Hand gesture recognition is a topic that is still investigated by many scientists for numerous useful aspects. This research investigated hand gestures for sign language number zero to nine. The hand gesture recognition was based on finger direction patterns. The finger directions were detected by a Leap Motion Controller. Finger direction pattern modeling was based on two methods: threshold and artificial neural network. Threshold model 1 contained 15 rules based on the range of finger directions on each axis. Threshold model 2 was developed from model 1 based on the behavior of finger movements when the subject performed hand gestures. The ANN model of the system was designed with four neurons at the output layer, 15 neurons at the input layer, seven neurons at the first hidden layer and 5 neurons at the second hidden layer. The artificial neural network used the logsig as the activation function. The result shows that the first threshold model has the lowest accuracy because the rule is too complicated and rigid. The threshold model 2 can improve the threshold model, but it still needs development to reach better accuracy. The ANN model gave the best result among the developed model with 98% accuracy. LMC produces useful biometric data for hand gesture recognition.

Abstract— Padang City is prone to liquefaction phenomena due to earthquakes. These phenomena can cause various damages to structures, infrastructures, and even can also cause deaths. Therefore, as one of the urban populated cities, the information about liquefaction potential is needed. One of them is by providing a liquefaction potential map, which is useful for mitigation and seismic disaster risks strategies. This article aims to provide a digital map of liquefaction potential in Padang City that integrates with Google Maps. The map is based on 40 coordinates in 7 subdistricts in the city with 3 colored markers that represent the levels of potential liquefaction i.e. no liquefaction level, moderate liquefaction level, and severe liquefaction level. The levels are classified based on the analysis of the secondary Cone Penetration Test data by using the calculation of the Factor of Safety and Liquefaction Potential Index with an earthquake assumption of 8 SR. The result shows that the map has ben able to display information about liquefaction potential, where 32.05% coordinates are classified as no liquefaction level with the highest percentage are in Kuranji, 22.5% are classified as moderate liquefaction level with the highest percentage are in Padang Utara, and 45.0% are classified as severe liquefaction level with the highest percentage are in Koto Tangah.

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Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character.

At this time, the application development process has experienced much development in terms of tools and the programming language used. The application development process is required to be carried out in a fast process using various existing tools. The application development and delivery process can be done quickly using Continuous Integration (CI) and Continuous Delivery (CD). This study uses the CI/CD technique to develop real-time applications using various programming languages implemented on a cloud infrastructure using the AWS codepipeline, which focuses on automatic deployment. Application source code is stored on different media using GitHub and Amazon S3. The source code will be tested for automatic deployment using the AWS code pipeline. The results of this study show that all programming languages can be appropriately deployed with an average time of 60 seconds