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All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Proceeding of the Electrical Engineering Computer Science and Informatics Annual Research Seminar PROCEEDING IC-ITECHS 2014 Proceeding of the Electrical Engineering Computer Science and Informatics Sriwijaya Journal of Informatics and Applications Jurnal Generic
M. Fachrurrozi .
Computer Science Faculty, Universitas Sriwijaya
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Journal : Sriwijaya Journal of Informatics and Applications

 1 
Face Detection Using Randomized Hough Transform (RHT) with Various Ellipses Segmentations Muhammad Fachrurrozi; Saparudin Saparudin; Mardiana Mardiana; Desty Rodiah; Winda Agusthia
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Face detection is one of earlier phase in face recognition process. This research aims to get the faces area on digital image without being affected by face orientation, lights condition, background and the expression. The detected face area is usually shaped by a rectangle. Many pixels on the rectangle are not part of face, especially at the four of the image corners. This research use an ellipse as replacement a rectangle. The detected face is shaped by ellipses with various sizes and orientations. The digital image segmentations is used to detect face candidates area. The ellipse is formed by using Randomized Hough Transform (RHT) method, which is influenced by the center point of ellipse candidates. RHT found three random pixels on segmented image. The rate of success of RHT is determined by segmentation results. The research result is tested by using various thresholds, and get the best accuracy at 74.4%. The rate of accuracy is measured by comparing between RHT ellipses shape and circle shape on OpenCV library as ground truth.
CLASSIFICATION OF ATRIAL FIBRILLATION IN ECG SIGNAL USING DEEP LEARNING Raihan Mufid Setiadi; Muhammad Fachrurrozi; Muhammad Naufal Rachmatullah
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.53

Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
Sign Language A-Z Alphabet Introduction American Sign Language using Support Vector Machine Muhammad Rasuandi; Muhammad Fachrurrozi; Anggina primanita
Sriwijaya Journal of Informatics and Applications Vol 4, No 2 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i2.74

Abstract

Deafness is a condition where a person's hearing cannot functionnormally. As a result, these conditions affect ongoing interactions,making it difficult to understand and convey information.Communication problems for the deaf are handled through theintroduction of various forms of sign language, one of which isAmerican Sign Language. Computer Vision-based sign languagerecognition often takes a long time to develop, is less accurate, andcannot be done directly or in real-time. As a result, a solution isneeded to overcome this problem. In the system training process,using the Support Vector Machine method to classify data and testingis carried out using the RBF kernel function with C parameters,namely 10, 50, and 100. The results show that the Support VectorMachine method with a C parameter value of 100 has betterperformance. This is evidenced by the increased accuracy of the RBFC=100 kernel, which is 99%.
Co-Authors Abdiansah Abdiansah Ade Iriani Sapitri Anggun Islami Anna Dwi Marjusalinah Annisa Darmawahyuni Auzan Lazuardi Bambang Tutuko Clara Fin Badillah Desty Rodiah Dewi Chayanti Erwin, Erwin Firdaus Firdaus Jerry Satiamy Saputra Junia Erlina Kanda Januar Miraswan Lidya Irfiyani Silaban Mardiana Mardiana Muhammad Naufal Rachmatullah Muhammad Naufal Rachmatullah Muhammad Omar Braddley Muhammad Rasuandi Novi Yusliani Novi Yusliani Osvari Arsalan Primanita, Anggina Rafly Pakomgan Raihan Mufid Setiadi Saparudin Saparudin Saparudin Saparudin Saparudin, Saparudin Winda Agusthia Yoga Yuniadi Yunita Yunita