Garuda - Garba Rujukan Digital

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.

Deteksi Wajah Tersamar Menggunakan Metode VGGFace dan SVM Siswanti, Sri Desy; Puspita, Heni; Ubaya, Huda; Selly, Selly; Herdiana, Dina
REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer Vol. 9 No. 2 (2025): Volume 9 Nomor 2 April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/remik.v9i2.14620

Wajah adalah salah satu bagian dari manusia yang memiliki ciri-ciri berbeda. Teknologi pengenalan wajah merupakan suatu teknologi yang dapat mengidentifikasi atau memverifikasi seseorang dari sebuah gambar atau video. Teknologi pengenalan wajah bermanfaat untuk bidang keamanan, pengawasan, verifikasi identitas umum, sistem peradilan pidana, investigasi basis data gambar. Mungkin saja seorang DPO menggunakan penyamaran, baik secara sengaja maupun tidak sengaja, untuk menyembunyikan diri atau berpura-pura menjadi orang lain, misalnya menggunakan jenggot, kumis, dan gaya rambut yang diubah yang menyebabkan kebingungan dalam mengenali orang. Selain itu, aksesori penyamaran seperti wig, topi, syal, helm, kerudung, kacamata hitam, atau masker dapat membuat bagian wajah terlihat berbeda. Riasan tebal atau prosedur eksternal seperti operasi plastik juga dapat mengubah bentuk, tekstur, dan warna wajah, sehingga menyulitkan mengenali seseorang. Dalam makalah ini, mengusulkan sebuah algoritma pengenalan wajah tersamar,dimana algoritma ini mengubah arsitektur VGG pada tahap klasifikasi. Perubahan ini mencakup penambahan lapisan flatten yang disatukan dengan metode SVM. Tujuan dari modifikasi ini adalah untuk meningkatkan nilai akurasi dalam pengenalan wajah tersamar. Dalam penelitian ini memanfaatkan arsitektur VGG untuk ekstraksi fitur, SVM digunakan sebagai metode klasifikasi dalam pengenalan wajah. Sistem pengenalan wajah yang dikembangkan terdiri dari empat tahap utama: pengambilan data, pengolahan data, ekstraksi fitur, dan klasifikasi. Data wajah diambil secara langsung di depan kamera berupa wajah tanpa tersamar dan wajah tersamar dengan lima posisi wajah yaitu wajah menghadap ke kanan, ke kiri, ke depan,ke atas dan ke bawah. Sistem ini diimplementasikan menggunakan library Keras, Sklearn, dan Numpy untuk mengolah data. Untuk meningkatkan nilai akurasi diperlukan pengaturan parameter dari klasifikasi SVM yaitu Cost (C) dan gamma (ℽ). Hasil dari pengujian menunjukkan bahwa metode yang diterapkan dalam sistem pengenalan wajah tersamar ini menghasilkan nilai akurasi yang lebih baik dibandingkan dengan penelitian yang lain, walaupun masih ada beberapa kekurangan dari metode yang diterapkan dalam penelitian ini

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 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.

Title

Found 9 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 9 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 9 Documents
Search