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All Journal Jurnal Matematika Jurnal Sains dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIEET (Journal of Information Engineering and Educational Technology) JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal Matematika UNAND Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Informatika (JUPITA) International Journal of Applied Mathematics and Computing. Konstanta : Jurnal Matematika dan Ilmu Pengelatuan Alam Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Wijayakusuma, I Gusti Ngurah Lanang
Universitas Udayana
Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Analysis of ResNet50 Model Response to Skin Tone Variations in Medical Image-Based Skin Disease Classification Made Ireina Dwiandra Divayanti; I Gusti Ngurah Lanang Wijayakusuma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12794

Abstract

Skin disease classification with deep learning has shown promising performance, however many models are primarily trained on datasets featuring light skin tones, which raises question about their effectiveness across a variety of akin types. This study analyses the response of a ResNet50 model based on transfer learning when faced with different skin tones in order to classifying skin disease using medical images. The model was trained on the HAM100000 which categorized into three classes: benign, malignant, and non-neoplastic. A bias analysis was then performed using the Fitzpatrick 17k dataset. The model demonstrated an overall accuracy of 70.85%, a precision rate of 74.03%, and a recall rate of 65.51%. Further analysis showed that the model had a consistent pattern of predicting malignant cases, which increased with darker skin tones, rising from 54% to 68.3%. To mitigate this issue, a threshold tuning approach was applied. After mitigation, the model achieved an accuracy of 74%, a weighted F1-score of 76%, dan a macro F1-score of 55%. Fairness evaluation after mitigation showed tha the proportion of malignant predictions increased from 56,3% in FST I to 69,9% in FST VI. These findings suggest that threshold tuning can improve classification performance and partially reduce bias intensity.
Comparative Analysis of ConvNeXt and EfficientNet-B0 for Early Leukemia Detection through Blood Cell Classification with Grad-CAM Interpretability Made Andini Maharani; I Gusti Ngurah Lanang Wijayakusuma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12816

Abstract

Leukemia is a hematological malignancy requiring early and accurate diagnosis for optimal patient outcomes, yet conventional microscopic examination remains subjective, time-consuming, and prone to inter-observer variability. This study presents a comprehensive comparative analysis of two state-of-the-art deep learning architectures EfficientNet-B0 and ConvNeXt-Tiny for multi-class blood cell classification aimed at early leukemia detection. Using a balanced dataset of 5,000 microscopic images encompassing five clinically significant classes (basophil, erythroblast, monocyte, myeloblast, and segmented neutrophil), both models were trained and evaluated under identical configurations with extensive data augmentation. Performance assessment encompassed classification metrics, inference speed, and interpretability through Gradient-weighted Class Activation Mapping (Grad-CAM) validated by randomization and occlusion tests. Results demonstrated that both architectures achieved exceptional performance with F1-scores exceeding 98% (EfficientNet-B0: 0.9893, ConvNeXt: 0.9920). ConvNeXt exhibited superior accuracy in distinguishing morphologically similar cells, attributed to its larger receptive fields and advanced architectural design, while EfficientNet-B0 demonstrated dramatic computational advantages with 134 FPS throughput and a compact model size of 18.3 MB six times smaller than ConvNeXt. Grad-CAM visualizations confirmed that both models focus on clinically relevant features including nuclear morphology and cytoplasmic characteristics, validated by low correlation with randomized models (average correlation <0.28) and significantly larger confidence drops during important region occlusion (6-18× greater than random occlusion). The findings establish evidence-based guidelines for model selection, ConvNeXt for high-precision diagnostic applications and EfficientNet-B0 for large-scale screening and edge deployment. This research contributes foundational evidence toward the development of transparent, reliable, and efficient computer-aided diagnosis systems, though prospective clinical validation on multi-institutional datasets remains an important direction for future work.
Multi-label Deep Learning for Thoracic Disease Co-occurrence in Chest Radiography Syahril Alamsyah Zainuddin; I Gusti Ngurah Lanang Wijayakusuma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12840

Abstract

Chest radiography (Chest X-Ray) remains the primary imaging modality for thoracic disease diagnosis, yet remains susceptible to misdiagnosis due to anatomical complexity and overlapping pathologies. This study proposes a multi-label Deep Learning framework based on the MobileNetV2 architecture for simultaneous classification of 14 pulmonary pathologies. To address the extreme class imbalance inherent in medical datasets, a two-stage fine-tuning strategy, ColorJitter augmentation, and class weighting (pos_weight) in the Binary Cross-Entropy Loss function were implemented. Furthermore, probability threshold optimization was performed dynamically for each class using Youden’s J Statistic. Ablation study results indicate that the baseline model achieved Mean AUROC of 0.828, while the proposed method achieved Mean AUROC of 0.822. However, this marginal trade-off was strategically accepted to achieve the primary clinical objective: dramatically improving sensitivity (Recall) on critical minority pathologies, including Cardiomegaly (from 73.6% to 85.1%), Fibrosis (64.6% to 72.5%), and Hernia (71.9% to 75.0%). This framework enables simultaneous multi-label classification of 14 pulmonary pathologies using independent sigmoid activations, which inherently supports the detection of co-occurring conditions without enforcing mutual exclusivity. Consequently, the approach demonstrates enhanced clinical utility as a medical screening instrument by substantially suppressing false negative rates for high-risk pathologies. When deployed as a Computer-Aided Diagnosis (CAD) system with appropriate clinical validation, this framework demonstrates the potential to serve as a secondary screening tool in healthcare settings with limited access to specialist radiologists, particularly for detecting high-risk pathologies such as Cardiomegaly and Fibrosis.
Four-Class Brain Tumor Classification Using ConvNeXt with Grad-CAM-Based Explainable Ni Kadek Jegeg Anastasya Dwipayanti; I Gusti Ngurah Lanang Wijayakusuma
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13607

Abstract

Brain tumor is one of the most critical diseases with high mortality rates requiring early and accurate diagnosis. This study proposes a deep learning-based brain tumor classification system using the ConvNeXt-Base architecture to classify four categories: glioma, meningioma, pituitary tumor, and no tumor. To further advance this field, this study addresses two open challenges in prior literature, the need for larger and more diverse datasets to ensure model robustness, and the demand for model transparency (resolving the black-box problem) to facilitate clinical adoption.. To address these, this study combines MRI images from three public sources, Figshare (Cheng et al., 2016), Br35H (Hamada, 2020), and Mendeley (Hira et al., 2025), totaling 11,474 unique images after perceptual hash-based deduplication. The model was trained using progressive unfreezing with AdamW optimizer, CosineAnnealingLR scheduler, and class-weighted cross-entropy loss. Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated as an Explainable AI (XAI) approach to visualize the regions influencing model decisions. The proposed ConvNeXt-Base model achieved an accuracy of 99.25%, macro precision of 99.23%, macro recall of 99.22%, macro F1-score of 99.22%, and macro AUC-ROC of 0.9995 on the test set. Grad-CAM visualizations confirm that the model focuses on clinically relevant tumor regions, particularly the sella turcica for pituitary tumors and distinct mass boundaries for meningioma, thereby improving interpretability and clinical trustworthiness of the AI system.
Co-Authors Agustina, Ni Putu Dina Anak Agung Kompiang Oka Sudana Arta Wiguna, I Putu Chandra Astutik, Dian Br Sebayang, Virna Dalira Chandra, Veronica Celine Damayanthi, Ni Wayan Rita Desak Made Sidantya Amanda Putri Dian Astutik, Ni Putu Driyandita, Bernadeta EKA N. KENCANA Fransisca Emmanuella Aryossi G K Gandhiadi I Gusti Ayu Made Srinadi I Ketut Gede Darma Putra I ketut Gede Darma Putra I Ketut Restu Wiranata I KOMANG GDE SUKARSA I MADE EKA DWIPAYANA I Nyoman Widana I Putu Eka Nila Kencana I Putu Winada Gautama I PUTU WINADA GAUTAMA I Putu Winada Gautama I WAYAN SUMARJAYA IM Suyana Utama IPW Gautama Isabel Divya Georgiana Walewangko Jocelynne, Charlotte Karina Maharani Bernis Karina Maharani Bernis Kencana, Eka N. Ketut Jayanegara Khairunisa, Mutiara Komang Dharmawan Leonard Kumaro LUH PUTU IDA HARINI LUH PUTU IDA HARINI Made Andini Maharani Made Ardika Mertha Putra Vaikuntha Made Ireina Dwiandra Divayanti Made Sudarma Made Sudarma Manurung, Mikael Triartama Maylianti, Ni Putu Minho Jo Minho Jo Ni Kadek Emik Sapitri Ni Kadek Jegeg Anastasya Dwipayanti Ni Luh Putu Suciptawati Ni Nyoman Bintang Marscelina Ni Putu Leony Putri Paramita Oka Sudana Pratama, Halim Meliana Pratiwi Tentriajaya, I Dewa Ayu Pradnya Putri, Desak Putri, Desak Made Sidantya Amanda Putri, Made Ayu Asri Oktarini Putu Veri Swastika Raharja, Made Agung Ratna Sari Widiastuti Riandika Fathur Rochim Sagun Chandra Yowani Sibannang, Maria Oktaviani Giska Siden, Hagia Sofia Swastika, Putu Veri Syahril Alamsyah Zainuddin Tentriajaya, I Dewa Ayu Pradnya Pratiwi Tobing, Charlotte Jocelynne L Ulfatun Farika Novitasari Widiastuti, Ratna Sari Yohanes Kristianto Kristianto