proceedings of KNASTIK Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah FIFO JUTEI (Jurnal Terapan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] JOINCS (Journal of Informatics, Network, and Computer Science) Journal of Software Engineering and Multimedia (JASMED)
Sri Suwarno
Universitas Kristen Duta Wacana
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : JUTEI (Jurnal Terapan Teknologi Informasi)

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Implementasi Transfer Learning Pada AlexNet Untuk Klasifikasi Motif Batik Yogyakarta Cahyaningtyas, Angela Gracia; Suwarno, Sri; Susanto, Budi
Jurnal Terapan Teknologi Informasi Vol 9 No 2 (2025): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2025.92.432

Abstract

This study discusses the implementation of Transfer Learning using the AlexNet architecture for classifying Yogyakarta Batik motifs, specifically Kawung, Parang, and Truntum. The dataset consists of 1,110 batik images that underwent preprocessing, augmentation, and data splitting. The research was conducted in two main experimental scenarios: applying Average Pooling and Max Pooling with a more complex classifier, and training the model without additional pooling layers using a simpler classifier. Furthermore, the experiments compared the model’s performance across different frozen layers and two optimizers (Adam and SGD). The results show that the best configuration was obtained using the SGD optimizer with Average Pooling and three frozen layers, achieving a test accuracy of 99.10%. In contrast, the Adam optimizer tended to produce lower and less stable performance. Experiments without pooling also reached high accuracy, but were less optimal than those with pooling. Therefore, this study demonstrates that the choice of pooling technique, classifier complexity, frozen layers, and optimizer plays a crucial role in achieving optimal performance of AlexNet for Batik classification.
Co-Authors Aditya Wikan Mahastama Budi Susanto Cahyaningtyas, Angela Gracia Ginantaka, Gregorius Sakti Hendro Setiadi Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Pasaribu, Paul Ronaldo Yonathan Rosa Delima Sri Hartati Sumolang, Philip Andrew