Sistem identifikasi plat nomor kendaraan adalah salah satu penelitian yang paling penting dalam bidang perkembangan teknologi Intelligent Transportation System (ITS). Secara umum, terdapat tiga tahap yaitu identifikasi yang disebut juga dengan deteksi, segmentasi, dan pengenalan. Beberapa metode seperti Otsu dan connected component label diterapkan dengan akurasi plat nomor kendaraan menurut karakter segmentasi. Penelitian ini berfokus pada peningkatan pre-processing menggunakan Otsu threesholding dengan metode Gaussian dan juga terhubung melalui komponen label yang diadopsi untuk membuktikan peningkatan keberhasil metode pre-processing, sehingga dapat meningkatkan hasil segmentasi karakter. Peningkatan dapat dilihat dengan rata-rata perbedaan penurunan nilai MSE menuju 1,75E+ 07 pixel dan PSNR yang lebih tinggi yaitu 4db/pixel. Dengan hasil ini, Otsu dengan metode pre-processing gaussian lebih baik daripada metode Otsu yang asli.
The studies of human mobility prediction in mobile computing area gained due to the availability of large-scale dataset contained history of location trajectory. Previous work has been proposed many solutions for increasing of human mobility prediction result accuration, however, only few researchers have addressed the issue of<em> </em>human mobility for implementation of LSTM networks. This study attempted to use classical methodologies by combining LSTM and DBSCAN because those algorithms can tackle problem in human mobility, including large-scale sequential data modeling and number of clusters of arbitrary trajectory identification. The method of research consists of DBSCAN for clustering, long short-term memory (LSTM) algorithm for modelling and prediction, and Root Mean Square Error (RMSE) for evaluation. As the result,<em> </em>the prediction error or RMSE value reached score 3.551 by setting LSTM with parameter of <em>epoch</em> and <em>batch_size</em> is 100 and 20 respectively.
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