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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Info Sains : Informatika dan Sains Jurnal Ilmiah Teknik Informatika dan Komunikasi Journal of Informatics, Electrical and Electronics Engineering Jurnal Sains Student Research Jurnal Media Akademik (JMA) Jurnal Intelek Dan Cendikiawan Nusantara Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi Jurnal Publikasi Ilmu Komputer dan Multimedia
Lailan Sofinah Harahap
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Penerapan Python dalam Perbandingan Metode Deteksi Tepi (Sobel, Prewitt, Canny) untuk Analisis Pengenalan Pola pada Gambar Daun Tengku Syahvina Rival Dini; Lailan Sofinah Harahap; Pebi Mina Husania
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol. 4 No. 2 (2025): Mei: Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v4i2.4062

Abstract

Digital image processing plays an important role in extracting visual information from natural objects, including the morphological structure of leaves. One of the crucial techniques in this process is edge detection, which is used to emphasize object boundaries and support pattern analysis. This study aims to compare three edge detection methods, namely Sobel, Prewitt, and Canny in recognizing patterns in leaf images using the Python programming language and the OpenCV library. The methods used include quantitative experiments by implementing the three edge detection techniques on grayscale leaf images, followed by visual result analysis based on the criteria of edge sharpness, morphological detail, noise, and computational efficiency. The results show that the Canny method produces the most accurate and clean edge detection, with the ability to capture small details and reduce noise significantly. Sobel shows quite good performance in highlighting the main structure of the leaf, while Prewitt produces simpler and less precise results. Based on the evaluation results, the Canny method is considered the most effective for the purposes of digital leaf pattern classification and analysis. This study provides an important contribution in selecting the optimal edge detection method for computer vision applications in the field of digital botany..
Co-Authors Abdul Rasyid Adinda Tarisyah Hsb Alisya Alfina Rizki Ritonga Alwi Andika Panggabean Arizka Anggraini Cici Melisma Cici Pratiwi Ela Khairani Br. Siregar Eva Andini Fajar Al Fahri Fajar Syakbani Ferdi Frans Dirga Fiqih Syahputra Henni Rosliana Pulungan Intan Widya Saputri Nst juwita sari M Choirul Amri Mazayah Tsaqofah Muhammad Nabhan Akbar Marpaung Naina Nazwa Hasibuan Nasution, Mansalwa Utama Nazwa Alya Faradita Nur Azizah Hidayat Nurhidayati - Pebi Mina Husania Puji Sri Alhirani Rani Chantika Rifdah Syahputri Rika Khairani Riko Prananda Prayugo SITI NURJANAH Tengku Syahvina Rival Dini Tia Ramadani Tiara Bela Harahap Warda Hamidah Winda Yunia Purnama