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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Annual Research Seminar Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT)

Ahmad Zarkasi

Universitas Sriwijaya

Author-ID : 2892414

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 19 Documents Claim Missing Document

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Title

Implementation Color Filtering and Harris Corner Method on Pattern Recognition System Ahmad Zarkasi; Sutarno Sutarno; Huda Ubaya; Muhammad Fajar
Computer Engineering and Applications Journal Vol 6 No 3 (2017)
Publisher : Universitas Sriwijaya

Color recognition and angle detection of underwater objects can be done with the help of underwater robots (ROV) with image processing applications. The processing of the object's image is recognizing various shapes and colors of objects in the water. In this research, the color filtering and Harris corner method will be designed, studied, tested and implemented. The color filtering method is used to recognize object color patterns, while the Harris Corner method is used to detect angles of underwater objects. Then classify images to get data on environmental pattern recognition. The color patterns tested include red, green, yellow and blue. the results obtained are all color patterns can be recognized well. while the shape of the object being tested includes cubes, triangles, rectangles, pentagons, and hexagons. the results of testing some of the shapes can be detected with a good angle and others still have errors. This is because testing the form of objects is done in various positions, such as from the front, right, left, up and below.Â

Performance Comparison of Feature Face Detection Algorithm on The Embedded Platform Ahmad Zarkasi; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto; Huda Ubaya; Rizki Kurniati
Computer Engineering and Applications Journal Vol 11 No 2 (2022)
Publisher : Universitas Sriwijaya

The intensity of light will greatly affect every process carried out in image processing, especially facial images. It is important to analyze how the performance of each face detection method when tested at several lighting levels. In face detection, various methods can be used and have been tested. The FLP method automates the identification of the location of facial points. The Fisherface method reduces the dimensions obtained from PCA calculations. The LBPH method converts the texture of a face image into a binary value, while the WNNs method uses RAM to process image data, using the WiSARD architecture. This study proposes a technique for testing the effect of light on the performance of face detection methods, on an embedded platform. The highest accuracy was achieved by the LBPH and WNNs methods with an accuracy value of 98% at a lighting level of 400 lx. Meanwhile, at the lowest lighting level of 175 lx, all methods have a fairly good level of accuracy, which is between 75% to 83%.

Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method Ahmad Zarkasi; Huda Ubaya; Kemahyanto Exaudi; Alif Almuqsit; Osvari Arsalan
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.444

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.

Co-Authors Abdurahman Ades Harafi Duri Aditya P. P. Prasetyo Aditya P.P. Prasetyo Agung Juli Anda Alif Almuqsit Arti Dian Nastiti Bambang Tutuko Bhakti Yudho Suprapto Bhakti Yudho Suprapto Boby Darmawansyah Citra Madona Denny Wahyudi Marzuk Deris Stiawan Devin Dimas Mahendra Eka Fasilah Endang Darmawan Yudi Fachrudin Abdau Firdaus Firdaus Gede Pradnyananda Huda Ubaya Huda Ubaya Ilham Junius Angkotasan Kemahyanto Exaudi Muhammad Al Ravi Muhammad Fajar Muhammad Ismuhariandy Muhammad Naufal Halim Nanda Syafitri Osvari Arsalan Ricy Ricy Firnando Rizki Kurniati Rossi Passarela Rossi Passarella Sarmayanta Sembiring Siti Nurmaini Sri Desy Siswanti Sutarno Sutarno

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