Penerapan Sistem Pakar Berbasis Aturan dan Metode Freeman Chain Code pada Aplikasi Pengukuran Isi Botol

Authors

  • Ken Ratri Wardani Institut Teknologi Harapan Bangsa
  • Ari Setiawan Institut Teknologi Harapan Bangsa
  • Yohanes Setiawan Institut Teknologi Harapan Bangsa

DOI:

https://doi.org/10.61769/telematika.v13i2.220

Keywords:

botol, volume, rule based, chain code

Abstract

In the field of the food or beverage industry, particularly in the beverage industry, a tool is needed to measure the contents of the bottle volume which is useful to supervise the process of bottle filling so that the contents of the bottle is not less or no more than the company limit. Rule-based expert systems can be used to calculate the estimated volume contents of a bottle by giving facts to the bottle and the fact of the bottle tolerance. From these facts will be calculated the estimated volume of the image to be tested and determine whether the image is included in the company standard or not. The chain code method can also be used in a wide screening process that serves to filter unwanted pixels for the purpose of searching the region over the bottle and surface area of the bottle contents. Testing the threshold value of the chain code is performed to find the most optimal threshold value for widespread filtering. Based on the results of testing the most optimal threshold value is 200 and 300 with 100% accuracy and 0.89 milliliter accuracy.


Pada bidang industri makanan atau minuman, khususnya pada industri minuman dibutuhkan alat untuk mengukur isi volume botol yang berguna untuk mengatasi masalah dalam pengisian botol sehingga isi botol tidak kurang atau tidak lebih dari batas perusahaan. Sistem pakar berbasis aturan dapat digunakan dalam menghitung perkiraan isi volume suatu botol dengan memberi fakta berupa citra botol berisi volume neto dan fakta berupa citra botol berisi volume toleransi. Dari fakta-fakta tersebut akan dihitung perkiraan volume dari citra yang akan diuji dan menentukan apakah citra tersebut masuk dalam standar perusahaan atau tidak. Metode kode rantai juga dapat digunakan dalam proses penyaringan luas yang berfungsi menyaring piksel-piksel yang tidak diinginkan  untuk keperluan proses pencarian wilayah atas botol dan wilayah permukaan isi botol. Pengujian nilai threshold luas area kode rantai dilakukan untuk mencari nilai threshold paling optimal untuk penyaringan luas. Berdasarkan hasil pengujian nilai threshold paling optimal adalah 200 dan 300 dengan akurasi 100% dan tingkat ketelitian 0,89 mililiter.



References

DavidTaylor, David Brunt. 2001 Manufacturing Operationsand Supply Chain Management 1st edition. Thomson Learning (September 2017), ISSN: 690019W-1FXU

Ahmad Fashiha Hastawan, Achmad Hidayatno, R.RizalIsnanto. 2013. Deteksi Sudut Menggunakan Kode Rantai Untuk Pengenalan Bangun Datar Dua Dimensi . (September 2017)

Gonzalez Rafael, Woods Richard. 2007. Digital Image Processing 3rd Edition. Pearson Education International (Agustus 2017), ISSN: 005475.

Jose Francisco Martinez, Jesus Ariel Carrasco Ochoa. 2006. Progress in Pattern Recognition, Image Analysis Recognition and Application. Springer (Desember 2017), ISSN: 0302-9743.

Yusron Rijal, Riza Dhian Ariefianto. 2008. Deteksi Wajah Berbasis Segmentasi Model Warna Menggunakan Template Matching Pada Objek Bergerak. (Oktober 2017)

Ligeza. 2006. Logical Foundations for Rule-Based System. Springer (Desember 2017), ISSN: I860-949X.

Takeshi Sato, N. Sugiarto Hartanto. 2003. Menggambar Mesin Menurut Standar ISO. PT Pradnya Paramita, ISBN: 979-408-006-3

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Published

2019-02-05

Issue

Vol. 13 No. 2 (2018)

Section

Articles

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