TEM Journal. Volume 12, Issue 1, pages 111-117, ISSN 2217-8309, DOI: 10.18421/TEM121-15, February 2023. TEM Journal – Volume 12 / Number 1 / 2023. 111 Comparative Analysis of Image on Several Edge Detection Techniques Adi Budi Prasetyo 1 , Rizki Wahyudi 1 , Imam Tahyudin 1 , Selvia Ferdiana Kusuma 2,3 , Luzi Dwi Oktaviana 1 , Azhari Shouni Barkah 1 , Budi Artono 1 1 Universitas Amikom Purwokerto, Purwokerto, Indonesia 2 Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia 3 Politeknik Negeri Madiun, Madiun, Indonesia Abstract – Edge detection of an image is needed to obtain information related to the size and shape of an image. There are numerous methods for detecting edges, including the Prewitt, Laplace, and Kirsch operators. Each edge detection method has different performance and results. Therefore, this study aims to analyze the performance comparison of the Prewitt, Laplace, and Kirsch operators. The analysis process is carried out using MSE, PSNR and Image Contrast values. Based on the experiments that have been carried out, the best edge detection is produced by the Prewitt operator. The average MSE and PSNR values obtained were 4.63 and 41.79 dB. The Laplace operator is good in the contrast value of 17.77. However, the contrast value only serves as a supporting parameter to clarify the differences in the results of each edge detection operator. So it can be concluded that the Prewitt edge detection method is the best method among the other two methods. Keywords – Edge Detection, Kirsch, Laplace, MSE, PSNR, Prewitt. 1. Introduction Image processing is needed to enhanced the quality of the image to be processed, so that later the image can be easily interpreted by humans or machines [1], [2], [3]. DOI: 10.18421/TEM121-15 https://doi.org/10.18421/TEM121-15 Corresponding author: Rizki Wahyudi, Universi tas Amikom Purwokerto, Purwokerto, Indonesia. Email: rizkiw@amikompurwokerto.ac.id, rizki.key@gmail.com Received: 23 September 2022. Revised: 14 December 2022. Accepted: 17 January 2023. Published: 27 February 2023. © 2023 Adi Budi Prasetyo et al; published by UIKTEN. This work is licensed under the Creative Commons Attribution‐ NonCommercial‐NoDerivs 4.0 License. The article is published with Open Access at https://www.temjournal.com/ Several types of image processing operations, such as Image enhancement, image restoration, image segmentation, image analysis, and image reconstruction are all subcategories. The image analysis process is needed to identify parameters related to the characteristics of objects in the image, then use these parameters to interpret the image [4], [5]. Image analysis consists of 3 stages, namely, feature extraction, segmentation, and classification. The key factor in feature extraction is the ability in edge detection. Edge detection in the image is the process of generating the edges of objects in the image so that the boundary line information in the image can be displayed [6]. The purpose of edge detection is to improve image detail that contains noise [7]. Edges of image are considered as important features in images for estimating the attributes and structure of edge objects that are usually recognized at the border between two different image regions [8]. The edge detection method is divided into seven operators, namely the Sobel operator, Roberts operator, Laplace operator, Canny operator, Prewitt operator, Laplacian of Gaussian (LoG) operator, and Kirsch operator [9]. Improper use of edge detection operators will result in failed detection [10]. Assessment of edge detection quality can be done objectively using the calculation of Mean Squared Error (MSE), Peak Noise to Signal Ratio (PSNR) and Histogram value. MSE is the average squared error between the actual value and the forecast value. PSNR is the ratio of the maximum value of the bit depth measured by the amount of influential noise [11]. The histogram is a graph that describes the spread of pixel intensity values of an image. Histograms can be used to find out important information from an image. In addition to objectively assessing the quality of edge detection, there is also a subjective quality assessment using the human senses. However, this is difficult to do because the assessment is very dependent on human vision [12].