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All Journal Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Havni Virul
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Computer Science & IT

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Pendeteksi Gerakan Pada Vidio Menggunakan Pyton dan OpenCV Supiyandi Supiyandi; Andriani Sitorus; Nurul Fitriah; Havni Virul; Syawaliah Putri Rangkuti
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 2 No. 6 (2024): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v2i6.522

Abstract

Motion detection is an important process in computer vision to analyze activities in videos. This study implements a simple system to detect motion in video files using Python and the OpenCV library. The system works by comparing consecutive frames in a video to detect changes and mark areas that experience motion. The implementation shows satisfactory results on various sample videos. This study provides a solution that is easy to implement and can be used in applications such as video analysis and computer-based monitoring.
Pengolahan Citra Huruf Hijaiyah Menggunakan Algoritma Support Vector Machine Lisa Amelia Putri; Andriani Sitorus; Nurul Fitriah; Havni Virul; Syawaliah Putri Rangkuti; Supiyandi Supiyandi
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 2 No. 3 (2024): Agustus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v2i3.168

Abstract

Hijaiyah letters are the alphabet used in Arabic and the Koran. Automatic recognition of hijaiyah letters has many benefits, especially in the fields of education and learning Arabic and the Koran. This research aims to classify hijaiyah letter recognition using image processing techniques and the Support Vector Machine (SVM) algorithm. We collected a dataset of images of 5 hijaiyah letters with a total of 400 images obtained from Google and also Iqro'. The train:test ratio is 8:2. Experimental results show that the proposed approach can achieve high accuracy in recognizing hijaiyah letters with an accuracy rate of 99.16%.
Co-Authors Andriani Sitorus Lisa Amelia Putri Nurul Fitriah Supiyandi Supiyandi Syawaliah Putri Rangkuti