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All Journal ComEngApp : Computer Engineering and Applications Journal JTEV (Jurnal Teknik Elektro dan Vokasional Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Emitor: Jurnal Teknik Elektro Annual Research Seminar JURNAL NASIONAL TEKNIK ELEKTRO Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jusikom : Jurnal Sistem Komputer Musirawas Dinamisia: Jurnal Pengabdian Kepada Masyarakat Journal of Information Technology and Computer Engineering KOMPUTIKA - Jurnal Sistem Komputer GERVASI: Jurnal Pengabdian kepada Masyarakat JTT (Jurnal Teknologi Terapan) Jurnal Teknologi Elekterika Jurnal Generic REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat J-Innovation Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Rekayasa elektrika ABDIKOM Emitor: Jurnal Teknik Elektro
Kemahyanto Exaudi
Fakultas Ilmu Komputer Universitas Sriwijaya
Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : ComEngApp : Computer Engineering and Applications Journal

 1 
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

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.
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.
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

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

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

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

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.
Implementation of Feature Selection for Optimizing Voice Detection Based on Gender using Random Forest Abdurahman; Vindriani, Marsella; Prasetyo, Aditya Putra Perdana; Sukemi; Buchari, M. Ali; Sembiring, Sarmayanta; Firnando, Ricy; Isnanto, Rahmat Fadli; Exaudi, Kemahyanto; Dudifa, Aldi; Riyuda, Rafki Sahasika
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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

Abstract

Gender-based voice detection is one of the machine learning applications that has various benefits in technology and services, such as virtual assistants, human-machine interaction systems, and voice data analysis. However, the use of too many features, including irrelevant features, can cause a decrease in accuracy and model performance. This research aims to optimize voice-based gender detection by applying a feature selection method to select significant features based on their correlation value to the target. Experimental results show that by using only the significant features selected through correlation analysis, the accuracy of the model is significantly improved compared to using all available features. This research confirms the importance of feature optimization to support the development of more efficient and accurate gender-based speech detection models.
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 Abdul Wahid Sempurna Abdul Wahid Sempurna Abdul Wahid Sempurna Abdurahman Abdurahman Ades Harafi Duri Adi Hermansyah Aditya P.P Prasetyo Aditya P.P. Prasetyo Aditya PPP Aditya Putra Perdana Prasetyo Ahmad Fali Oklilas Ahmad Fali Oklilas Ahmad Rifai Ahmad Zarkasi Ahmad Zarkasi Alif Almuqsit Almuqsit, Alif B Azhar, Iman Saladin Bagus Prasetyo Bangun Sudrajat Bangun Sudrajat Barzan Trio Putra Brema Alfaretz Tarigan Buchari, M. Ali Dedy Kurniawan Deris Stiawan Desyandri Desyandri Dody Firmansyah Dudifa, Aldi Fakhrurroja, Hanif Fatimah, Sayyidatina Fitriyanto, Megi Hadir Kaban Huda Ubaya Huda Ubaya Huda Ubaya Ichsan Mahjud Izzati Millah Hanifah Jorena Jorena M. Dimas Firmansyah Mileandira, Leviarta Monica Ayu Amaria Muhammad Ajran Saputra Muhammad Furqon Rabbani Nabillah Selva Setiawan Nadhira, Wardha Osvari Arsalan Pingki Pingki Prasetiyo, Bagus Prasetyo, Aditya P P Prasetyo, Aditya P.P. Prasetyo, Aditya PP Prasetyo, Aditya Putra Perdana Purwita Sari Purwita Sari Purwita Sari Purwoko, Agus Putra Perdana Prasetyo, Aditya R Rendyansyah Rahmad Fadli Isnanto Rahmat Budiarto Rahmat Fadli Isnanto Rahmatullah, Ikang Rendyansyah Rendyansyah Rendyansyah Rendyansyah Rendyansyah Ricy Firnando Rido Zulfahmi Riyuda, Rafki Sahasika Romadhona, Londa Arrahmando Rony, Zahara Tussoleha Rossi Passarella Roswitha Yemima Tiur Mediswati Sari, Komang Mita Sarmayanta Sembiring Sarmayanta Sembiring Sarmayanta Sembiring Sayyidatina Fatimah Sri Desy Siswanti Sri Desy Siswanti Sry Desy Siswanti Sukemi Sutarno Sutarno Tharisa Antya Perdani Tri Wanda Septian Tri Wanda Septian, Tri Wanda Vindriani, Marsella Wahyu Gunawan Winda Kurnia Sari