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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 ABDIKOM
Ahmad Zarkasi
Universitas Sriwijaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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Rancang Bangun Sendok Parkinson Menggunakan ESP-32 Dan Metode Complementary Filter Devin Dimas Mahendra; Ahmad Zarkasi
Generic Vol 12 No 2 (2020): Vol 12, No 2 (2020)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Penelitian ini dimaksudkan untuk membuat rancang bangun alat bantu makan berupa sendok yang di khususkan untuk penderita Parkinson. Rancang bangun sendok penderita Parkinson dalam penelitian ini mengunakan sensor 3-Axis gyroscope accelerometer MPU-6050, Mikrokontroler ESP32, dan Motor Servo SG-90 untuk menstabilkan posisi sendok akibat getaran tangan penderita Parkinson. Data yang diperoleh dari penelitian ini adalah sensor MPU-6050 yang mendeteksi getaran tangan penderita lalu data tersebut distabilkan menggunakan metode complementary filter yang berfungsi untuk meminimalisir Error pada sensor MPU-6050.
Implementation of Facial Landmarks Detection Method for Face Follower Mobile Robot Ahmad Zarkasi; Fachrudin Abdau; Agung Juli Anda; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto; Huda Ubaya; Rizki Kurniati
Generic Vol 14 No 1 (2022): Vol 14, No 1 (2022)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

This paper presents a new technique for facesrecognition based on auto-extracted facial marks. Our landmarks are those related to the outer corner of the nose. With extracted landmarks, a triplet of areas and their associated geometric invariance are formed. Where later the points on the outer corners of the eyes and nose will be connected with lines that will form a triangle. Later the line length will be calculated using the Euclidean Distance formula so that the area value of the triangle can be obtained. Then the data obtained will be trained using the Support Vector Machine algorithm so that they can recognize faces. And later the system will be implanted into a mobile robot with raspberry.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (405.575 KB) | DOI: 10.18495/comengapp.v11i2.405

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 movement controller based on dynamic facial pattern recognition Siti Nurmaini; Ahmad Zarkasi; Deris Stiawan; Bhakti Yudho Suprapto; Sri Desy Siswanti; Huda Ubaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp733-743

Abstract

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).
Robot Vision Penyortir Menggunakan Metode Logika Fuzzy Berbasis Image Processing Abdurahman - -; Ahmad Zarkasi; Citra Madona
Generic Vol 14 No 2 (2022): Vol 14, No 2 (2022)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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Abstract

Kemajuan teknologi industri 4.0 mulai menjadi aspek dasar didunia industri saat ini. Otomasi pada sistem industri menjadi solusi untuk mengurangi human error dalam melakokan kegiatan produksi diindustri. Implementasi sistem tertanam berbasis kecerdasan buatan perlu dikembangkan sebagai prototype yang dapat membantu meningkatkan kualitas home industri. Pada penelitian ini implementasi robot penyortir telur dengan menggunakan metode fuzzy logic diharapkan dapat mengklasifikasikan telur berdasarkan bobot dan diameter telurnya. Robot menggunakan dua parameter utama dalam mengklasifikasikan telur yaitu bobot telur dan diamter telur. Kedua paramater ini menjadi input sistem fuzzy logic. Sistem penghitungan bobot menggunakan sensor load cell dan arduino uni sedangkan sistem perhitungan diamter telur menggunakan camera dan raspbery pi. Data bobot dan diameter telur kemudian akan di klasifikasikan menggunakan metode takagi sugeno kang. Robot sudah dapat menyortir telur berdasarkan ukuran fisik dan bobot dari telur, dengan rata-rata error yang di peroleh yaitu 0,0135 untuk diameter dari ukuran fisik telur dan 0,002543333 untuk bobot dari telur yang disortir.
Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern Ahmad Zarkasi; Siti Nurmaini; Deris Stiawan; Bhakti Yudho Suprapto
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 4: December 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i4.3957

Abstract

The facial recognition process is normally used to verify and identify individuals, especially during the process of analyzing facial biometrics. The face detection algorithm automatically determines the presence or absence of a face. It is, however, theoretically difficult to analyze the face of a system with limited resources due to the complex pattern of a face. This implies an embedded platform scheme which is a combination of several learning methods supporting each other is required. Therefore, this research proposed the combination of the Haar Cascade method for the face detection process and the WNNs method for the learning process. The WNNs face recognition Algorithm (WNNs-FRA) uses facial data at the binary level and for binary recognition. Moreover, the sample face data in the binary were compared with the primary face data obtained from a particular camera or image. The parameters tested in this research include detection distance, detection coordinates, detection degree, memory requirement analysis, and the learning process. It is also important to note that the RAM node has 300 addresses divided into three face positions while the RAM discriminator has three addresses with codes (00), (10), and (10). Meanwhile, the largest amount of facial ROI data was found to be 900 pixels while the lowest is 400 pixels. The total RAM requirements were in the range of 32,768 bytes and 128 bytes and the execution time for each face position was predicted to be 33.3% which is an optimization because it is 66.67% faster than the entire learning process
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.
Implementasi Scoreboard Digital untuk Pertandingan Bulutangkis pada Gedung Olahraga Fasilkom Unsri Berbasis Teknologi Wireless Kemahyanto Exaudi; Sarmayanta Sembiring; Aditya P.P Prasetyo; Rahmat Fadli Isnanto; Huda Ubaya; Ahmad Zarkasi; Adi Hermansyah
Jurnal Abdikom Vol 1 No 2 (2023): Jurnal Pengabdian Kepada Masyarakat Bidang Ilmu Komputer (ABDIKOM)
Publisher : Fakultas Ilmu Komputer

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Abstract

Implementasi Scoreboard pada lapangan bulutangkis sangat penting bagi para pemain maupun penonton didalam suatu pertandingan. Scoreboard memiliki peran penting dalam memberikan informasi angka yang diperoleh untuk menentukan yang menang dan kalah. Fasilkom Unsri memiliki gedung olahraga baru dengan fasilitas lapangan bulutangkis berstandar internasional. Namun perhitungan skor-nya masih menggunakan metode manual. Tujuan pengabdian ini dilakukan adalah untuk mewujudkan lapangan bulutangkis Fasilkom memiliki sistem perhitungan skor menggunakan Scoreboard digital berbasis wireless. Metode yang digunakan adalah dengan memberikan pelatihan langsung kepada para penggiat olahraga bulutangkis dalam menggunakan Scoreboard digital. Sistem Scoreboard digital ini terdiri dari dua bagian utama, yaitu Scoreboard digital berbasis wireless sebagai informasi skor perolehan dan aplikasi skor yang diaplikasikan oleh wasit menggunakan smartphone. Berdasarkan hasil pengabdian yang telah dilakukan menunjukkan bahwa Scoreboard digital ini berhasil diimplementasikan dengan baik dan memberikan dampak positif terhadap permainan bulutangkis di gedung olahraga fasilkom. Hal ini dibuktikan dengan semakin banyaknya pengguna lapangan bulutangkis di gedung fasilkom, sehingga dibutuhkan pembagian waktu penggunaan bagi yang ingin bermain bulutangkis.
Implementation of Fisherface Algorithm for Eye and Mouth Recognition in Face-Tracking Mobile Robot Ahmad Zarkasi; Huda Ubaya; Kemahyanto Exaudi; Ades Harafi Duri
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29266

Abstract

Facial recognition is an artificial intelligence algorithm that distinguishes one face from another by capturing facial patterns visually. This recognition specifically detects and identifies individuals based on facial features by scanning the entire face. Several methods are used for facial detection, including facial landmarks points, Local Binary Patterns Histograms (LBPH), and Fisherface. In the context of this research, Fisherface is used to reduce the dimensionality of facial space in order to obtain image features. The method is insensitive to changes in expression and lighting, leading to better pattern classification and making it suitable for implementation on mobile devices such as robot vision. Therefore, this research aimed to measure the response time speed and accuracy level of pattern recognition when implemented on mobile robot devices. The results obtained from the accuracy testing showed that the highest accuracy for face detection process was 90%, while the lowest was 78.3%. In addition, the average execution time (AET) for the fastest process was 1.63 seconds and the slowest was 1.72 seconds. For pattern recognition, the statistics showed 90% accuracy, 100% precision, 81.81% recall, and F-1 score of 89.5%. Meanwhile, the longest execution time was 0.084 seconds and the fastest was 0.064 seconds. In face tracking process, the mobile robot movement was based on real-time pixel sizes, determining x and y values to produce the center of face region.
Co-Authors Abdurahman Ades Harafi Duri Adi Hermansyah 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 Huda Ubaya Huda Ubaya Ilham Junius Angkotasan Kemahyanto Exaudi Muhammad Al Ravi Muhammad Fajar Muhammad Ismuhariandy Muhammad Naufal Halim Nanda Syafitri Osvari Arsalan Rahmat Fadli Isnanto Rizki Kurniati Rossi Passarela Rossi Passarella Sarmayanta Sembiring Siti Nurmaini Sri Desy Siswanti Sutarno Sutarno