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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

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.

Sistem Deteksi Kematangan Buah Pisang Berdasarkan Warna Kulit Menggunakan Metode HSV Zarkasi, Ahmad; Y. A. P, Kadek Dwivayana; Ubaya, Huda; Afifah, Nurul; Heriyanto, Ahmad; Sazaki, Yoppy; -, Abdurahman -
Generic Vol 16 No 1 (2024): Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i1.178

Pengolahan citra digital merupakan teknik manipulasi citra secara digital yang khususnya menggunakan komputer menjadi citra lain yang sesuai dengan kebutuhan. Klasifikasi kematangan pisang dapat dilakukan dengan dua cara, yaitu dengan menggunakan kandungan nutrisi dan tingkat kematangan warna pisang. Penelitian ini pengusulkan pendeteksian kematangan buah pisang berdasarkan warna kulit, dengan menggunakan metode ruang warna HSV (Hue, Saturation, Value). Komponen prosesor utama menggunakan Raspberry Pi 3B sebagai pengolah data Raspberry Pi Camera V2 sebagai penangkap citra buah pisang. Hasil penelitian ini berupa sistem bisa membedakan warna dari buah pisang yang berada dalam satu frame. Hasil yang diperoleh adalah , nilai efektif HSV yang didapat dari pengujian deteksi warna kuning kulit buah pisang adalah Hmin 15, Hmax 40-60, Smin 100, Smax 255, Vmin 60, dan Vmax 255. Dengan nilai HSV tersebut didapatkan nilai rata-rata keberhasilan sebesar 55%.

Perancangan Alat Penditeksi Kebakaran Menggunakan Sensor Api dan Sensor Asap Berbasis Internet of Things (IoT) Sembiring, Sarmayanta; Ubaya, Huda; Mileandira, Leviarta; Exaudi, Kemahyanto
Generic Vol 16 No 1 (2024): Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i1.180

Telah dirancang sebuah sistem yang dapat menditeksi terjadinya kebakaran dengan menggunakan 2 buah sensor flame KY-026 dan 1 sensor asap MQ2. Output sistem ini adalah 2 buah LED, Buzzer, notifikasi berbasis Internet of Things menggunakan blynk app dengan pusat pengendali menggunakan mikrokontroler ESP32. Sensor flame KY-026 dapat mendeteksi berbahaya apabila nilai ADC kurang dari 130, cukup berbahaya <231, cukup aman>297, dan aman >330 dengan jarak minimal 0 cm dan maksimal 200 cm. Sensor MQ2 mendeteksi dengan nilai ADC lebih dari 650. Hasil pengujian menunjukkan sistem yang dirancang dapat bekerja sesuai dengan yang diinginkan dengan tingkat keberhasilan mencapai 100%, pada saat pengujian sebanyak 10 kali.

Sistem Kendali Sirkulasi Udara dan Pembatasan Jumlah Pelanggan Toko Berbasis IoT Hanif, Labiq Al; Prasetyo, Aditya Putra Perdana; Ubaya, Huda
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 02 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.02.81-92.2021

The emergence of the COVID-19 pandemic in early 2020 had a major impact on human life on a global scale. Many actions and policies are aimed at anticipating transmission and breaking the chain of the spread of the COVID-19 virus, thus requiring store owners to implement various health protocols. This study discusses the monitoring system for the condition of the storeroom in real-time with the IoT concept, and the implementation of Sugeno fuzzy logic in controlling the speed of the exhaust fan motor to circulate air in the room and limit the number of customers during the COVID-19 pandemic based on conditions of temperature, humidity, and many people in the storeroom. The actual test results from the implementation of Sugeno fuzzy logic show that the system has good performance in controlling the speed of the exhaust fan and limiting the number of customers based on the level of danger of the potential COVID-19 transmission in the room automatically and can monitor the condition of the room through the Thinger.io website in real time.

Sensor Node Network Monitoring System using RESTful Web Services in Smart Farming Technology Isnanto, Rahmat Fadli; Ubaya, Huda; Asvi, M. Fauzi; Haidar, Rosali; Sari, Purwita
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5220

The agricultural sector in Indonesia heavily depends on optimal environmental conditions. This study proposes a smart farming system based on the Internet of Things (IoT), utilizing RESTful web services as a more flexible and efficient IoT platform alternative. The system is designed for real-time monitoring of various environmental parameters such as temperature, air humidity, and soil conditions, including nitrogen (N), phosphorus (P), potassium (K), electrical conductivity (EC), pH, and soil moisture—captured through a network of sensor nodes. Test results show that the developed RESTful API architecture successfully facilitates effective communication between hardware and software components, enabling flexible data access and analysis through a web-based monitoring interface. This system is expected to assist farmers in making more informed decisions regarding land management, improve agricultural productivity, and support sustainable farming practices.

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

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

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

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

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 (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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

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.

Implementasi Scoreboard Digital untuk Pertandingan Bulutangkis pada Gedung Olahraga Fasilkom Unsri Berbasis Teknologi Wireless Exaudi, Kemahyanto; Sembiring, Sarmayanta; P.P Prasetyo, Aditya; Fadli Isnanto, Rahmat; Ubaya, Huda; Zarkasi, Ahmad; Hermansyah, Adi
Jurnal Pengabdian kepada Masyarakat Bidang Ilmu Komputer Vol 1 No 2 (2023): Jurnal Pengabdian Kepada Masyarakat Bidang Ilmu Komputer (ABDIKOM)
Publisher : Fakultas Ilmu Komputer

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

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 Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Duri, Ades Harafi
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

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.

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