The government through the Ministry of Education and Culture (Kemendikbud) has made a decision to suspend teaching and learning activities in schools. The learning process that starts face to face directly in the classroom turns into distance learning /brave. However, the government decided to reopen schools in the Covid-19 corona virus green zone for teaching and learning activities for students. The opening of special schools in the green zone will be held in mid-July 2020. School openings must be opened with strict health protocols, no updated potential for new Covid-19 clusters in schools. This is a form of application of the "New Normal" that is being adapted to the people of Indonesia. Indonesian people must consider the existence of this corona virus pandemic with new normalcy, such as using a compilation mask outside the home, always using a hand compass tool and using a loudspeaker and distance measuring device. The purpose of this study was to make an automated hand sanitizer design as an effort to improve the delivery of Covid-19 in schools. Automatic hand sanitizer is useful to facilitate the hand sanitizer liquid out of the bottle, so it is more effective to use and does not run out quickly. This study uses an Arduino Nano microcontroller as the main control, a human hand detection sensor, and a servo motor as an actuator that will activate the automatic bottle. The mouth of the hand sanitizer bottle uses an elastic hose that leads to the part where the cleaning liquid comes out. This research uses the Research and Development (RnD) method. The result of this research is an automatic hand sanitizer with a large size hand sanitizer that can be mounted into a tool with a maximum of 500 ml. This automatic hand sanitizer will automatically release the hand sanitizer fluid which approves the sensor under the user's hand protective device.
Internet of Things (IoT) networks leverage wireless communication protocols, which adversaries can exploit. Impersonation attacks, injection attacks, and flooding are several examples of different attacks existing in Wi-Fi networks. Intrusion Detection System (IDS) became one solution to distinguish those attacks from benign traffic. Deep learning techniques have been intensively utilized to classify the attacks. However, the main issue of utilizing deep learning models is projecting the data, notably tabular data, into an image. This study proposes a novel projection from wireless network attacks data into a grid-based image for feeding one of the Convolutional Neural Network (CNN) models, EfficientNet. We define the particular sequence of placing the attribute values in a grid that would be captured as an image. Combining the most important subset of attributes and EfficientNet, we aim for an accurate and lightweight IDS module deployed in IoT networks. We examine the proposed model using the Wi-Fi attacks dataset, called the AWID2 dataset. We achieve the best performance by a 99.91% F1 score and 0.11% false-positive rate. In addition, our proposed model achieved comparable results with other statistical machine learning models, which shows that our proposed model successfully exploited the spatial information of tabular data to maintain detection accuracy.
Face is one of the elements used to identify identity between humans. The purpose of making this thesis is as a basic basis for developing an attendance system and making artificial intelligence that can identify humans through their faces. How to do data processing, the data taken comes from a video of office employees which lasts approximately 10 seconds. To make a program that can recognize the faces of office employees, the Convolutional Neural Network (CNN) method is used which will be trained to be able to distinguish each unique feature on the face to distinguish and recognize humans specifically. In performing facial recognition, office employees can provide input in the form of facial photos of office employees who have been trained and use the camera on a smartphone to perform face recognition directly. The faces of office employees used as targets for this CNN training came from Pt Eternal Indonesia, Faculty of Information Technology, Tarumanagara University, and Kekar Clinic. The output of the application is the accuracy of each photo of the office employee's face given. The results of the confusion matrix test show that the trained model has an accuracy of 80.39%, a precision of 80%, a recall of 80%, and an f1-score of 80%.
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