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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer AL-UMRON: Jurnal Pengabdian kepada Masyarakat
Putri, Valencia Sefiana
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Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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PENDAMPINGAN PEMBANGUNAN WEBSITE DAN KONTEN DIGITAL KREATIF DI ERA 5.0 BERBASIS GENERATIVE ARTIFICIAL INTELLIGENCE (GEN-AI) Basuki, Setio; Faiqurrahman, Mahar; Putri, Valencia Sefiana; Nugraha, Muhammad Daffa; Shafiyah, Rahajeng Febri
Al-Umron : Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): AL-UMRON : Jurnal Pengabdian kepada Masyarakat
Publisher : LEMBAGA PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (LPPM) UNIVERSITAS NAHDLATUL ULAMA SUNAN GIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/alumron.v6i1.4191

Abstract

The use of Generative Artificial Intelligence (Gen-AI) in digital content development and website development is a new approach to digital marketing in the 5.0 era. The community service program spearheaded by Universitas Muhammadiyah Malang (UMM) aims to help schools improve branding, visibility, and promotional effectiveness with AI technology that generates automated content, so that they can create websites and creative content without coding using Gen-AI. The methods used include (i) observation, (ii) digital marketing strategy development, (iii) training module development, (iv) mentoring implementation, and (v) evaluation of results. The program invited 20 teachers from three Secondary Schools. The effectiveness of this program was evaluated through questionnaires before and after mentoring, related to several aspects, namely (i) Understanding of AI, (ii) Utilization of AI to Create Text Teaching Materials (Textbooks), (iii) Utilization of AI to Create Learning Videos, (iv) Utilization of AI to Create Websites. The results showed a significant increase in the aspect of understanding of AI increased from 62% (pre-test) to 83% (post-test), utilization of AI for text teaching materials increased from 64% (pre-test) to 85% (post-test), and utilization of AI to create learning videos and websites increased from 57% (pre-test) to 82% (post-test).
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.
Co-Authors Basuki, Setio Faiqurrahman, Mahar Nugraha, Muhammad Daffa Shafiyah, Rahajeng Febri