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The present advances in technology has enabled us to exchange messages easily with many parties especially via computer networks. However important messages need to be secured to avoid exposure to unintended recipients. Cryptography, which one of the purposes is confidentiality, may serve to secure these messages so that the content may not be exposed to unintended parties. Another aspect to be considered is the size of the message. A small message is easier to transmit and to store, and to this end is where compression comes into play. This study utilized the Spritz algorithm to achieve cryptographic confidentiality and Variable Length Binary Encoding as the compression algorithm to reduce data size. The algorithms were implemented using SharpDevelop IDE and was tested in a Local Area Network environment. The average times for encryption process was 41 ms, while for compression was 38 ms. Decompression process on the average required 673 ms, while decryption took 49 ms on average. As for other parameters such as Compression Ratio the average was 91% while the average of Space Savings was 9%. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common causes of death according to WHO in 2020. One of the ways to detect lung cancer is through X-rays. Because the examination is generally carried out manually through the naked eye of experts, it is advisable to have an alternative to detect the disease as early as possible so that the patient can get the right treatment. This study aims to detect the presence of lung cancer from chest x-ray images with the application of deep learning, namely Convolutional Neural Network (CNN). In this study, the authors used 90 cancer images and 90 normal images. The research design stage consisted of pre-processing on x-ray images and identification using CNN. The research obtained an accuracy rate of 87%. [ABSTRACT FROM AUTHOR]

Gastritis is a disease that occurs in the stomach and can be dangerous if not treated quickly. Gastritis can affect anyone, young or old. Therefore, a system is needed to identify early gastritis identification so that it can help the community to find out early on their gastritis. The identification method used in this study is the Radial Basis Function Neural Network with several image processing techniques. Tongue image is used as input image for image processing. Prior to the initial identification, the image preprocessing process is carried out, namely cropping, resizing, brightness, image feature extraction using color extraction using Hue Saturation Value (HSV). The dataset used in this study are limited to 150 tongue images and resulted in the ability to identify early gastritis with an accuracy of 93.3% for 50 testing images. [ABSTRACT FROM AUTHOR]

The role of a microcontroller-based robot has been carried out, which is a tool that basically serves to facilitate human work. The trash can collection robot using color sensors can be used by its users, namely the community to be able to help provide convenience to the cleanliness of the environment. The program used in the robot uses the C language that is processed in the CodeVision AVR application. The steps for completing the application in this study are designing programs, creating programs, conducting testing of robots, repairs and finally the process of improving the program. The program can be implemented on the condition that there is hardware that will be the output of the overall research results. With the successful design of the trash can Collection Robot using this color sensor, it can be used as a guide for technology users to add knowledge and participate in continuing the innovation of creating new useful technologies. [ABSTRACT FROM AUTHOR]