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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jurnal Pendidikan Sosiologi dan Humaniora Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal KALAM Analisis: Jurnal Studi Keislaman Jurnal Script Jurnal Fisika Unand Jurnal Informatika dan Teknik Elektro Terapan JAWI : Journal of Southeast Asia Islamic Contemporary Issues Jurnal Fisika FLUX Al-Adyan: Jurnal Studi Lintas Agama Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Reka Buana : Jurnal Ilmiah Teknik Sipil dan Teknik Kimia Jurnal Kreativitas PKM Indonesian Physical Review Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Community Development Journal: Jurnal Pengabdian Masyarakat Spektrum Sipil Jurnal Generic Reputasi: Jurnal Rekayasa Perangkat Lunak Al-Iftah: Journal of Islamic studies and society Innovative: Journal Of Social Science Research ABDIKOM Socio Religia
Ahmad Zarkasi
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Journal : ComEngApp : Computer Engineering and Applications Journal

 1 
The Eye and Nose Identification Chip Controller-Based on Robot Vision Using Weightless Neural Network Method Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Fitriyanto, Megi
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.615

Abstract

Increasingly advanced image analysis in computer vision, allowing computers to interpret, identify, and analyze pictures with accuracy comparable to humans. The availability of data sources in decimal, hexadecimal, or binary forms enables researchers to take the initiative in applying their study findings. Decimal formats are typically used on traditional computers like desktops and minicomputers, whereas hexadecimal and binary formats were utilized on single-chip controllers. Weightless Neural Network is a method that can be implemented in a single chip controller. The aim of this research is to develop a facial recognition system, for eye and mouth identification, that works in a single chip controller or also called a microcontroller. The suggested method is a Weightless Neural Network with Immediate Scan approach for processing and identifying eye and nose patterns. The data will be handled in many memory locations that are specifically designed to handle massive volumes of data. The data is made up of primary face data sheets and face input data. The data sets utilized are (x,y) pixels, and frame sizes range from 90x90 pixels to 110x110 pixels. Each face shot will be processed by selecting the region of the eyes and nose and saving it as an image file. The eye and nose will identify the face frame. Next, the photos will be converted to binary format. A magazine matrix will be used to transmit binary data from a minicomputer to a microcontroller via serial connection. Based on a known pattern, the resultant similarity accuracy is 83,08% for the eye and 84,09% for the sternum. In contrast, the similarity percentage for an eye ranges from 70% to 85% for an undefined pattern.
Performance Comparison of Feature Face Detection Algorithm on The Embedded Platform Zarkasi, Ahmad; Nurmaini, Siti; Stiawan, Deris; Suprapto, Bhakti Yudho; Ubaya, Huda; Kurniati, Rizki
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 2 (2022)
Publisher : Universitas Sriwijaya

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Abstract

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 Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Almuqsit, Alif; Arsalan, Osvari
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

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.
Implementation of Weightless Neural Network in Embedded Face Recognition for Eye and Nose Pattern Mobile Identification Zarkasi, Ahmad; Exaudi, Kemahyanto; Sazaki, Yoppy; Romadhona, Londa Arrahmando
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

The pattern of the human face is a form of self-identity and also a form of originality for each individual. The development of facial recognition technology impacts its application in various computing devices, both in computer vision and on single-chip processors. One of the continuously developed implementations is in the form of robot vision by identifying facial features. This research aims to develop a facial recognition system focusing on the identification of the eye and nose areas. This research utilizes the Weightless Neural Network (WNN) method with the Immediate Scan technique. The combination of methods allows for rapid and accurate pattern recognition, even when the face changes position. The detection process is carried out using the Haar Cascade Classifier algorithm, which functions to recognize faces and divides the area into nine different zones to ensure accurate identification. The hardware implementation was carried out on a Raspberry Pi for face detection and facial pattern recognition, as well as the data processor for the robot vision sensor and actuator on the microcontroller. The results of the robot's movement testing have worked well according to the calculation of GPS data values to determine the robot's last position. Then, in the face pattern recognition process, it shows that the proposed method can achieve a maximum accuracy level of up to 98.87% in testing with the internal data set, while testing under different conditions experiences a slight decrease in accuracy to 91.38%. The highest similarity percentage to the faces of other individuals reached 75.69%, indicating that this method is quite adaptive to various facial variations. The execution time of the identification process ranges from 11 ms to 17 ms, depending on the amount of data compared during the scanning. This research is expected to serve as a foundation for further development in robotics systems and embedded system-based facial recognition.
The Eye and Nose Identification Chip Controller-Based on Robot Vision Using Weightless Neural Network Method Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Fitriyanto, Megi
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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

Abstract

Increasingly advanced image analysis in computer vision, allowing computers to interpret, identify, and analyze pictures with accuracy comparable to humans. The availability of data sources in decimal, hexadecimal, or binary forms enables researchers to take the initiative in applying their study findings. Decimal formats are typically used on traditional computers like desktops and minicomputers, whereas hexadecimal and binary formats were utilized on single-chip controllers. Weightless Neural Network is a method that can be implemented in a single chip controller. The aim of this research is to develop a facial recognition system, for eye and mouth identification, that works in a single chip controller or also called a microcontroller. The suggested method is a Weightless Neural Network with Immediate Scan approach for processing and identifying eye and nose patterns. The data will be handled in many memory locations that are specifically designed to handle massive volumes of data. The data is made up of primary face data sheets and face input data. The data sets utilized are (x,y) pixels, and frame sizes range from 90x90 pixels to 110x110 pixels. Each face shot will be processed by selecting the region of the eyes and nose and saving it as an image file. The eye and nose will identify the face frame. Next, the photos will be converted to binary format. A magazine matrix will be used to transmit binary data from a minicomputer to a microcontroller via serial connection. Based on a known pattern, the resultant similarity accuracy is 83,08% for the eye and 84,09% for the sternum. In contrast, the similarity percentage for an eye ranges from 70% to 85% for an undefined pattern.
Co-Authors -, Abdurahman - Abdurahman Abdurahman Adi Hermansyah, Adi Aditya Yoga Purnama Adrianus Inu Natalisanto Adryan Fitrayudha Agustam Agustam Ain, Hurun Almuqsit, Alif Amanda, Anggun Diah Amelia, Dinda Risky Amirin Kusmiran Andi Eka Putra Anwar Efendy Aroby, Sudhan Athalaza, Muhammad Nawar Aulia Muttaqin Bhakti Yudho Suprapto Bintar Asror Syaffutra Cahya, Serdian Eska Deris Stiawan Dhiafah Hera Darayani Duri, Ades Harafi Ellya Rosana Erna Kumalasari Erni Yustissiani Fadli Isnanto, Rahmat Fariyadin, M. Ruslin Anwar, Indradi Wijatmiko, Adiman Fitriyanto, Megi Ghofar Taufiq Hadi Purnawan Satria Hadipurnawan Satria Hamdani, Hafiz Hananda Putra, Muhammad Fauzan Hariyadi, Hariyadi Hasan, Aly Heni Pujiastuti Heriyanto, Ahmad Heru, M. Hidayat, Ahmad Taufiq Hidayatullah, Muhammad Huda Ubaya Idris Mandang, Idris Idrus Ruslan Intifadhah, Sahara Hamas Isfanari Isfanari Joni Safaat Adiansyah Kalsum, Afif Umi Kemahyanto Exaudi Kholis Nurhanafi Kurniati, Rizki Melan Susanti, Melan Mislan Muh. Deni Kurniawan Muhammad Afif Munir, Rahmawati Naniek Widyastuti Ni Nyoman Kencanawati Novianti, Intan Nugroho Budi Wibowo Nuraidha, Amalia C Nurul Hidayati Osvari Arsalan P.P Prasetyo, Aditya Primanita, Anggina Putra, Lalu Lunk Ryanata Rahayu, Rafika Ade Ramli, Azhar Jaafar Romadhona, Londa Arrahmando Salim, Luthfi Sari, Putri Winda Sarmayanta Sembiring Sasmito Setiawati, Susi Siti Nurmaini Sudarman Suhar Janti Suhendri Suhendri Sukuryadi, Sukuryadi Sutarno Sutarno Sutarno Syafrimen Syafril Syahrir Syahrir Syarif Hidayatullah Titik Wahyuningsih Ubaidillah, Aji Syailendra Wahidah Wahidah Wahyudi, Mudji winarti, dwi Wulandari, Darmawati Y. A. P, Kadek Dwivayana Yesi Novaria Kunang Yudha, Gesit Yudi, Endang Darmawan