Sistem Box Carton Product Quality Checker Machine Menggunakan Image Processing Dengan Metode Gray Level Co-occurrence Matrix (GLCM)

Wahyudi, Dhenta Fawaz Ghali Dika (2021) Sistem Box Carton Product Quality Checker Machine Menggunakan Image Processing Dengan Metode Gray Level Co-occurrence Matrix (GLCM). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

PT.X mengalami kendala terkait gagalnya proses sortir pada robotic palletizer system, sebelum proses sortir terdapat pengecekan kualitas box carton product oleh manusia. Cacatnya kondisi berat box atau isi produk dalam box carton tidak sesuai dengan semestinya dan cacatnya kondisi fisik box dapat mengakibatkan proses sortir pada robotic palletizer system menjadi terganggu dan terjadi maintenance pada sistem yang dapat mengakibatkan perusahaan mengalami kerugian.
Maka dari itu dirancang sebuah alat yang dapat dapat mendeteksi kualitas box menggunakan image processing dengan metode Gray Level Co-occurrence Matrix (GLCM) sebagai pendeteksi tekstur dari objek dengan mengeluarkan nilai energy, correlation, homogeneity, dissimilarity, dan contrast.. Kemudian hasil pembacaan GLCM akan dilakukan klasifikasi menggunakan SVM (Support Vector Machine) untuk memilah kondisi goodbox dan badbox. Kemudian berat box akan ditimbang menggunakan loadcell. Kondisi kualitas box dari segi fisik dan berat keduanya harus dalam keadaan baik atau harus terpenuhi (menggunakan logika AND), jika salah satu saja tidak memenuhi maka dilakukan proses reject.
Hasil dari alat pendeteksi kualitas box ini, ketika mendeteksi kualitas fisik box pada ruang terbuka yang dilakukan pengujian pada siang hari menghasilkan nilai accuracy sebesar 85%, Ketika dilakukan pengujian pada malam hari menghasilkan akurasi sebesar 80%, Ketika dilakukan pengujian didalam ruang tertutup menghasilkan akurasi sebesar 90% dan Ketika dilakukan pengujian keseluruhan didapat hasil akurasi sebesar 80%.
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PT.X experienced problems related to the failure of the sorting process on the robotic palletizer system, before the sorting process there was a check on the quality of the carton product box by humans. The defective condition of the weight of the box or the contents of the product in the carton box does not match it properly and the defect in the physical condition of the box can cause the sorting process on the robotic palletizer system to be disrupted and maintenance occurs on the system which can cause the company to suffer losses.
Therefore, a tool is designed that can detect box quality using image processing with the Gray Level Co-occurrence Matrix (GLCM) method as a texture detector from objects by issuing energy, correlation, homogeneity, dissimilarity, and contrast values. Then the results of GLCM readings will be classified using SVM (Support Vector Machine) to sort out the conditions of goodbox and badbox. Then the weight of the box will be weighed using a loadcell. The condition of the quality of the box in terms of physical and weight both must be in good condition or must be fulfilled (using AND logic), if one of them does not meet then a reject process is carried out.
The results of this box quality detection tool, when detecting the physical quality of the box in an open space that is tested during the day, produces an accuracy value of 85%, When testing at night produces an accuracy of 80%, When testing in a closed room produces an accuracy of 90% and when the overall test was carried out, the results of accuracy were 80%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Gray Level Co-occurrence Matrix, Image Processing, Support Vector Machine (SVM)
Subjects:	Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. T Technology > T Technology (General) > T57.5 Data Processing T Technology > TA Engineering (General). Civil engineering (General) > TA1573 Detectors. Sensors T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing--Digital techniques T Technology > TJ Mechanical engineering and machinery > TJ1398 Conveyors T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK2785 Electric motors, Induction.
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	Dhenta Fawaz Ghali Dika Wahyudi
Date Deposited:	25 Aug 2021 00:35
Last Modified:	25 Aug 2021 00:35
URI:	http://repository.its.ac.id/id/eprint/90289

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