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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) LOYALITAS: Jurnal Pengabdian Kepada Masyarakat
Cici El Dirrah Syafitri Simanungkalit
Universitas Islam Negeri Sumatera Utara
Automotive Engineering Computer Science & IT Control & Systems Engineering

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Comparative Analysis of Sobel, Prewitt, and Canny Methods in Detecting Object Edges in Betta Fish Images Alfin Alfarizi; Cici El Dirrah Syafitri Simanungkalit; Fahmi Nur Alimsyah Purba; Lailan Sofinah Harahap
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2293

Abstract

Edge detection is a crucial stage in digital image processing for recognizing the shape and structure of an object. The application of edge detection to betta fish images presents a unique challenge due to their layered, intricately textured, and often semi-transparent fin morphology. This study aims to analyze and compare the performance of three edge detection algorithms, namely Sobel, Prewitt, and Canny, in extracting shape features from betta fish images. The research methodology involved converting the dataset images into a grayscale format and subsequently implementing the three algorithms using the OpenCV library in the Python programming language. The evaluation was conducted visually by observing the sharpness of the edge lines, object continuity, and the occurrence of noise. The results indicate that the Canny algorithm provides the most optimal performance, as it is capable of detecting the thin edge lines of the fish fins with greater detail and continuity due to its hysteresis thresholding process. Meanwhile, the Sobel and Prewitt methods produced thicker edge lines but were less sensitive to the details of the transparent fins. This study is expected to serve as a reference in selecting the appropriate segmentation method for biological objects with complex morphologies.
Sosialisasi QRIS sebagai Upaya Peningkatan Literasi Keuangan dan Pendapatan UMKM melalui Program KKN di Kawasan Eks Lokalisasi Bandar Baru Cici El Dirrah Syafitri Simanungkalit; Divia Inge Salsabila; Irfan Aqil Ramadhan; Nur Wafiq Azizah; Rifky Bas Praptama Sembiring; Bagus Ramadi
LOYALITAS: Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 2 (2025): November 2025
Publisher : Universitas KH. Mukhtar Syafaat (UIMSYA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30739/loyalitas.v8i2.4292

Abstract

This community engagement program focuses on the socialization of Quick Response Code Indonesian Standard (QRIS) as a strategy to enhance financial literacy and income among Micro, Small, and Medium Enterprises (MSMEs) in the ex-localization area of Bandar Baru, Sibolangit. The program was conducted as part of the Community Service (KKN) of Universitas Islam Negeri Sumatera Utara. The methods applied include observation, structured interviews, and direct mentoring to MSMEs, particularly in introducing QRIS as a digital payment system. The socialization activities emphasized the importance of digital financial literacy, practical training on QRIS usage, and awareness of consumer protection through the PEKA (Peduli, Kenali, Adukan) program. The findings revealed that prior to the program, only 13.3% of MSMEs had used QRIS, while after the intervention, 50% expressed interest and began registering for QRIS. Furthermore, participants demonstrated increased understanding of QRIS functions, technical skills in digital transactions, and more positive attitudes toward cashless payments. Although challenges remained, such as limited access to digital devices and weak internet connectivity, the program successfully promoted behavioral changes and provided new insights into digital finance. The results indicate that socialization through KKN is effective in improving financial literacy and encouraging digital transformation for MSMEs in rural communities.
Co-Authors Alfin Alfarizi Bagus Ramadi Divia Inge Salsabila Fahmi Nur Alimsyah Purba Irfan Aqil Ramadhan Lailan Sofinah Harahap Nur Wafiq Azizah Rifky Bas Praptama Sembiring