Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Vol 6 No 2 (2022): April 2022

Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features

Rohmat Indra Borman (Universitas Teknokrat Indonesia)
Yusra Fernando (Universitas Teknokrat Indonesia)
Yohanes Egi Pratama Yudoutomo (Universitas Teknokrat Indonesia)

Article Info

Publish Date
29 Apr 2022


The increase in the number of vehicles every year results in traffic jams. So it is necessary to identify the type of vehicle so that the vehicle can be arranged according to the path. This study aims to develop a system that can identify the type of vehicle using the Learning Vector Quantization (LVQ) algorithm. For LVQ to work well in identifying, information in the form of characteristics of the object is needed. For this reason, the LVQ algorithm is combined with morphological feature extraction using the parameters of area, circumference, eccentricity, primary axis length, and minor axis length to obtain shape features. Based on the test results using a confusion matrix by calculating precision, recall, and accuracy, it is obtained that the precision value is 85%, recall is 82%, and accuracy is 83%. This paper shows that for vehicle identification, the combination of morphological feature extraction and LVQ algorithm produces a model that can identify vehicles based on their shape and classify classes through competitive layers that are supervised by single-layer network architecture. This makes the computational process faster and does not burden the computational process.

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Journal Info





Computer Science & IT Engineering


Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat ...