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All Journal INFOKAM JAIS (Journal of Applied Intelligent System) Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Abdimasku : Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Seminar Nasional Teknologi dan Multidisiplin Ilmu
Sinaga, Daurat
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Public Health Social Sciences

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Journal : JAIS (Journal of Applied Intelligent System)

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Pattern Recognition on Vehicle Number Plates Using a Fast Match Algorithm Cahaya Jatmoko; Daurat Sinaga; Edi Sugiarto; Nur Rokhman; Heru Lestiawan
Journal of Applied Intelligent System Vol 6, No 2 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i2.4625

Abstract

Computer Vision was the fast developing apps in the world, it is make people make a lot of new algorithm. Before we can use in out app, we need to test the algorithm to make sure how effective and optimal the algorithm to solve every case we given. A lot of traffic system has implemented computer vision, they need fast and can work in every condition, because every vehicle who pass needs to be recognized. In this research Fast Match algorithm was chosen because they can solve some test and make a lot of image have a similarity with the template. It makes accuracy of the data can be achieved with this algorithm. For example on of the sample was have a SAD point for 0.5 and Overlap Error for 0.5 and can run in standard computer just for a couple second. It makes the template and the original image has a little similarity.
A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor Cahaya Jatmoko; Daurat Sinaga
Journal of Applied Intelligent System Vol 3, No 2 (2018): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v3i2.2151

Abstract

In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data.
Co-Authors Aris Marjuni Cahaya Jatmiko Cahaya Jatmoko Chaerul Umam De Rosal Ignatius Moses Setiadi Defri Kurniawan Edi Sugiarto Eko Hari Rachmawanto Feri Agustina Heru Lestiawan Nova Rijati Novita Kurnia Ningrum Nur Rokhman