Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur Pada Gambar Wajah Berkualitas Rendah

Wildah, Siti Khotimatul; Agustiani, Sarifah; Mustopa, Ali; Wuryani, Nanik; Nawawi, Hendri Mahmud; Safitri, Rizky Ade

doi:10.37373/infotech.v2i2.189

Cited by 3 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…LBPH dapat mengenali citra dengan berbagai ekspresi wajah, pencerahan cahaya dalam berbagai kondisi, dan rotasi gambar. Metode LBPH akan menghasilkan tekstur dan bentuk citra digital dari fitur yang sudah diektraksi dengan cara membagi citra ke bagian yang lebih kecil [5].…”

Section: Lbph (Local Binary Pattern Histogram)unclassified

Identifikasi Pengenalan Wajah Untuk Sistem Presensi Menggunakan Metode KNN (K-Nearest Neighbor)

Yulianti¹,

Triastomoro²,

Saidah³

2022

Tekinkom

View full text Add to dashboard Cite

Attendance is an activity that is so important and cannot be separated from a teaching and learning activity to calculate and see student attendance. In this Final Project, research on the automatic attendance system is carried out through facial recognition identification (face recognition) using a webcam as a system input, then the resulting image capture results from each image will be processed through feature extraction using the LBPH (Local Binary Pattern Histogram) method and classification with the KNN (K-Nearest Neighbor) method and the help of OpenCV library-based Python software. The research in this Final Project obtained an average accuracy value in facial recognition using LBPH (Local Binary Pattern Histogram) of 93.9%, with an average FAR value of 4.66% and an average FRR value of 1.33%. For the classification of KNN (K-Nearest Neighbor) using Euclidean Distance when k = 1 obtained an accuracy of 100% with a computation time of 34 ms, at the time of k = 3 an accuracy of 98% with a computation time of 37 ms was obtained and at the time of k = 5 an accuracy of 88% with a computation time of 42 ms.

show abstract

Section: Lbph (Local Binary Pattern Histogram)unclassified

Identifikasi Pengenalan Wajah Untuk Sistem Presensi Menggunakan Metode KNN (K-Nearest Neighbor)

Yulianti¹,

Triastomoro²,

Saidah³

2022

Tekinkom

View full text Add to dashboard Cite

show abstract

“…Moh Ariei Hasan has research categorizing diseases using the CNN approach on photos of grape leaves. 4,000 photos were used in the test, of which 4,000 were divided into 2800 for training purposes, 800 for validation purposes, and 400 for testing purposes [5]. The accuracy of the test was 99.50% during training and 97.25% during testing [6].…”

Section: Introductionmentioning

confidence: 99%

Detection of mango leaf disease using the convolution neural network method

Saragih,

Nur Azizi,

Alfattah Atalarais

et al. 2024

tekno

View full text Add to dashboard Cite

Mango trees are a common plant in Indonesia. In 2021, 2.84 million tonnes of mangoes were produced in Indonesia, according to the Central Statistics Agency (BPS). You can eat mangoes either ripe or unripe. Additionally, this fruit can be turned into meals and beverages. Many farmers grow mango plants, however, Some illnesses can infect mangoes and lead to crop failure or poor fruit quality. Fungal infections like anthracnose and black fungus, often known as black fungus, affect mango plants, but many farmers continue to mistakenly believe they are identifying plant illnesses and pests. To the type of disease in mango plants, the Convolutional Neural Network (CNN) method was applied in this study. It has been demonstrated that CNN is a very efficient way of processing images and identifying key elements for intricate pattern recognition. A total of 1,405 leaf photos from three different categories—525 anthracnose images, 656 black sooty mold images, and 224 healthy images—were used as samples for CNN to identify illnesses in mango plants. This image data was taken from the kaggle.com website. The CNN model is trained using distinct datasets into training data and validation data after data collection and preprocessing. On training data, the model is 95% accurate, while on validation data, it is 98% accurate. By feeding photos of mango leaves into the model and evaluating the predictions, the detection is put into practice. Action can be taken to control the illness in these mango trees based on prediction findings showing the presence of disease with a decent amount of confidence

show abstract

System of student attendance with IoT-based face identification using Raspberry Pi

Renaldi,

Sukirman

2023

CHEMISTRY BEYOND BORDERS: INTERNATIONAL CONFERENCE ON PHYSICAL CHEMISTRY: The 1st Annual Meeting of the Physical Chemistry Divi

View full text Add to dashboard Cite

Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur Pada Gambar Wajah Berkualitas Rendah

Cited by 3 publications

References 8 publications

Identifikasi Pengenalan Wajah Untuk Sistem Presensi Menggunakan Metode KNN (K-Nearest Neighbor)

Identifikasi Pengenalan Wajah Untuk Sistem Presensi Menggunakan Metode KNN (K-Nearest Neighbor)

Detection of mango leaf disease using the convolution neural network method

System of student attendance with IoT-based face identification using Raspberry Pi

Contact Info

Product

Resources

About