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All Journal International Journal of Advances in Intelligent Informatics JISKa (Jurnal Informatika Sunan Kalijaga)
Andrey Ferriyan
Universitas Pembangunan Nasional Veteran Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Non-destructive classification of sugarcane milling feasibility using deep learning: A comparative study of VGG19 and ResNet50 Nur Indrianti; Raden Achmad Chairdino Leuveano; Heru Cahya Rustamaji; Andrey Ferriyan; Panut Mulyono; Bayu Prasetya Wijaya
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2236

Abstract

Assessing sugarcane quality is crucial for ensuring both economic value and processing efficiency in sugar production. Conventional approaches, such as refractometer-based Brix measurements, are destructive, labor-intensive, and unsuitable for large-scale or rapid field evaluations. This study proposes a non-destructive deep learning framework for classifying sugarcane internodes into two quality categories (< 16 °Bx and ≥16 °Bx) to address existing limitations. Two convolutional neural network architectures, VGG19 and ResNet50, were evaluated utilizing a defined transfer learning and data augmentation methodology. Because of its residual connections, which enable deeper and more stable feature learning, ResNet50 consistently outperformed VGG19, achieving the highest accuracy of 78.85% on the Luar2_Putih dataset. This comparative finding demonstrates that modern residual-based networks provide superior robustness for subtle visual classification tasks in agricultural imaging, while also validating the stability of the proposed two-phase training framework. The study advances AI-driven non-destructive quality assessment by offering a scalable, field-deployable solution that supports sustainable, efficient sugarcane processing in line with the UN Sustainable Development Goals (SDG 2, 9, 12, and 13).
Deteksi Anomali Jaringan Transaksi Token ERC20 di Blockchain Ethereum Menggunakan Graph Neural Network Nazhif Alaudin Fahmi; Andrey Ferriyan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 3 (2026): September 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6065

Abstract

The rapid development of Decentralized Finance (DeFi) on the Ethereum blockchain has increased ERC20 token transaction volumes but also triggered a surge in anomalous activities such as Ice Phishing, NFT Scams, and Reward Scams. Conventional detection methods on tabular data often fail to capture complex relational patterns between accounts. This study proposes a Graph Neural Network (GNN) approach using the GraphSAGE architecture to detect anomalies in ERC20 transaction networks. Transaction datasets were acquired from Etherscan, processed into graphs with additional structural features (PageRank, degree), and imbalance was handled using class weighting. Based on testing using 33,848 transactions, the model yielded an average accuracy of 95.92% and a macro F1-score of 82.62%. These results prove that GraphSAGE is effective in learning feature representations and graph structures to detect various ERC20 fraud modes simultaneously.
Co-Authors Bayu Prasetya Wijaya Heru Cahya Rustamaji Nazhif Alaudin Fahmi Nur Indrianti Panut Mulyono Raden Achmad Chairdino Leuveano