This research aims to expose strategy and scale of politeness in Riau students directive speech surrounded by Javanese culture, also its implications on languagelearning in manners in high school. This is a qualitative descriptive research. The data are in the form of directive speech either in formal or informal. The data source includes the activities of 15 Riau students of UMS, UNS, and IAIN. The data gathering techniques includes refering, recording, and taking notes. Data analysis methods in this research includes pragmatics comparison wether intralingual andextralingual. The interpretation of directive speech form is done by reffering to Prayitno’s work, while the analysis of politeness scale is based on Lakoff’s model.The results showed that (1) Riau students are much prefer to use indirect strategy when they are communicating with Javanese society (66,70% : 33,30%). (2) Theanalysis of politeness scale refers to the results of the politeness strategy category in terms of politeness levels low, medium, and fine. The combination of politenessstrategy of Riau students has a low level of politeness with comparison 47%: 23%: 30%. Total data on the scale of politeness in directive speech is low for as many as14 data, while the level of politeness are as many as 7 data, and the level of good politeness is as much as 9 data. (3) The implications of this research on languagelearning with manners in high school can be used as learning materials. In the 2013 curriculum, it is stated that the core competencies-2 (KI-2) of social attitude of classX, XI, and XII demands students to able to use the Indonesian language with manners while KTSP curriculum focused at speaking skills. Language learning can be fun ifstudents and teachers are able to use the language more politely.
Abstract— The application of in-depth learning methods has been successfully applied in computer vision task with the ability to learn the features of differences in real world images by directly from the original image by passing layer after layer to get the high dimensions image, in this study we applied the YOLO method approach with network adaptation features based on Darknet-53 on a video dataset recorded by the activities of University of Indonesia Prima (UNPRI) students with are conditions of video with different objects as a surveillance system, based on the results of research into object classification produces an overall accuracy of 93%, but for the classification of objects bikes, buses, and cars have the lowest accuracy of 30% for bikes, 54% of cars and buses by 40% so it is necessary to develop methods to improve accuracy.
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