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All Journal Building of Informatics, Technology and Science Academia Open
Sindhu Rakasiwi
Universitas Dian Nuswantoro, Semarang
Computer Science & IT Medicine & Pharmacology Public Health

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Optimization of Transfer Learning VGG-16 and ResNet50 for Deep Learning-Based Classification of Edible and Poisonous Mushroom Images: Optimalisasi Transfer Learning VGG-16 dan ResNet50 untuk Klasifikasi Citra Jamur Edible dan Poisonous Berbasis Deep Learning Almira Zuhrotus Safira; Sindhu Rakasiwi
Academia Open Vol. 11 No. 1 (2026): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.11.2026.13078

Abstract

General Background Accurate identification of edible and poisonous mushrooms is critical for food safety because high visual similarity among species often causes misclassification. Specific Background Deep learning with transfer learning using Convolutional Neural Networks has been widely applied for image-based mushroom classification, particularly through pretrained architectures such as VGG-16 and ResNet50. Knowledge Gap Nevertheless, limited comparative evidence exists regarding which architecture provides more stable and balanced performance when applied to relatively small and diverse mushroom image datasets. Aims This study compares VGG-16 and ResNet50 transfer learning models for binary mushroom toxicity classification using the Kaggle Edible and Poisonous Mushroom Images dataset consisting of 2,820 images from 47 species. Results Using a 70:15:15 training, validation, and testing split with standardized preprocessing and data augmentation, the fine-tuned VGG-16 model achieved 96% test accuracy with a loss of 0.1671, while the ResNet50 model reached 92% accuracy with a loss of 0.2991. Both models obtained a ROC AUC value of 1.000, although VGG-16 demonstrated more balanced precision, recall, and F1-scores across classes. Novelty This research presents a direct and systematic comparison of two widely used pretrained CNN architectures under identical experimental settings. Implications The findings support automated mushroom toxicity identification to assist safer mushroom consumption decisions. Highlights: The strongest model achieved 96% accuracy with lower classification loss. The alternative model produced lower accuracy under identical conditions. Both approaches reached perfect ROC AUC with differing class balance. Keywords: Deep Learning, Convolutional Neural Network (CNN), VGG-16, ReNet50, Mushroom Image Classification
Analisis Komparatif Kinerja Algoritma Support Vector Machine, Random Forest, dan Naive Bayes untuk Klasifikasi Sentimen pada Komentar YouTube Eustachius Dito Dewantoro; Sindhu Rakasiwi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8959

Abstract

The rise of social media platforms like YouTube has made them a primary medium for public discourse on socio-political issues, such as the "August 25th protests," which triggered massive polarization in the digital space. The vast volume of comments necessitates a computational approach for sentiment analysis. This study aims to classify public sentiment into positive and negative categories while comparing the performance of Naive Bayes, Random Forest, and Support Vector Machine (SVM). These algorithms were selected for their computational efficiency on high-dimensional text data compared to Deep Learning models. The methodology involved collecting 2,917 comments via the YouTube Data API v3, followed by text preprocessing, lexicon-based automated labeling, and TF-IDF feature weighting. To address the dataset's imbalance, where negative sentiment dominated at 78.8%, stratified sampling was applied to maintain class proportions. Results indicate that SVM achieved the highest accuracy at 88.2%, outperforming Random Forest (83.1%) and Naive Bayes (81.2%). SVM's superiority stems from its ability to find an optimal hyperplane that maximizes class margins, ensuring stability in imbalanced datasets. This research contributes a robust classification framework for understanding public opinion dynamics on specific political issues in Indonesia.
Evaluasi Strategi Fine-Tuning pada ConvNeXt dan Swin Transformer untuk Klasifikasi Kanker Kulit Ahmad Bintang Saputra; Sindhu Rakasiwi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9015

Abstract

Skin cancer is one of the diseases whose prevalence continues to increase every year, especially in areas with high exposure to ultraviolet (UV) rays. The main challenge in diagnosing skin cancer lies in the visual similarity between benign and malignant lesions, which often leads to misdiagnosis even by experienced medical personnel. The development of deep learning technology has made significant progress in medical image classification through a transfer learning approach. This study aims to compare the performance of two architectures from Transformer and CNN, namely Swin Transformer and ConvNeXt, in the task of classifying two class benign and malignant skin cancer images. Both models use pretrained from ImageNet and are applied with three different fine-tuning strategies, namely Linear Probe (LP), Full Fine-Tuning (FT), and a combination of the two previous strategies (LP-FT). The dataset used is the ISIC Archive Dataset with an 80:20 data split for training and validation, consisting of 3.297 images divided into two classes, with 1800 benign images and 1.497 malignant images. The evaluation was performed using the accuracy, precision, recall, and F1-score metrics. Swin Transformer with the LP-FT strategy achieved the best performance, with an accuracy of 92,27%, precision of 92,24%, recall of 92,17%, and an F1-score of 92,20%. These findings indicate that the two-stage fine-tuning approach can improve model stability and generalization, as well as contribute to the development of a more accurate artificial intelligence based skin cancer diagnosis system.
Co-Authors Ahmad Bintang Saputra Almira Zuhrotus Safira Eustachius Dito Dewantoro