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Azizul
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Student Calling Machine When After School by Whatsapp Text Message Syaiful Amri; Syahrizal; Khairudin Syah; Azizul
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

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Abstract

Crowds still have the potential to occur during the implementation of 100 percent face-to-face learning. School residents, especially students, still find it difficult to keep their distance from each other and that happens especially when it's time to go home from school. To overcome this condition, schools are moving quickly to find solutions. For example, the education unit made an alternative to prevent crowds. Namely providing a special waiting area for students, chairs lined up in the school hallway. The children sat while waiting for their parents to come pick them up. As soon as the parents arrive, the student is called over the loudspeaker, and so on. However, this method is not effective enough to reduce the crowd. Because at the time of calling the students who were picked up, the officer (teacher picket) was overwhelmed to recognize the parents or guardians of the students who were picking up the call by loudspeaker, due to the large number of students there could also be a change in the person who picked up the previous day.
Perbandingan Metode Haar Cascade, YoloV3, dan TinyYoloV3 Dalam Mendeteksi Kendaraan Bermotor Berbasis Video Marzuarman; Stephan; Muharnis; Azizul; Doni Mirza Rinaldi; Bagas Prasetyo
ABEC Indonesia Vol. 10 (2022): 10th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

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Motorized vehicles are one of the most important needs in everyday life. Every year in Indonesia there is always an increase in the number of motorized vehicles along with the increase in population. Many researchers in the field of information technology use image processing systems to investigate and develop systems that can be used on public roads, one of which is to detect motor vehicles. In general, the methods that are often used to detect objects are Haar cascade, YOLOV3, and TinyYOLOV3. In this study, a comparison was made, to determine the best accuracy of the three methods in detecting motorized vehicles. The test was carried out using Python 3.10 software that has been installed with OpenCV, where the test was carried out using a video with a duration of 1 minute 23 seconds which was downloaded from the youtube.com site. Based on the test results, the YOLOV3 method gets the best level of accuracy, which is 74%. For the Haar cascade method, the accuracy value is 41%, and TinyYOLOV3 produces an accuracy rate of 25%.
Co-Authors Bagas Prasetyo Doni Mirza Rinaldi Khairudin Syah Marzuarman, Marzuarman Muharnis Stephan Syahrizal Syahrizal Syaiful Amri