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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 Sinkron : Jurnal dan Penelitian Teknik Informatika 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 Jurnal Repositor Indian Journal of Forensic Medicine & Toxicology Journal of System and Computer Engineering 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 M.Rafly Rahman; Setio Basuki; Muhammad Ilham Perdana; La Febry Andira Rose Cynthia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
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.
Detecting Research Evolution and Trends in The Computer Vision Domain using Topic Modeling and Large Language Models Setio Basuki; Zamah Sari; Rizky Indrabayu; Masatoshi Tsuchiya
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 10 No. 1 (2026)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v10i1.2098

Abstract

Research evolution and trends in computer vision (CV) are important for understanding the field’s land-scape. Trends show which topics are gaining attention, while evolution reveals how those topics change over time. Understanding both helps researchers gain insight into CV and anticipate emerging areas of focus. How-ever, the rapid growth of publications makes such detection challenging. This paper aims to detect research evolution and trends in CV using topic modeling (TM) and large language model (LLM) techniques. The study applies TM and LLM approaches to papers from leading CV conferences, Computer Vision and Pattern Recog-nition (CVPR), International Conference on Computer Vision (ICCV), and Winter Conference on Applications of Computer Vision (WACV), published between 2013 and 2023, totaling more than 21,000 papers, using only abstracts and titles. The TM methods used are Latent Dirichlet Allocation (LDA) and Bidirectional Encoder Representations from Transformers for Topic Modeling (BERTopic), which generate keywords that represent topics. LLMs then refine these topics to support better analysis. The results show that research evolution and trends are easier to identify from abstracts than from titles, with BERTopic outperforming LDA in internal va-lidity based on coherence metrics and external validity based on human judgment. In addition, the topics evolved from traditional image processing tasks in earlier years to a stronger focus on deep learning and, more recently, generative approaches. Integrating TM techniques with LLMs enhances the detection of evolving re-search themes and trends in CV. This approach provides a clearer understanding of the field's development and helps anticipate future directions.
Explainable Non-Organic Waste Classification: A Comparative Study of CNN with SHAP Interpretability Against Vision Transformer Approaches Setio Basuki; Lika Anjelina; Alfian Wahyu Juhar Putra; Yusuf Nur Muhammad
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2694

Abstract

This study develops a classification model based on Machine Learning (ML) and Computer Vision (CV) to automatically distinguish recyclable and non-recyclable waste. Waste management still faces the challenge of low manual sorting efficiency, thus reducing recycling potential. The dataset consists of 399 waste images divided with a ratio of 80:10:10 for training, validation, and testing. Three combinations of visual features were tested, namely Mean Color (RGB) + LBP + HOG, Histogram + HOG + Edge, and Mean Color (RGB) + Histogram + GLCM, each of which was evaluated using four conventional ML algorithms, namely SVM-RBF, SVM-Linear, Random Forest, and Gradient Boosting. Meanwhile, deep learning models namely CNN, ViT, and LoRA ViT were trained directly on raw images without manual feature extraction. Experimental results show that CNN achieved the highest testing accuracy of 82.50%, outperforming all conventional ML models that achieved a maximum accuracy of 75.00%, as well as ViT (72.50%) and LoRA ViT (70.00%). The application of SHAP-based Explainable AI (XAI) provides transparency to the model's decision-making process. These findings demonstrate that CNN with certain regularization settings are effective for distinguishing recyclable and non-recyclable waste, in supporting sustainable smart waste management systems.
SC-Literature Intelligence: A Retrieval-Augmented Generation Framework for Multi-Category AI Literature Synthesis in Supply Chain Setio Basuki; Amelia Khoidir; Muhammad Ilham Perdana; Muhammad Daffa Nugraha; Masatoshi Tsuchiya
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16477

Abstract

This paper develops SC-Literature Intelligence, a retrieval-augmented generation (RAG) framework for research synthesis of scientific literature on artificial intelligence (AI) in the supply chain domain. The study addresses the fragmentation of scientific findings, which makes cross-document understanding difficult by supporting four categories of literature-analysis queries: trend analysis, gap detection, comparative synthesis, and evidence-based question answering (QA). The primary novelty lies in introducing a category-aware research synthesis framework capable of evaluating RAG performance across multiple literature-analysis tasks rather than conventional question answering. The framework is built from Scopus-indexed abstracts through pre-processing, chunk-based embedding using BGE-M3 and LaBSE, vector storage, semantic retrieval, and prompt-guided generation evaluated using the RAGAS framework across 640 experimental runs. The results show that BGE-M3 consistently outperforms LaBSE on all RAGAS indicators with the best configuration (chunk size 64, Top-K 5) achieving scores between 0.722 and 0.856 across faithfulness, answer relevancy, context precision, and context recall. Gap detection emerges as the best-supported query category, whereas comparative synthesis remains the most challenging. Failure analysis further reveals that retrieval-stage issues dominate over generation-stage issues, identifying embedding quality as the primary bottleneck. These findings demonstrate that category-aware RAG-based synthesis can support structured, evidence-grounded literature analysis in the supply chain AI domain.
Co-Authors Abdachul Charim Abdillah, Abid Famasya Agus Eko Minarno Akbi, Denar Regata Akmal Shahib, Maulana Alfian Wahyu Juhar Putra Alfira Rizky Alimuddin Hasan Al Kabir Amelia Khoidir 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 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 Kartina, Leny Khoirir Rosikin Kusuma, Selvia Ferdiana La Febry Andira Rose Cynthia Lika Anjelina Lina Dwi Yulianti M.Rafly Rahman Mahar Faiqurahman Masatoshi Tsuchiya Mauridhi Hery Purnomo Mizwar Mizwar Muhammad Daffa Nugraha Muhammad Fadliansyah Muhammad Ilham Perdana Muhammad Nasrul Tsalatsa Putra Muhammad Rizki Muhammad Yusuf Mustikasari, Rahma Ira Novita Daian Marlissa Nugraha, Muhammad Daffa Nur Hayatin Putri, Valencia Sefiana Rangga Pratama Rima Mediana Mashita Risa Etika, Risa Rizky Indrabayu 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 Yusuf Nur Muhammad Zachra, Fatimatus Zakiyah Rakhmawati Zamah Sari