PROTOTYPE APPLICATION OF CROWD DETECTION SYSTEM FOR TRADITIONAL MARKET VISITOR BASED ON IOT USING RFID MFRC522

Crowds of people are the government's concern in dealing with the COVID-19 pandemic because the virus transfers unwittingly from one person to another and transmits it to the closest environment. One of the locations where crowds are difficult to avoid is a traditional market and is thought to be one of the places that have the potential to become the center of the spread of COVID-19. Various efforts made by the government in suppressing crowds have yielded results, but not a few violations that occur are carried out intentionally or unintentionally, one of the efforts to prevent crowd violations is the traditional market visitor detection monitoring system by market management so that market visitors do not violate health protocols and crowds that occur in an area can be avoided. In this study, an IoT-based crowd detection system application prototype uses an RFID sensor MFRC522 as a crowd indicator based on data on the number of visitors entering a kiosk that is recorded in the database and then displayed on the application, this data becomes an indicator of which kiosk other visitors want to go to so that the crowd can be avoided. System functionality testing was carried out with 4 scenarios and system reliability testing through data transmission was carried out 10 times with test data in the form of kiosk id and visitor id sent via a single Transmission Control Protocol (TCP) with a full-duplex communication channel. The test results show that crowd indications can be detected in the application with data transmission speeds reaching 875 KB/s with an average delay of 231.4 ms and a standard deviation of 215 ± 313 ms

End-To-End Evaluation of Deep Learning Architectures for Off-Line Handwriting Writer Identification: A Comparative Study

Identifying writers using their handwriting is particularly challenging for a machine, given that a person’s writing can serve as their distinguishing characteristic. The process of identification using handcrafted features has shown promising results, but the intra-class variability between authors still needs further development. Almost all computer vision-related tasks use Deep learning (DL) nowadays, and as a result, researchers are developing many DL architectures with their respective methods. In addition, feature extraction, usually accomplished using handcrafted algorithms, can now be automatically conducted using convolutional neural networks. With the various developments of the DL method, it is necessary to evaluate the suitable DL for the problem we are aiming at, namely the classification of writer identification. This comparative study evaluated several DL architectures such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet end-to-end to examine their advantages to offline handwriting for writer identification problems with IAM and CVL databases. Each architecture compared its respective process to the training and validation metrics accuracy, demonstrating that ResNet50 DL had the highest train accuracy of 98.86%. However, Xception DL performed slightly better due to the convergence gap for validation accuracy compared to all the other architectures, which were 21.79% and 15.12% for IAM and CVL. Also, the smallest gap of convergence between training and validation accuracy for the IAM and CVL datasets were 19.13% and 16.49%, respectively. The results of these findings serve as the basis for DL architecture selection and open up overfitting problems for future work