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All Journal Jurnal Sistem Informasi dan Ilmu Komputer Ignite: Journal Islamic Global Network for Information Technology and Entrepreneurship
Rosa Prahasti
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Analisis Performa Algoritma Pendeteksian Tepi pada Citra Multispektral Supiyandi Supiyandi; Dinah Makhroza Silalahi; Dwi Prapita Sari; Rosa Prahasti; Donny Dwi Putra
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 2 No. 3 (2024): Agustus : Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v2i3.3472

Abstract

Multispectral image is a type of digital image that captures spectral information in several channels or bands. Edge detection is one of the basic techniques in image processing which is used to identify the boundaries of objects in an image. This research aims to analyze the performance of several edge detection algorithms on multispectral images. The algorithms tested include the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian (LoG) algorithms. Tests were carried out on high resolution multispectral images from the Landsat-8 satellite. The evaluation metrics used are accuracy, precision, recall, and F1-score. The research results show that the Canny algorithm has the best performance with the highest F1-score compared to other algorithms. Apart from that, this research also analyzes the effect of the number of channels in multispectral images on the performance of edge detection algorithms.
Facial Landmarks and Face Detection in Python With OpenCv Supiyandi Supiyandi; Icha Miranti Irzan; Risma Hidayati; Rosa Prahasti; Natria Selina
Journal Islamic Global Network for Information Technology and Entrepreneurship Vol. 2 No. 4 (2024): Journal Islamic Global Network for Information Technology and Entrepreneurship
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/ignite.v2i4.2011

Abstract

Face detection and facial landmarks are an important technique in the field of computer vision with a wide range of potential applications, including expression recognition, security systems, and human-computer interaction. This study explores the implementation of facial landmarks detection using Python and OpenCV, focusing on the use of the Haar Cascade algorithm for face detection and the Local Binary Features (LBF) model for the identification of landmarks. The proposed method implements real-time detection via webcam, capable of recognizing 68 important points on the human face. The results show that the approach using OpenCV and LBF models has good accuracy in detecting and tracking facial features in different lighting conditions and viewing angles. This research contributes to the development of efficient and reliable facial detection methods, with wide application potential in the fields of computer vision, security, and behavioral analysis.
Co-Authors Dinah Makhroza Silalahi Donny Dwi Putra Dwi Prapita Sari Icha Miranti Irzan Natria Selina Risma Hidayati Supiyandi Supiyandi