Data pengguna sepeda motor tahun 2016 berdasarkan Badan Pusat Statistik terdapat 105.150.082 pengguna di Indonesia . Angka ini adalah angka yang terbanyak dari semua total kendaraan. Menurut katadata.co.id, terdapat 98.000 kali kecelakan yang terjadi pada tahun 2017. Hal ini didominasi oleh kendaraan khususnya sepeda motor. Kecelakan yang ditimbulkan disebabkan oleh kelalaian pengguna sepeda motor dalam merawat motornya tersebut. Upaya dalam mengantisipasi terjadinya kecelakaan salah satunya adalah melakukan pengecekan kendaraan bermotor secara rutin. Menurut buku panduan sepeda motor, bahwa setiap kali motor yang digunakan wajib untuk melakukan pengecekan minimal 3 bulan sekali agar motor tetap pada performa utamanya. Metode yang digunakan untuk pengambilan keputusan dalam penjadwalan dan pengingat menggunakan metode fuzzy sugeno. Fuzzy merupakan suatu cara untuk memetakan suatu ruang input ke dalam suatu ruang output . Solusi yang ditawarkan pada penelitian ini akan aplikasi mobile yang dikhususkan untuk pengguna sepeda motor dalam melakukan perawatan rutin sebagai penjadwalan dan pengingat. Hasil yang didapatkan Berdasarkan pengujian manual dan pengujian melalui system yakni 16 siap service dan 14 tidak siap service. Presentase keakuratan system dengan perhitungan manual 100% sama dengan perhitngan system. Prensentasi pengaruh terhadap perawatan motor adalah 88.27% setuju terhadap pembuatan aplikasi ini untuk perawatan motor terhadap kecelakaan motor.
Indonesia is a country that has a variety of cultures, one of which is wayang kulit. This typical javanese performance art must continue to be preserved so that to be known by future generations. There are many wayang figures in Indonesia, and the most famous is punakawan. Wayang punakawan consists of four character namely semar, gareng petruk, and bagong. To preserve wayang punakawan to be known by the next generation, then in this study created a system that is able to identify real-time punakawan object using deep learning technology. The method that used is Single Shot Multiple Detector (SSD) as one of the models of deep learning that has a good ability in classifying data with three-dimensional structures such as real-time video. SSD model with MobileNet layer can work in slight computation, so that it can be run in real-time system. To classify object there are two steps that must be done such as training process and testing process. Training process takes 28 hours with 100.000 steps of iteration.The result of training process is a model which used to identify object. Based on the test result obtained an accuracy to detect object was 98,86%. This prove that the system has been able to optimize object in real-time accurately.
A leader-follower robot is used to perform different tasks without continuous human assistance. The movement of robot leader-follower to environment who do not structure, avoid persecution and achieving goals is very difficult. Related to the problem, the robot leader-follower requires navigating robots independently using Interval Fuzzy Logic Type-2 (IFLT) 2 Algorithm. The IFLT 2 algorithm performance is successfully applied to this leader-follower robot, with 8 base rules less than the Fuzzy Logic Type 1 Algorithm. This simulation, the robot successfully moves to avoid obstacles and go hand in hand with the position of the follower robot always following the position of the robot leader.
Currency recognition is one of the essential things since everyone in any country must know money. Therefore, computer vision has been developed to recognize currency. One of the currency recognition uses the SIFT algorithm. The recognition results are very accurate, but the processing takes a considerable amount of time, making it impossible to run for real-time data such as video. AKAZE algorithm has been developed for real-time data processing because of its fast computation time to process video data frames. This study proposes the faster real-time currency recognition system on video using the AKAZE algorithm. The purpose of this study is to compare the SIFT and AKAZE algorithms related to a real-time video data processing to determine the value of F1 and its speed. Based on the experimental results, the AKAZE algorithm is resulting F1 value of 0.97, and the processing speed on each video frame is 0.251 seconds. Then at the same video resolution, the SIFT algorithm results in an F1 value of 0.65 and a speed of 0.305 seconds to process one frame. These results show that the AKAZE algorithm is faster and more accurate in processing video data.
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