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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Tropical and Infectious Disease JOIN (Jurnal Online Informatika) Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Paediatrica Indonesiana Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JRST (Jurnal Riset Sains dan Teknologi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat JSiI (Jurnal Sistem Informasi) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Gema Ngabdi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Repositor Indian Journal of Forensic Medicine & Toxicology AL-UMRON: Jurnal Pengabdian kepada Masyarakat Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Jurnal Komputer, Informasi dan Teknologi
Basuki, Setio
Universitas Muhammadiyah Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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Explainable AI-Driven Convolution Neural Network for Quality Grading of Soybean Seeds Putri, Valencia Sefiana; Basuki, Setio
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1566

Abstract

This study developed a soybean seed grading system based on Explainable Artificial Intelligence (XAI). Traditional soybean quality assessment is time-consuming, and limited research has applied explainable AI methods to the grading process. To address these issues, this study employed classification and XAI methods through several stages. First, it examined five main categories of soybean seed characteristics: broken, immature, intact, skin-damaged, and spotted. Second, it used the Soybean Seeds Dataset contain-ing 5,513 images. Third, data preprocessing was carried out, including image normalization and data division for training and testing. Finally, a Convolutional Neural Network (CNN) model based on the VGG-16 architecture was used for classification experiments. Three XAI methods, namely Shapley Additive Explanations (SHAP), Local Interpretable Model Agnostic Explanations (LIME), and Layerwise Relevance Propagation (LRP), were applied to evaluate model performance and interpretability. The VGG-16 model achieved an accuracy of 91%, with precision, recall, and F1-score values of 0.91, 0.91, and 0.90, respectively. The interpretability analysis using SHAP, LIME, and LRP showed that the model consistently identified key features such as seed shape and surface texture, demonstrating that the system is transparent and reliable in determining soybean seed quality.
Klasifikasi Hoax Vs Non-Hoax Pada Berita Bencana Alam Berbahasa Indonesia Menggunakan Word Embedding Rangga Pratama; Setio Basuki
Jurnal Komputer, Informasi dan Teknologi Vol. 5 No. 1 (2025): Juni
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v5i1.2338

Abstract

Hoaks atau berita palsu terkait bencana alam dapat menyebabkan kepanikan dan disinformasi yang berdampak luas pada masyarakat. Oleh karena itu, diperlukan metode otomatis untuk mengklasifikasikan berita hoax dan non-hoax secara efektif. Penelitian ini mengimplementasikan metode Word Embedding dan algoritma Long Short-Term Memory (LSTM) dalam klasifikasi berita hoax bencana alam berbahasa Indonesia. Tiga model Word Embedding yang digunakan adalah Word2Vec, FastText, dan GloVe. Proses penelitian melibatkan tahap preprocessing data, pembagian dataset, implementasi model LSTM, hingga analisis kinerja model dengan menggunakan metrik akurasi, precision, recall, serta F1-score. Hasil dari penelitian ini menyatakan bahwa model FastText dengan LSTM memberikan akurasi tertinggi sebesar 99%, diikuti oleh Word2Vec-LSTM dan GloVe-LSTM. Model FastText mampu menangkap informasi dari kata-kata yang jarang muncul, meningkatkan efektivitas dalam mendeteksi berita hoax. Selain itu, teknik augmentasi data menggunakan metode Random Synonym Replacement terbukti meningkatkan variasi dan keseimbangan dataset, yang berdampak positif pada performa model. Dengan penelitian ini, diharapkan dapat menjadi acuan bagi peneliti selanjutnya dalam pengembangan sistem deteksi berita hoax yang lebih akurat dan efisien, khususnya dalam konteks berita bencana alam.  
Integrating Tabular Data and Textual Representations for Clinical Risk Prediction Using Machine Learning and Large Language Models Rahman, M.Rafly; Basuki, Setio; Perdana, Muhammad Ilham; Cynthia, La Febry Andira Rose
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2570

Abstract

Global health is currently facing serious challenges due to the increasing number of chronic disease patients, such as those with heart failure, diabetes, and cancer. This issue arises from the limitations of electronic health record (EHR) systems, which are not yet fully capable of ensuring accurate clinical diagnoses because of potential data input errors and delays in symptom identification by medical personnel. In response to this issue, this paper focuses on the integration of medical tabular data with a classification approach based on classical machine learning (ML) and large language models (LLM) to improve the accuracy of patient diagnosis predictions. This paper aims to develop and compare the performance of various ML models, such as XGBoost, SVM, and logistic regression, as well as LLM models like Gemini, LLaMA, and Qwen in fine-tuning, few-shot, and zero-shot scenarios. The paper results show that the combination of Gemini and the few-shot approach (250 shots) achieved the highest accuracy of up to 99.8% in predicting heart failure risk. The main finding of this study is that the narrative text representation of tabular data processed with LLM significantly enhances contextual understanding and classification accuracy, making this approach highly potent for application in AI-based clinical decision-making.
Robustness Under Attack: Assessing Adversarial Fragility in Deep Learning Models for COVID-19 Radiography Prediction Kamil, Muhammad Hisyam; Farma, Elga Putri Tri; Basuki, Setio
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1506

Abstract

Deep learning, especially Convolutional Neural Network (CNN) architectures, has significantly improved medical image analysis for predicting lung diseases through chest X-ray (CXR) images, including pneumonia and COVID-19. However, despite achieving high diagnostic precision, CNN models remain highly susceptible to adversarial attacks, defined as small, visually imperceptible alterations optimized to exploit non-linear decision boundaries that cause high-confidence mispredictions. This vulnerability presents a critical concern in clinical settings, where deterministic diagnostic errors directly compromise patient safety. This paper systematically implements white-box adversarial attacks to quantify the resilience of CNN models in multi-class CXR image classification. This paper utilizes the COVID-19 Radiography Dataset, comprising four diagnostic categories: COVID-19, Lung Opacity, Normal, and Viral Pneumonia. A DenseNet-121 architecture was employed for feature extraction, and the trained model was subsequently subjected to Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks under varying L∞​-bounded epsilon settings. The empirical experiments reveal three critical findings: 1) The implementation of sub-pixel adversarial attacks causes severe performance degradation, where the PGD attack constrained at an epsilon of 0.1/255 reduced the global model accuracy from a baseline of 95.42% to 25.32%; 2) Iterative attacks (PGD) represent the absolute worst-case scenario for model reliability by efficiently discovering high-dimensional manifold gaps, whereas the model demonstrates relative resilience to linear, single-step FGSM perturbations; and 3) Gradient-weighted Class Activation Mapping (Grad-CAM) analysis verifies that this performance collapse is associated with a deterministic semantic shift, displacing the model's spatial attention from clinically relevant pulmonary regions toward spurious background noise. In conclusion, this paper empirically proves that despite exhibiting high accuracy on clean data, unprotected CNNs remain fundamentally unsafe for autonomous clinical deployment due to their acute vulnerability to gradient-based perturbations, necessitating the future integration of robust adversarial training frameworks
Co-Authors Abdachul Charim Abdillah, Abid Famasya Agus Eko Minarno Akbi, Denar Regata Akmal Shahib, Maulana Alfira Rizky Alimuddin Hasan Al Kabir Aminudin Aminudin Amrul Faruq Aulia Arif, Wardhana Baiduri, Senja Bangkit Putrawan Charim, Abdachul Diany Yogiantoro Dini Tri Purwaningsih Dominicus Husada Dwiyanti Puspitasari, Dwiyanti Edo Ardhiansyah Effendy, Nico Ardia Faiqurrahman, Mahar Faizun Nuril Hikmah Farma, Elga Putri Tri Gita Indah Marthasari Haq, Arini Hariyady Hariyady Hendra Saputra Hilman Hilman Hilwana, Lutifta Husada, Dominicius Indrabayu, Rizky Irfan, Muhammad irma fitriani Irwanto Irwanto Ismoedijanto Izzah, Tsabita Nurul Kamil, Muhammad Hisyam Kartina, Leny Khoirir Rosikin Kusuma, Selvia Ferdiana La Febry Andira Rose Cynthia Lina Dwi Yulianti Mahar Faiqurahman Mauridhi Hery Purnomo Mizwar Mizwar Muhammad Fadliansyah Muhammad Nasrul Tsalatsa Putra Muhammad Rizki Muhammad Yusuf Mustikasari, Rahma Ira Novita Daian Marlissa Nugraha, Muhammad Daffa Nur Hayatin Perdana, Muhammad Ilham Putri, Valencia Sefiana Rahman, M.Rafly Rangga Pratama Rima Mediana Mashita Risa Etika, Risa Rizky Sulaiman Rizky, Alfira S, Vinna Rahmayanti Sari, Zamah Shafiyah, Rahajeng Febri Siti Maghfiroh Soegeng Soegiyanto Sumadi, Fauzi Dwi Setiawan Syafaah, Lailis Titin Eka Puspitawati Tsuchiya, Masatoshi Wibowo, Prasetyo Wicaksono, Galih Wasis Wisnujono Soewono Yuda Munarko Yufis Azhar Zachra, Fatimatus Zakiyah Rakhmawati Zamah Sari