Pengenalan Wajah Menggunakan Local Binary Pattern (Lbp) Dan Support Vector Machine (Svm)

Mujib, Khusnil; Hidayatno, Achmad; Prakoso, Teguh

doi:10.14710/transient.7.1.123-130

Cited by 14 publications

(13 citation statements)

References 2 publications

(2 reference statements)

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“…In the system extract the introduction of the face image, one of the known extraction methods is the Local Binary Pattern (LBP) method this approach by Ojala et al in 1996 (Mujib et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Lecturer Attendance System using Face Recognition Application an Android-Based

Susanto

Fauziah

Andrianingsih

2021

CNAHPC

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In the field of industries, businesses, and offices the use of security systems and administrative management through data input using a face recognition system is being developed. Following the era of technological advances, communication and information systems are widely used in various administrative operational activities and company security systems because it is assessed by using a system that is based on facial recognition security levels and more secure data accuracy, the use of such systems is considered to have its characteristics so it is very difficult for other parties to be able to engineer and manipulate data produced as a tool to support the company's decision. Related to this, causing the author is to try to research the detection of facial recognition that is present in the application system through an Android device, then face recognition detection will be connected. and saved to the database that will be used as data about the presence of teaching lecturers. Using the local binary pattern histogram algorithm method to measure the face recognition system that can be applied as a technique in the attendance system of lecturers to be more effective and efficient. Based on testing by analyzing the false rate error rate and the false refusal rate can be seen that the average level of local binary pattern histogram accuracy reaches 95.71% better than through the Eigenface method which is equal to 76.28%.

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Section: Discussionmentioning

confidence: 99%

Lecturer Attendance System using Face Recognition Application an Android-Based

Susanto

Fauziah

Andrianingsih

2021

CNAHPC

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“…Langkah Kedua Gambar 4. Langkah ketiga Jika koordinat dari piksel pusat adalah ( , ) maka koordinat dari tetangga P ( , ) pada tepi lingkaran dengan jari-jari R dapat ditentukan dengan persamaan (1) dan persamaan (2) [10].…”

Section: Gambar 1 Histogram Equalization[9] B Local Binary Pattern (Lbp)unclassified

“…Perhitungan metode Local Binary Pattern dapat pula di selesaikan dengan persamaan (3) dan persamaan (4) [4], [9] [10]. Local Binary Pattern disebut seragam jika memiliki paling banyak perpindahan dua bitwise dari 0 ke 1, atau sebaliknya, sehingga pola yang seragam memiliki pola tanpa perpindahan atau dua perpindahan.…”

Section: Gambar 1 Histogram Equalization[9] B Local Binary Pattern (Lbp)unclassified

Local Binary Pattern untuk Pengenalan Jenis Daun Tanaman Obat menggunakan K-Nearest Neighbor

Lamasigi¹,

Hasan²,

Lasena³

2020

Ilk. J. Ilm.

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Traditional medicinal plants are types of plants that contain active substances that function to treat and are used by the community to cure or prevent various diseases. Therefore, a study was conducted to test the Local Binary Pattern method for feature extraction of each existing traditional medicinal plant and K-Nearest Neighbor for the classification process after extraction from the Local Binary Pattern method. From testing the Local Binary Pattern and K-Nearest Neighbor methods were able to produce a good accuracy of 96.67%, the accuracy value was obtained from manual convusion matrix calculations with a value of k = 9. Meanwhile, the lowest accuracy results are at k = 1 with an accuracy value of 70%. The extraction and classification results from the Local Binary Pattern and K-Nearest Neighbor methods used 120 datasets which were divided into 90 training data with 6 types of medicinal plant leaves consisting of 15 thorn spinach leaves, 15 binahong leaves, 15 castor leaves, 15 African leaves, and 15 betel leaves with 30 experimental data testing.

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“…Wajah dapat digunakan untuk mengenali seseorang, seperti kebutuhan absensi, pendataan penduduk, dan sistem keamanan, dengan menggunakan sistem pengenalan wajah [2]. Karena memiliki ciri yang berbeda wajah menjadi salah satu sistem keamanan yang sulit ditembus, seperti dari pencahayaan, warna kulit, potongan rambut, kacamata serta posisi wajah yang bebeda yang dalam keadaan menunduk, menoleh atau mengadah [3].…”

Section: Pendahuluanunclassified

Perancangan Sistem Keamanan Brangkas Menggunakan Pengenalan Wajah Berbasis Android

Rama

Fauziah

Nurhayati

2020

mib

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The level of security in terms of access is one of the main priorities of everyone to improve the security system that feels the need for improvement following the development of modern technology. This study discusses a security system using Android-based face recognition. The aim of this research is that the safe safety system has a better level of security than the previous system. The initial stage to build this system, the authors do the literature data collection stage as a basis for the theory and system development methods used by software designers before is the waterfall method, in general this method is divided into several stages, including: Analysis, Design, Program Code and Unit Testing. For the method used in the research of this system is the eigenfaces algorithm method for the detection of facial objects in the initial process of image training. As well as the Local Binary Patterns algorithm method and Histrogram Equalization at the stage of reading the user's face recognition image accurately which has an accuracy of face reading up to 95.56%. The results of the user's face data will be processed in Wemos D1 and the data will be sent and stored in a database. The results of data from face recognition data will be used again as user data to open the safe. The conclusion, the system can read the user's face in real time and can work well for safe security systems

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Pengenalan Wajah Menggunakan Local Binary Pattern (Lbp) Dan Support Vector Machine (Svm)

Cited by 14 publications

References 2 publications

Lecturer Attendance System using Face Recognition Application an Android-Based

Lecturer Attendance System using Face Recognition Application an Android-Based

Local Binary Pattern untuk Pengenalan Jenis Daun Tanaman Obat menggunakan K-Nearest Neighbor

Perancangan Sistem Keamanan Brangkas Menggunakan Pengenalan Wajah Berbasis Android

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