Wetland landscape based on Sentinel-2 images and geo-tagged photographs in Centla, Tabasco

This is an analysis of the geographic landscape in the Centla wetlands of Tabasco, Mexico. A map shows the use of remote sensing data combined with easily understood and conveyed visual descriptive data which show the ecological conditions of the landscape. The central map of this article presents a land use and land cover study, obtained from Sentinel-2 MSI data for the Centla wetland zones. The support vector machine algorithm is used to classify Sentinel-2 images. The results show a high general precision of 90.0%, as well as high precision in separating types of wetlands. Information obtained during fieldwork at the ground level is inserted in the map, comprised of photos taken with a mobile application along the Grijalva and the San Pedro y San Pablo rivers. These photos provide visual verification of the map.

PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86

Method of Analyzing and Managing Volleyball Action by Using Action Sensor of Mobile Device

With the development of electronic technology and sensor technology, more and more intelligent electronic devices integrate micro inertial sensors, which makes the research of human action recognition based on action sensing data have great application value. Data-based action recognition is a new research direction in the field of pattern recognition, which is essentially a process of action data acquisition, feature extraction, feature extraction, and recognition, the process of classification and recognition. Inertial motion information includes acceleration and angular velocity information, which is ubiquitous in daily life. Compared with motion recognition based on visual information, it can more directly reflect the meaning of action. This study mainly discusses the method of analyzing and managing volleyball action by using the action sensor of mobile device. Based on the motion recognition algorithm of support vector machine, the motion recognition process of support vector machine is constructed. When the data terminal and gateway of volleyball players are not in the same LAN, the classification algorithm classifies the samples to be tested through the characteristic data, which directly affects the recognition results. In this paper, the support vector machine algorithm is selected as the data classification algorithm, and the calculation of the classification process is reduced by designing an appropriate kernel function. For multiclass problems, the hierarchical structure of directed acyclic graph is optimized to improve the recognition rate. We need to bind motion sensors to human joints. In order to realize real-time recognition of human motion, mobile devices need to add windows to the motion capture data, that is, divide the data into a small sequence of specified length, and provide more application scenarios for the device. This method of embedding motion sensors into devices to read motion information is widely used, which provides a convenient data acquisition method for human motion pattern recognition based on motion information. The multiclassification support vector machine algorithm is used to train the classification algorithm model with action data. When the signal strength of the sensor is 90 t and the speed is 2.0 m/s and 0.5 m/s, the detection accuracy of the adaptive threshold is 93% and 95%, respectively. The results show that the SVM method based on hybrid kernel function can greatly improve the recognition accuracy of volleyball stroke, and the recognition time is short.