THE EFFECTIVENESS OF ONLINE CLASSES DURING THE COVID-19 PANDEMIC (Case Study : Students Of The Management Study Program Of Persada Bunda)

Online classes are the main means of lecturing process during the Covid-19 Pandemic. No exception for the management economics Department of Persada Bunda College Pekanbaru which utilizes online applications, such as Zoom, Whatsapp groups, Google classrooms, and other application media to carry out the online class process. This research was conducted to find out what the effectiveness of online classes were like for the Management Study Program students of the Persada Bunda Economic College Pekanbaru during the Covid-19 Pandemic. This research is a quantitative descriptive study that brought an online survey method via google form. The obtained test results were that the majority of students from the Management Study Program of the Persada Bunda Pekanbaru have carried out the online class process from home using a cellphone / smartphone by utilizing a fair internet data connection. The online class process shows a general picture that the students' understanding towards the material provided was less than optimal and the increasing number of assignments given to students had an impact on the class process which was less effective. Other results show that students are not ready to face the new rule set by the government, "the new normal life" if online classes continue. Another result also show that the effective lecturing system during the Covid-19 pandemic should be both online and offline that are carried out alternately while still paying attention to health protocols in order to prevent Covid-19 from spreading.

Deep Neural Network Algorithm Feedback Model with Behavioral Intelligence and Forecast Accuracy

When attempting to apply a large-scale database that holds the behavioral intelligence training data of deep neural networks, the classification accuracy of the artificial intelligence algorithm needs to reflect the behavioral characteristics of the individual. When a change in behavior is recognized, that is, a feedback model based on a data connection model is applied, an analysis of time series data is performed by extracting feature vectors and interpolating data in a deep neural network to overcome the limitations of the existing statistical analysis. Using the results of the first feedback model as inputs to the deep neural network and, furthermore, as the input values of the second feedback model, and interpolating the behavioral intelligence data, that is, context awareness and lifelog data, including physical activities, involves applying the most appropriate conditions. The results of this study show that this method effectively improves the accuracy of the artificial intelligence results. In this paper, through an experiment, after extracting the feature vector of a deep neural network and restoring the missing value, the classification accuracy was verified to improve by about 20% on average. At the same time, by adding behavioral intelligence data to the time series data, a new data connection model, the Deep Neural Network Feedback Model, was proposed, and it was verified that the classification accuracy can be improved by about 8 to 9% on average. Based on the hypothesis, the F (X′) = X model was applied to thoroughly classify the training data set and test data set to present a symmetrical balance between the data connection model and the context-aware data. In addition, behavioral activity data were extrapolated in terms of context-aware and forecasting perspectives to prove the results of the experiment.

Designing Application For Defect Recording and Handover Of Property Based On Mobile Application by Applying SQLite Technology

Property development companies that produce housing products, high rise dwellings, and office buildings generally have data on the quality of buildings, one of which is obtained during the defect inspection process between developers and consumers before handing over units. Recording data is generally still done manually using a form on a paper. For these conditions, researchers tried to build an application based on mobile apps to digitally record the defect checklist of the dwelling so that the data collected can be processed for the needs of analysis and development strategies. Difficulties encountered during the unit handover process using digital methods on the newly completed property, one of which is the quality of data and internet signals. Mobile apps certainly require a data signal connection to send data to the server. This Android-based mobile apps will implement SQLite technology which allows the recording of transactions to be done locally first, which can then be synchronized into the database server after getting the required internet data connection. SQLite was chosen because it has a relatively small library code unlike relational DBMS in general. SQLite is also easy to use without complex configurations. With the support of the ease of function of SQLite it also allows applications to be integrated with the property sales application system.

Microwave Propagation in Maritime Environments

A simple question that arises when dealing with maritime communications is: How would one offload large quantities of data from sea to shore and vice versa if one cannot use conventional solutions such as satellite communications or cellular data? In this note, we describe the first prototype solutions that were produced. They gave rise to SeaFi, which has become an enabling technology in the context of oceanic and coastal research. Measurements at sea are key to scientific research projects such as, for example, HIGHWAVE, a recently started ERC Advanced Grant project that relies partly on the possibility of measuring breaking waves in real time. Since the bottleneck is the real-time transmission of data, transferring measurement data at sea using SeaFi instead of using conventional satellite communications or a cellular data connection quickly became an evidence. To assess the resilience of SeaFi, a series of offshore experiments were performed from May to July 2018. Those experiments led on June 6, 2018, to a world record for the longest wireless microwave transmission at sea between a moving ship and a lighthouse, using the SeaFi communication system. In addition, the proposed solution also promises a better future for lighthouses around the world that are now gradually falling into disuse. This breakthrough in maritime telecommunications could change the way scientific researchers retrieve data in real time at sea in the coming years.