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All Journal Jurnal Ilmu dan Teknologi Kesehatan Computer Science (CO-SCIENCE)
Andy Maulana Yusuf
Telkom University
Computer Science & IT Health Professions

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Pendekatan Hibrida menggunakan Sistem Inferensi Fuzzy dan Pembelajaran Mendalam untuk Klasifikasi Penyakit Alzheimer pada Citra MRI Bagas Wibowo; Andy Maulana Yusuf; Bintang Vieshe Mone; Sabrina Adinda Sari
jitek Vol 13 No 2 (2026): Maret 2026
Publisher : Poltekkes Kemenkes Jakarta III

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32668/jitek.v13i2.2381

Abstract

Early detection of Alzheimer's disease using brain MRI image data can substantially improve clinical intervention and patient management. Our study evaluates the performance of an Alzheimer's classification system based on Fuzzy Inference Systems (FIS), specifically for the Mamdani and Sugeno models, in identifying four patient categories: (1) Non-Dementia, (2) Very Mild Dementia, (3) Mild Dementia, and (4) Moderate Dementia. In addition, this study compares the classification performance and computational efficiency of several deep learning architectures, including a traditional CNN (VGG16), a modern model (EfficientNet-B0), and a hybrid Fuzzy Convolutional Inference Engine (FCIE) that integrates CNN-based feature extraction with fuzzy logic reasoning. The dataset used consists of normalized and augmented Alzheimer's MRI images, and each model was trained and validated using a 70%:15%:15% split for training, validation, and testing. Experimental results show that the Mamdani and Sugeno FIS models achieve validation accuracies of about 32% and 35%, respectively, which highlights the limitations of pure texture-based features in capturing complex classification patterns. In contrast, VGG16 and EfficientNet-B0 produced validation accuracies of 82.81% and 85.22%, respectively, with AUC values of 0.95 and 0.96, respectively. However, when both schemes were combined into a hybrid model FCIE achieved the highest validation accuracy of 98.03% and AUC of 0.99. Comparative analysis of metrics, including precision, recall, F1-score, AUC, and training duration, shows a clear trade-off between accuracy and computational efficiency. This study recommends the FCIE model for clinical applications requiring high diagnostic accuracy, while EfficientNet-B0 is suggested for medical environments with moderate GPU resource constraints.
Integrating LLM Intent-Aware Approach for Enhancing the Quality of Bundle Recommendations Andy Maulana Yusuf; Musrinah Musrinah
Computer Science (CO-SCIENCE) Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i2.12101

Abstract

Digital grocery shopping has shifted consumer patterns toward multi- item purchasing. While bundle recommendation systems address this, existing models relying on product ID co-occurrence fail to capture the mixed shopping intentions inherent in consumer baskets. To address these limitations, we propose Intent-aware Bundle Recommendation (IABR), a framework shifting from structural matching to semantic intent reasoning. IABR utilizes Large Language Models to decompose baskets into coherent sub-packages. Subsequently, we fine- tuned Gemma-3-4B using Parameter-Efficient Fine-Tuning to generate narrative intent descriptions regarding short-term shopping missions and long-term sustainable user lifestyles. These intents are encoded via Sentence Transformers for semantic retrieval. Extensive testing on the Instacart dataset demonstrates IABR’s significance against baselines like BGCN. Our IABR method achieved a Recall@20 of 28.15% while improving diversity scores by 12% (p < 0.05). This validates that generative semantic modeling enables accurate next- bundle predictions, effectively balancing precision with thematic variation and personalization.
Co-Authors Bagas Wibowo Bintang Vieshe Mone Musrinah Musrinah Sabrina Adinda Sari