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All Journal Journal of Applied Data Sciences Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Mhd. Furqan
Universitas Islam Negeri Sumatera Utara, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Algoritma Support Vector Machine Untuk Analisis Sentimen Masyarakat Indonesia Terhadap Pandemi Virus Corona Di Media Sosial Mhd. Furqan; Mhd. Ikhsan; Rafizah Aini
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 4, No 4 (2023): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v4i4.241

Abstract

The corona virus pandemic refers to the spread of coronavirus disease 2019 or what is known as coronavirus disease 2019 in various parts of the world. This outbreak is a change from a new type of coronavirus called SARS-CoV-2. The issue of this pandemic has become a hot topic of discussion, including on social media. The most frequently used platform among the public is Twitter. On social media, the corona virus pandemic has always been a topic of conversation that is often discussed, causing controversy. Controversy occurs because every day the opinions on social media Twitter regarding the corona virus pandemic are always increasing so that, when people read news on social media about the pandemic, it raises concerns because people's opinions are different. From this problem the author will create a system that analyzes opinions from Twitter social media to get opinion sentiment about what is happening in the community regarding the problem of the corona virus pandemic. This study uses the SVM method which is fast and effective for text classification. The results of this study will classify positive, negative and neutral sentences. The accuracy obtained from the model with the SVM algorithm is 98%. Testing is done by calculating precision, recall, F-measure..
Multiclass Skin Lesion Classification Algorithm using Attention-Based Vision Transformer with Metadata Fusion Mhd. Furqan; Norliza Katuk; Dedy Hartama
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1017

Abstract

Early and accurate classification of skin lesions is essential for timely diagnosis and treatment of skin cancer. This study presents a novel multiclass classification framework that integrates dermoscopic images with clinical metadata using an attention-based Vision Transformer (ViT) architecture. The proposed model incorporates a mutual-attention fusion mechanism to jointly learn from visual and tabular inputs, augmented by a class-aware metadata encoder and imbalance-sensitive loss function. Training was conducted using the HAM10000 dataset over 30 epochs with a batch size of 32, utilizing the Adam optimizer and a learning rate of 0.0001. The model demonstrated superior performance compared to a ViT Baseline, achieving 93.4% accuracy, 92.2% F1-score, 0.95 AUC, and significant reductions in MAE and RMSE. Additionally, Grad-CAM visualizations confirmed the model’s ability to focus on diagnostically relevant regions, enhancing interpretability. These findings suggest that the integration of structured clinical information with transformer-based visual analysis can significantly improve classification robustness, particularly in underrepresented lesion types. However, the model’s current performance is evaluated only on the HAM10000 dataset, and its generalizability to other clinical or non-dermoscopic image sources remains to be validated. Future studies should therefore explore multi-institutional datasets and real-world deployment scenarios to assess robustness and scalability. The proposed framework offers a practical, interpretable solution for AI-assisted skin lesion diagnosis and demonstrates strong potential for clinical deployment.
Co-Authors Dedy Hartama Mhd. Ikhsan Norliza Katuk Rafizah Aini