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Prilia Rahmayanti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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ResNet50-Based Deep Learning Architecture with Focal Loss Optimization for Automated Fruit Ripeness Classification Stefani Hardiyanti Putri; Nasrullah; Fefi Maulana; Prilia Rahmayanti; Efmi Maiyana
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2449

Abstract

This study develops an Enhanced ResNet50 architecture with Focal Loss optimization for automated fruit ripeness classification. The research implements systematic modifications to the standard ResNet50 framework, incorporating attention mechanisms, strategic transfer learning with 20 trainable layers, and advanced class imbalance handling through Focal Loss function (α=[0.809, 1.904, 0.807], γ=2.0). The model processes RGB images (224×224×3) across three ripeness categories: Overripe, Ripe, and Unripe, utilizing the Kaggle Fruits Ripeness Classification Dataset containing 4,434 high-quality images. The Enhanced ResNet50 architecture achieves 97.22% classification accuracy with corresponding precision, recall, and F1-scores of 0.9722, demonstrating superior performance compared to standard ResNet50 (91.7%), VGG16 (89.2%), and EfficientNet-B0 (88.5%). The model exhibits efficient computational characteristics with 50-100ms inference time and 104.55 MB model size, while successfully addressing mild class imbalance (ratio 0.424) through systematic optimization techniques.
Co-Authors Efmi Maiyana Fefi Maulana Nasrullah Stefani Hardiyanti Putri