Pengenalan Nomor Seri Tabung Gas Medis Menggunakan Jaringan Syaraf Tiruan Back Propagation

https://doi.org/10.22146/ijeis.7122

Adhi Prahara(1*), Agus Harjoko(2)

(1) 
(2) Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Abstrak

Optical Character Recognition (OCR) merupakan aplikasi dalam pengenalan pola untuk mengenali karakter pada citra digital.  Dalam penelitian ini, OCR digunakan untuk mengenali nomor seri pada tabung gas medis. Tabung gas medis memiliki nomor seri yang ditulis dengan cat pada badan tabung gas.  Oleh karena itu, tampilan karakter nomor serinya rentan terhadap derau seperti retakan cat pada nomor seri maupun latar belakangnya.  Selain itu, nomor serinya ada yang tidak ditulis dengan cetakan standar sehingga bentuk karakternya seperti karakter tulisan tangan. 

Metode yang digunakan dalam sistem ini meliputi perbaikan citra, segmentasi karakter, dan pengenalan karakter nomor seri. Perbaikan citra dilakukan dengan menerapkan filter bilateral untuk menghaluskan citra dan menajamkan tepian. Segmentasi karakter menggunakan metode thresholding pada label warna latar belakang dan nomor seri yang didapat dari klastering dengan K-means.  Pengenalan nomor seri menggunakan jaringan syaraf tiruan back propagation pada citra karakter nomor seri hasil segmentasi karakter.

Pengujian dilakukan dengan 20 citra sampel nomor seri tabung medis.  Hasil pengujian menunjukkan keakuratan deteksi 95,05%, kesalahan deteksi 1,98% dan keakuratan pengenalan 91,09%.  Akurasi pengenalan dipengaruhi oleh adanya derau seperti kondisi plat tabung gas, false positif, dan kelengkapan latar belakang.   

 

Kata kunci—Nomor seri, OCR, K-means, Filter bilateral, JST Back Propagation

 

Abstract

Optical Character Recognition (OCR) is an application in pattern recognition to recognize characters on the digital image. In this study, OCR is used to recognize serial number on medical gas cylinders. Medical gas cylinders have serial numbers written with paint on the body of the gas cylinder. Therefore, the serial numbers is susceptible to noise such as paint cracks on serial numbers and background. In addition, there are serial numbers written with non-standard mold so the shapes of its character like a handwriting characters.

The method used in the system are image enhancement, character segmentation and serial number recognition.  Image enhancement is done by applying bilateral filter to refine image and sharpen image edges. Character segmentation is done by thresholding serial numbers and background color labels obtained from K-means clustering.  Serial number recognition is done by applying back propagation neural network on characters serial number obtained from character segmentation.

The tests conducted with 20 serial number of medical gas cylinders image samples. The test results showed 95,05% detection accuracy with 1,98% error and 91,09% recognition accuracy. Accuracy mainly influenced by noise such as plate conditions, false positives, and completeness of the background.

 

Keywords—Serial number, OCR, K-means, Bilateral filter, Backpropagation ANN


Keywords


Serial number, OCR, K-means, Bilateral filter, Backpropagation ANN

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DOI: https://doi.org/10.22146/ijeis.7122

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