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Afina Lina Nurlaili
National Development University 'Veteran' of East Java
Computer Science & IT

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English Language Anya Ningrum Nur'afifah; Anggraini Puspita Sari; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2484

Abstract

The increasing prevalence of diseases in pet cats highlights the need for a diagnostic support system that is both fast and accurate. The Certainty Factor (CF) method is widely used in expert systems to represent uncertainty in decision-making. However, its reliance on subjective expert judgment can reduce diagnostic accuracy and system confidence. This study aims to optimize CF values using the Particle Swarm Optimization (PSO) algorithm, enabling the system to adapt better to actual medical data. CF values were initially collected from two veterinary experts and combined using the median method to minimize bias. These values were then optimized using PSO, with parameter tuning performed individually for each disease to maximize fitness. The dataset used for validation consisted of 100 medical records of cats diagnosed with one of nine common feline diseases, including Ringworm, Scabies, Helminthiasis, and others. The results show an increase in diagnostic accuracy from 85% (using original CF) to 88% (after PSO optimization). Moreover, 70.73% of the cases with consistent diagnoses before and after optimization showed an increase in final CF values, indicating greater confidence in the system’s diagnostic decisions. These findings suggest that the integration of CF with PSO not only improves diagnostic accuracy but also strengthens the reliability of expert systems in the veterinary field, particularly for early and efficient identification of cat diseases.
Random Forest – Deep Convolutional Neural Network Ensemble Model for Skin Disease Classification Ananda Rheza Kurniawan; Yisti Vita Via; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2528

Abstract

Skin diseases such as psoriasis, atopic dermatitis, and tinea are chronic conditions that significantly affect quality of life and require rapid and accurate classification to support early treatment. However, limited medical personnel and inadequate classification tools in various regions remain major challenges in handling these cases. This study proposes an automatic skin disease classification system based on digital images using an ensemble method that combines Deep Convolutional Neural Network (DCNN) and Random Forest (RF). The dataset used comprises 4,246 images categorized into four classes (psoriasis, atopic dermatitis, tinea, and normal skin), sourced from Kaggle and DermNet. Preprocessing steps include image resizing, normalization, and data augmentation, while hyperparameter tuning is conducted using Bayesian Optimization. The ensemble model applies a soft voting mechanism to integrate predictions from both DCNN and RF. Experimental results show that the RF-DCNN model achieves an accuracy of up to 84.35% in the 80:10:10 data split scenario, surpassing the performance of the conventional CNN model. These results suggest that the hybrid DCNN-RF approach enhances accuracy, stability, and generalization in skin disease classification. The proposed model holds strong potential for implementation in artificial intelligence-based clinical decision support systems, especially in regions with limited access to dermatology specialists. Future work is encouraged to explore more advanced architectures such as EfficientNet and Swin Transformer for further performance improvements.
Co-Authors Ananda Rheza Kurniawan Anggraini Puspita Sari Anya Ningrum Nur'afifah Yisti Vita Via