Implementasi Raspberry Pi Untuk Rancang Bangun Sistem Keamanan Pintu Ruang Server Dengan Pengenalan Wajah Menggunakan Metode Triangle  Face

Wijaya, Indra Dharma; Nurhasan, Usman; Barata, Mula Agung

doi:10.33795/jip.v4i1.138

Cited by 10 publications

(9 citation statements)

References 0 publications

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“…This research was conducted to meet the needs of a server room security system that is easy to apply and inexpensive in terms of cost and maintenance. The results of this research detection system has an accuracy of 75% [4]. Face Recognition using the Haar-Cascade method which is a face detection system will be applied to the Raspberry Pi which is a Single-Board Circuit.…”

Section: Introductionmentioning

confidence: 93%

Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI

Wibowo,

Rozaq,

Yusva

2024

protk

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Throughout his life, humans have the ability to recognize tens to hundreds of faces. One of the biometric techniques that relate body measurements and calculations that are directly related to human characteristics is a system that can detect and identify faces. To be able to overcome various current problems, facial recognition is required through computer applications, including identification of criminals, development of security systems, image and film processing, and human-computer interaction. So the author makes a face processing system based on Raspberry Pi with the Local Binary Patterns Histogram (LBPH) method. In running a facial recognition system using a face, at the initial stage the process of sampling the face of the person who is the owner of the room access is carried out. Then from the face samples that have been obtained, the learning process is carried out by converting the image into digital values through the Local Binary Patterns Histogram method. This method reduces image data into simpler data, to speed up the face recognition process. The results of the test show that face recognition works as expected, even being able to detect at low light brightness values (≥6 lux) and even face recognition accuracy of 79.15%. For face data that has been through the learning process, the face can be recognized, then the recognized face data is stored in a directory.

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Section: Introductionmentioning

confidence: 93%

Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI

Wibowo,

Rozaq,

Yusva

2024

protk

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“…Webcam juga dilengkapi dengan kabel dukungan yang fleksibel, yang satu ujungnya terhubung ke papan sirkuit dan ujung lainnya memiliki konektor. Kabel dukungan ini dapat dikendalikan untuk menyesuaikan ketinggian, arah, dan sudut pandang webcam [17].…”

Section: Kamera Webcamunclassified

IMPLEMENTASI RASPBERRY PI DALAM ALAT KLASIFIKASI PENYAKIT MATA DENGAN ARSITEKTUR YOLOv8 MENGGUNAKAN OFTALMOSKOP

Kukuh Isnaen,

Stefanie

2023

jati

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Dampak negatif yang diterima oleh mata karena paparan ultraviolet secara langsung dari perangkat elektronik akan menyebabkan penyakit pada mata, seperti katarak, glaukoma, dan penyakit pada retina. Apabila tidak segera ditangani, penyakit mata dapat menyebabkan kebutaan. Jika tidak ditangani, penyakit mata dapat menyebabkan kebutaan. Alat ini dirancang dengan tujuan untuk memudahkan masyarakat dalam melakukan pemeriksaan mata secara rutin dan mendeteksi secara dini adanya gangguan mata, sehingga memungkinkan segera dilakukannya tindakan medis oleh dokter untuk mengatasi masalah mata yang dihadapi oleh masyarakat. Pengujian ini akan di implementasikan ke gambar citra fundus mata untuk diklasifikasi matanya. Dalam pemanfaatan AI dapat diterapkan di dunia kesehatan Khususnya kesehatan mata yang bertujuan membantu klasifikasi dini penyakit pada mata untuk meningkatkan kualitas hidup masyarakat dan menciptakan alat diagnosis penyakit mata otomatis menggunakan raspberry pi dan kamera webcam dengan arsitektur YOLOv8. Tujuan pengujian alat ini untuk melihat keefektifan alat yang sudah dirancang dan mendapatkan data yang dibutuhkan. Pada percobaan pertama mengklasifikasi mata katarak menghasilkan akurasi 100%. Pada percobaan kedua mengklasifikasi mata glaukoma menghasilkan akurasi 99%. Pada percobaan ketiga mengklasifikasi mata diabetic retinopathy menghasilkan akurasi 78%. Pada percobaan keempat mengklasifikasi mata normal menghasilkan akurasi 77%.

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“…The key used is Magnetic Doorlock, the advantage lies in this lock mechanism. This key utilizes electric current to create a magnetic force that is the main restraint so that this key will not be dangerous if something undesirable happens, such as fire, power outage, or other disasters, because this key will automatically open when there is no electric current [9][10][11][12].…”

Section: Microcontroller Module Selectionmentioning

confidence: 99%

Automatic Door Access Model Based on Face Recognition using Convolutional Neural Network

Zuraiyah,

Maryana,

Kohar

2022

matrik

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Automatic door access technology by utilizing biometrics such as fingerprints, retinas and facial structures is constantly evolving. The use of masks during the Covid-19 Pandemic and post-pandemic has become an obligation wherever humans are active. The study aimed to create an automated door access model using Convolutional Neural Network (CNN) algorithms and Amazon Rekognition as cloud-based software. The CNN algorithm is applied to classify faces wearing masks or not wearing masks. The CNN architecture model uses sequential, convolution2D, max polling 2D, flatten dan dense. The hardware includes the Raspberry Pi, USB Webcam, Relay, and Magnetic Doorlock. The test results were obtained from the results of the accuracy plot on the Convolutional Neural Network model with an accuracy rate of 99% at an epoch value of 8 with a learning time of 67 seconds.

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Implementasi Raspberry Pi Untuk Rancang Bangun Sistem Keamanan Pintu Ruang Server Dengan Pengenalan Wajah Menggunakan Metode Triangle Face

Cited by 10 publications

References 0 publications

Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI

Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI

IMPLEMENTASI RASPBERRY PI DALAM ALAT KLASIFIKASI PENYAKIT MATA DENGAN ARSITEKTUR YOLOv8 MENGGUNAKAN OFTALMOSKOP

Automatic Door Access Model Based on Face Recognition using Convolutional Neural Network

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