Maulidia Rahmah Hidayah, Maulidia Rahmah
Computer Science - Semarang State University

Published : 2 Documents
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Penggunaan Metode Depth First Search (DFS) dan Breadth First Search (BFS) pada Strategi Game Kamen Rider Decade Versi 0.3

Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang

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Abstract

Pada permainan Game Kamen Rider Decade ini sangat membutuhkan strategi yang tepat jika ingin memenangkan dengan mudah permainan ini. Penelitian ini bertujuan untuk mengimplementasikan metode Dept First Search (DFS) dan Breadth First Search (BFS) pada Game Kamen Rider Decade, yang merupakan permainan dengan strategi penyelesaiannya menggunakan metode pencarian buta (blind search). Pengumpulan data dilakukan dengan pendekatan kualitatif dengan metode deskriptif, dimana pengujian dilakukan dengan memainkan 3 kali masing-masing dengan metode selalu BFS dan selalu DFS. Hasil menunjukan peluang lebih besar memenangkan permainan ini adalah dengan strategi selalu BFS. Dimana kemampuan BFS pada permainan ini dapat berguna untuk pertahanan terhadap musuh. 

Recognition Number of The Vehicle Plate Using Otsu Method and K-Nearest Neighbour Classification

Scientific Journal of Informatics Vol 4, No 1 (2017): May 2017
Publisher : Universitas Negeri Semarang

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Abstract

The current topic that is interesting as a solution of the impact of public service improvement toward vehicle is License Plate Recognition (LPR), but it still needs to develop the research of LPR method. Some of the previous researchs showed that K-Nearest Neighbour (KNN) succeed in car license plate recognition. The Objectives of this research was to determine the implementation and accuracy of Otsu Method toward license plate recognition. The method of this research was Otsu method to extract the characteristics and image of the plate into binary image and KNN as recognition classification method of each character. The development of the license plate recognition program by using Otsu method and classification of KNN is following the steps of pattern recognition, such as input and sensing, pre-processing, extraction feature Otsu method binary, segmentation, KNN classification method and post-processing by calculating the level of accuracy. The study showed that this program can recognize by 82% from 100 test plate with 93,75% of number recognition accuracy and 91,92% of letter recognition accuracy.