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Traffic signs are part of road equipment whose existence is very important, in addition to functioning as warnings, prohibitions, orders, or instructions for road users, traffic signs are also a means to reduce accidents and regulate driver behavior. Because of the importance of this function, it is necessary to collect accurate sign data in a spatial database. Currently, there have been many database developments for the management of traffic signs, but data collection is still done manually, by means of surveyors recording groups of traffic signs and entering them into the database. The difficulty faced is the time and accuracy of the surveyors when it comes to selecting groups of signs, this is due to the large number of groups/sub-groups of signs. This problem needs to be solved with the help of a sign group detection tool with an image recognition approach. This study aims to develop an image recognition method to extract photo geotagging information on traffic signs into spatial data and attributes of traffic sign groups. The object of the signs that are sampled are signs that are on roads with the status of provincial roads in the Special Region of Yogyakarta. The results showed that the machine learning-based image recognition accuracy reached 88.66%, further research is needed to improve accuracy by paying attention to the geotagging photo capture variable.

Android smartphone, one of the entertainment media that is used by many people for games. Besides being an entertainment media, games can be used as educational media, such as the introduction of traffic signs. Knowledge of traffic signs on motorists influences the occurrence of traffic accidents. Therefore, the introduction of traffic signs is one of the efforts in controlling traffic. This is because knowledge of traffic signs is currently lacking. In this study, it will present the making of a Smart Rider application that aims to introduce traffic signs to prospective drivers and drivers of motorized vehicles. The software development method used is Waterfall, which includes communication, planning, modeling, construction and deployment. Application testing uses material testing, functionality, usability and portability. Usability testing is done by media experts and users. Users in this study were taken from Kebumen Vocational High School 1 students. From the results of testing the application as a whole, shows that the application has a good function. The test results can be seen from the material test reaching 94.4%, the user response reached 78.74%, and the results of testing by media experts reached 87.5%. Thus, the Smart Rider application is declared feasible to be used as a game for introducing Android-based traffic signs.