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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) INTECOMS: Journal of Information Technology and Computer Science Jurnal Informatika Global Prosiding Konferensi Nasional PKM-CSR Knowbase : International Journal of Knowledge in Database Jurnal Pengabdian Masyarakat: BAKTI KITA International Journal of Multidisciplinary Approach Research and Science
Yenie Syukriyah
Universitas Widyatama
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Nursing Public Health Social Sciences Other

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Image Segmentation for Sweet Potato Leaf Disease Detection using U-Net Yenie Syukriyah; Adi Purnama
International Journal of Multidisciplinary Approach Research and Science Том 3 № 03 (2025): International Journal of Multidisciplinary Approach Research and Science
Publisher : PT. Riset Press International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59653/ijmars.v3i03.1848

Abstract

The detection and management of sweet potato leaf diseases play a vital role in ensuring sustainable crop yields and reducing agricultural losses. This study proposes an automated segmentation approach using the U-Net convolutional neural network to detect disease regions on sweet potato leaves. The dataset, consisting of leaf images and corresponding masks, underwent a structured preprocessing pipeline including resizing, normalization, and reshaping. The U-Net architecture, comprising an encoder-decoder structure with skip connections, was trained on 70% of the dataset and evaluated using accuracy, Intersection over Union (IoU), and Dice coefficient. Experimental results show that the model achieved an accuracy of 94.6%, IoU of 0.88, and a Dice coefficient of 0.92, indicating strong segmentation performance. Visual comparison between predictions and ground truth masks further confirms the model’s effectiveness in isolating disease regions. This research demonstrates the potential of U-Net as a reliable deep learning framework for plant disease detection and contributes to the development of intelligent agricultural monitoring systems.
PENINGKATAN LITERASI BAGI APARATUR LAYANAN PUBLIK MELALUI EDUKASI TEKNOLOGI BLOCKCHAIN DALAM MENDUKUNG LAYANAN E-GOVERNMENT DI SAMSAT KUNINGAN Azizah Zakiah; Viddi Mardiansyah; Ulil Surtia Zulpratita; Yenie Syukriyah
Jurnal Pengabdian Masyarakat : BAKTI KITA Vol. 7 No. 1 (2026): Nopember - April
Publisher : LPPM Universitas Islam Darul 'Ulum Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52166/baktikita.v7i1.12484

Abstract

Digital transformation in public services requires the enhancement of technological literacy among government officials, particularly regarding blockchain technology which has the potential to improve transparency, data security, and efficiency in e-Government services. However, the level of understanding of this technology among public service officers remains relatively low. This Community Service Program (PKM) aims to improve blockchain technology literacy among public service officers at the Regional Revenue Management Center of Kuningan Regency as part of efforts to support e-Government development. The activity was conducted through a webinar-based socialization and educational program consisting of material presentations, interactive discussions, and evaluation of participants’ understanding using pre-test and post-test instruments. The results indicate a significant improvement in participants’ understanding, where most participants who were initially in the low understanding category shifted to the good understanding category after the activity. This improvement is also reflected in the Understanding Index (UI) and the percentage of learning achievement increase. Furthermore, high participant enthusiasm and active involvement during the activity demonstrate the effectiveness of the program. This PKM activity provides a meaningful contribution to strengthening the readiness of human resources as an initial foundation for implementing blockchain technology to support more transparent, secure, and efficient e-Government services.
Co-Authors Adi Purnama Azizah Zakiah Bagus Alit Prasetyo Esa Fauzi Falahah . Feri Sulianta Indriani Indriani Pradhana, Risky Reza SY Yuliani Ulil Surtia Zulpratita Viddi Mardiansyah yakub, sy yuliani yuliani Yan Puspitarani