Persons with disabilities also have the right to communicate between each other, both with normal people and people with other disabilities. People with disabilities will be difficult to communicate with other people. They use 'sign language' to communicate. That's why other normal people will be difficult to communicate with them. Because there are not many normal people that can understand the 'sign language'. The system can help to communicate with disabilities people are needed. In this paper, we proposed sign language recognition for Sistem Isyarat Bahasa Indonesia (SIBI) using leap motion based on K-Nearest Neighbor. Technology of leap motion controller will generate the existence of coordinate points on each bone in hand. As an input, we used the value of distance between the coordinates of each bone distal to the position of the palm, which were measured using Euclidean Distance. This feature of distance will be used for training and testing data on K-Nearest Neighbor method. The experiment result shows that the best accuracy is 0,78 and error 0,22 with proposed parameter of K = 5.
Persons with disabilities also have the right to communicate between each other, both with normal people and people with other disabilities. People with disabilities will be difficult to communicate with other people. They use 'sign language' to communicate. That's why other normal people will be difficult to communicate with them. Because there are not many normal people that can understand the 'sign language'. The system can help to communicate with disabilities people are needed. In this paper, we proposed sign language recognition for Sistem Isyarat Bahasa Indonesia (SIBI) using leap motion based on K-Nearest Neighbor. Technology of leap motion controller will generate the existence of coordinate points on each bone in hand. As an input, we used the value of distance between the coordinates of each bone distal to the position of the palm, which were measured using Euclidean Distance. This feature of distance will be used for training and testing data on K-Nearest Neighbor method. The experiment result shows that the best accuracy is 0,78 and error 0,22 with proposed parameter of K = 5.
Over the past few years, people have been able to get and share information through social media easily. Some of that information can be a false issue created by a buzzer account that intends to influence people into a specific opinion. Politicians often use social media to maintain a good image in society by utilizing buzzer accounts. The main characteristic of a buzzer account is that they upload the same content repeatedly within a certain period. Before analyzing data taken from social media such as Twitter, we need a buzzer detection system to filter data from buzzer users. This research attempts to build a buzzer detection system using text processing and classification method. We use the similarity of tweets as a feature for the buzzer detection system by applying Cosine Similarity to the Term Frequency - Inverse Document Frequency (TF-IDF) feature of the tweets. In addition, we will use other features such as the number of followers, number of followings, the intensity of tweets, the ratio of retweets, and the ratio of tweets that contain links as additional features in this study. This research uses these features as inputs to the Support Vector Machine model to determine whether an account is a buzzer or not. This system has promising results by having 89% accuracy, 86.67% precision, 70.91 % recall, and 78% F1-score.
Abstract— Hybrid energy generation is a renewable energy power plant with more efficiency and advantages than a stand-alone generator. Using 2 generators for battry storage can speed up the charging of energy to the battery but in the battery charging system, there is still wasted energy and an uncontrolled Charging and Discharging system on the battery resulting in a short battery life. Therefore we need a tool to control the Charging and Discharging performance of the battery in order to optimize battery charging thereby extending battery life.Automatic Switching System is an innovation of Charging and Discharging activity management so that it does not occur simultaneously on the battery, this process is carried out by a microcontroller. The use of 2 DC power sources as a charging source helps speed up battery charging for 30 minutes faster than 1 source. Through several modes provided, this tool can select sources and batteries that can be used as a condition for Charging and Discharging.
CAPTCHA refer to Completely Automated Public Turing test to tell Computers and Humans Apart. CAPTCHA are used to ensure that the operators are human not robots. The basic idea of using CAPTCHA is segmentation and recognition. Random characters, graphic images, or CAPTCHA audio become possible solutions to improve security and resilience for protection systems. In this paper used CAPTCHA random characters. However the CAPTCHA text needs to be analyzed again whether it is still solved by the computer or not it needs to be analyzed, improved, and developed to avoid automatic interference. Data set of text CAPTCHA paypal or so-called paypal HIP with 20 pieces of training data to get the template as much as 36 images that is from the numbers 0-9 and the letter A-Z. This particular paypal HIP data is limited by not using numbers 0 and 1 with the letters O and Q because of the similarity between the data. The method used starts from pre-processing, segmentation, and classification. Pre-processing techniques used consist of removing noise by tresholding and using cleaning techniques. We use bounding box and padding for segmentation method. And then for classification used counting pixel, vertical projections, horizontal projections, dan template correlation. By using these methods will be known which method can recognize CAPTCHA text accurately so as to affect the robustness of the CAPTCHA text.
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