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All Journal Buana Information Technology and Computer Sciences (BIT and CS) Prosidia Widya Saintek
Firman Nurdiyansyah
Universitas Widya Gama Malang
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Transportation

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Classification of Tulungagung Batik Images in Comparison of Convolution Neural Network and Vision Transformer Algorithms: Author's Country: Indonesia Augustiar Mahendra Mochammad; Firman Nurdiyansyah; Fitri Marisa
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/84twwe32

Abstract

Batik is a significant Indonesian cultural heritage with a vast diversity of motifs, making manual classification a challenging task. This research provides a comparative analysis of two prominent deep learning architectures, the Convolutional Neural Network (CNN), represented by VGG16, and the Vision Transformer (ViT), represented by DeiT, for the classification of Tulungagung batik images. A balanced dataset of 2,400 images, comprising two classes (Bangoan and Majanan), was utilized. The experiment was conducted using three distinct training-to-testing split ratios (80:20, 70:30, and 60:40) to evaluate model robustness. Performance was assessed using accuracy, precision, recall, F1-score, and the confusion matrix. The results indicate that the CNN (VGG16) model consistently outperformed the ViT (DeiT), achieving its peak accuracy of 96% on both the 80:20 and 60:40 split ratios, showcasing high stability. The ViT (DeiT) model was more sensitive to the data split, reaching a peak accuracy of 94% with less consistent performance. We conclude that for this specific classification task, the VGG16 architecture is more robust, stable, and effective than the DeiT architecture.
VISUAL ANALYTICS PERFORMA PEMAIN SEPAK BOLA BERDASARKAN POSISI MENGGUNAKAN RADAR CHART Devita Putri Anggraini; Ismail Akbar; Affi Nizar Suksmawati; Firman Nurdiyansyah
Prosidia Widya Saintek Vol. 5 No. 2 (2026)
Publisher : Universitas Widyagama Malang

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Abstract

Perkembangan sport analytics mendorong pemanfaatan data statistik pemain sebagai dasar pengambilan keputusan dalam sepak bola. Namun, penyajian data dalam bentuk tabel atau angka masih menyulitkan pengguna dalam mengeksplorasi dan membandingkan karakteristik performa pemain berdasarkan posisi bermain. Penelitian ini bertujuan mengembangkan dashboard visual analytics menggunakan Tableau dengan Radar Chart sebagai visualisasi utama untuk mendukung analisis performa pemain sepak bola. Penelitian menerapkan pendekatan User-Centered Design (UCD) yang meliputi pengumpulan dataset, identifikasi kebutuhan pengguna, persiapan data, perancangan dashboard, dan evaluasi pengguna. Dataset yang digunakan adalah FIFA 22 Complete Player Dataset yang diperoleh dari Kaggle dan memanfaatkan atribut Overall, Pace, Shooting, Passing, Dribbling, Defending, dan Physic. Dashboard yang dihasilkan mengintegrasikan Key Performance Indicator (KPI), Radar Chart, Bar Chart, Scatter Plot, Heatmap, serta filter interaktif sehingga pengguna dapat mengeksplorasi data secara lebih informatif dan interaktif. Hasil evaluasi terhadap 30 responden menunjukkan bahwa dashboard memiliki tingkat penerimaan yang sangat baik serta mampu membantu pengguna dalam membandingkan karakteristik performa pemain dan mendukung proses analisis maupun pengambilan keputusan secara lebih efektif.
Co-Authors Affi Nizar Suksmawati Augustiar Mahendra Mochammad Devita Putri Anggraini Fitri Marisa Ismail Akbar