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All Journal Jurnal Informatika Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Terpadu
Pane, Yeremia Yosefan
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Computer Science & IT Engineering

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Klasifikasi Jenis Burung menggunakan Metode Transfer Learning Pane, Yeremia Yosefan; Sihombing, Jeremia Jordan
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.744

Abstract

Indonesia is known for its abundant natural resources, including its diverse bird fauna. The identification and classification of bird species is essential in maintaining biodiversity as well as for practical habitat management. Therefore, an efficient and accurate approach is needed to identify bird species. This study uses a deep learning approach to test and compare the MobileNetV2 architecture with architectures used in previous studies in recognizing bird species. We use a transfer learning approach that utilizes existing knowledge from pre-trained models and combines it with a Convolutional Neural Network (CNN) algorithm to detect and classify birds based on images with a total image data of 95376. Experimental results show that by using the MobileNetV2 architecture, we achieved an accuracy of 96.4% with a loss value of 0.241. Compared with the architecture used in previous research, our results show a significant improvement in accuracy and efficiency. The time taken to perform the classification at each step is about 646 ms. This study shows that using MobileNetV2 architecture in the transfer learning approach with CNN effectively performs bird species classification.
Implementation of You Only Look Once Version 8 Algorithm to Detect Multi-Face Drivers and Vehicle Plates Saputra S, Kana; Taufik, Insan; Ramadhani, Irham; Siregar, Angginy Akhirunnisa; Pinem, Josua; Lubis, Afiq Alghazali; Pane, Yeremia Yosefan; Putri, Rezkya Nadilla
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.22026

Abstract

Checking the identity of motorcycle owners when leaving the college area is a mandatory activity for security officers to ensure that vehicles entering and exiting the college are the same driver. The conventional checking process often causes the impact of vehicle queues when the volume of vehicles increases. Therefore, an intelligent system is needed to detect multi-plate vehicles automatically. One approach in the world of image detection of an object is the use of the YOLO (You Only Look Once) algorithm. This algorithm predicts bounding boxes and possible classes in a single frame. This research divides objects into 3 classes, namely vehicles, driver's faces, and vehicle plates. The dataset used was 74 varied images consisting of 50 training data, 12 validation data and 12 testing data. The image was trained using 300 epochs and a batch size of 8 and resulted in an F1 score calculation for detecting objects reaching 92%.
Motorcycle License Plate and Driver Face Verification Using Siamese Neural Network Model Pane, Yeremia Yosefan; S, Kana Saputra; Al Idrus, Said Iskandar; Syahputra, Hermawan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.31750

Abstract

The security and efficiency of vehicle access management systems have become a primary concern for various institutions, including universities, offices, and public facilities. Effective access management not only enhances security but also improves the flow of incoming and outgoing vehicles, reduces congestion, and enhances user experience. This research aims to develop a vehicle plate detection system and driver face recognition using the Siamese Neural Network model to optimize traffic at the gate. The methods used include the application of deep learning algorithms, specifically the Siamese Neural Network, to verify the driver's face and the use of You Only Live Once (YOLO) to detect and recognize vehicle plates in real-time. Data was collected through direct capture with the researcher's camera. The model was trained and tested using a dataset containing images of vehicle license plates and driver faces. The results showed that the developed model was able to detect and recognize the vehicle plate and the driver's face with a fairly high accuracy, namely in the object detection results getting bounding box validation is 1.05 and class loss validation is 0.95, and 0.85 mAP. As well as in training using the Siamese Neural Network, the highest result is 0.82 with a learning rate of 10e-5 with 30 epochs. It is hoped that this system can be one of the innovations that can be applied in government agencies, universities, industries, etc.
Co-Authors Hermawan Syahputra Insan Taufik Irham Ramadhani Kana Saputra S Lubis, Afiq Alghazali Pinem, Josua Putri, Rezkya Nadilla Said . Iskandar Sihombing, Jeremia Jordan Siregar, Angginy Akhirunnisa