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All Journal Jurnal Teknologi Telematika : Jurnal Informatika dan Teknologi Informasi OPSI International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Artificial Intelligence and Robotics (IJAIR)
Heru Cahya Rustamaji
Jurusan Teknik Informatika, Fakultas Teknik Industri, UPN “Veteran” Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Non-destructive classification of sugarcane milling feasibility using deep learning: A comparative study of VGG19 and ResNet50 Indrianti, Nur; Leuveano, Raden Achmad Chairdino; Rustamaji, Heru Cahya; Ferriyan, Andrey; Mulyono, Panut; Wijaya, Bayu Prasetya
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).
Co-Authors Adi Prasetyo Ananta, Moh. Ais Andrey Ferriyan, Andrey Annesa Maya Sabarina Aprilia Saptu Ningrum Aprilianta, Kevin Aulia Pasca Sahida Awang Hendrianto Pratomo Bambang Yuwono Bambang Yuwono Bambang Yuwono Dessyanto Boedi Prasetyo Didi Setya dito ahmad Eko Budi Hartono Fani Widiastuti Ferriyan , Andrey Granitia Septirina Junaedi Hafsah Hafsah Hasna Nur Hanani Herlina Jayadianti Herry Sofyan Herry Sofyan Hidayatulah Himawan Laninda Laninda Leuveano, Raden Achmad Chairdino Mariani Mariani Muhammad Iqbal Mulyono , Panut Niken Oktaviana Nur Heri Cahyana Nur Indrianti Panut Mulyono Prapcoyo, Hari Rifki Indra Perwira Rifki Indra Perwira Rusdita Bazzarudin Suryandaru Saifullah, Shoffan Santosa, Budi Syaputra, Hendra Arya Taufiq Akbar, Ahmad Usamah Dani, Usamah Wijaya, Bayu Prasetya Wilis Kaswidjanti Wilis Kaswidjanti Yenni Sri Utami Yuli Fauziah