Sistem Klasifikasi Produk Pada Proses Quality Checker Menggunakan Metode Support Vector Machine (SVM)

Putra, Hendry Primanto Adji (2021) Sistem Klasifikasi Produk Pada Proses Quality Checker Menggunakan Metode Support Vector Machine (SVM). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Di Indonesia banyak perusahaan produksi yang masih menggunkan cara manual atau masih menggunakan tenaga manusia, tepat nya pada bagian pengemasan. Beberapa perusahaan produksi mengalami beberapa kendala terkait gagalnya proses sortir, hal ini diakibatkan karena terdapat beberapa produk yang memiliki kualitas buruk (memiliki cacat fisik) masuk kedalam proses pengemasan sehingga mengakibatkan proses produksi menjadi terganggu.
Dari permasalahan tersebut maka penelitian ini bertujuan membangun sebuah sistem quality checker dengan mengimplementasikan aplikasi berbasis website yang dapat mengklasifikasi sebuah produk dengan mensortir produk baik dan produk buruk menggunakan metode klasifikasi Support Vector Machine (SVM) dan metode ekstraksi Gray Level Co-Occurrence (GLCM) menggunakan kamera.
Pada hasil pengujian sistem quality checker, metode ekstraksi Gray Level Co-Occurrence (GLCM) dan metode klasifikasi Support Vector Machine (SVM) mampu mengklasifikasi produk baik dan produk buruk dengan hasil training model yang didapatkan memiliki nilai rata-rata sebesar 98%.
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In Indonesia, many production companies still use manual methods or still use human labor, specifically in the packaging section. Several production companies experienced several problems related to the failure of the sorting process, this was due to the fact that some products that had poor quality (having physical defects) entered the packaging process, causing the production process to be disrupted.
From these problems, this study aims to build a quality checker system by implementing a website-based application that can classify a product by sorting good and bad products using the Support Vector Machine (SVM) classification method and the Gray Level Co-Occurrence (GLCM) extraction method using camera.
On the results of the quality checker system testing, the Gray Level Co-Occurrence (GLCM) extraction method and the Support Vector Machine (SVM) classification method were able to classify good products and bad products with the training model results obtained having an average value of 98%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Produk, Klasifikasi, Support Vector Machine (SVM), Gray Level Co-Occurrence (GLCM), Kamera, Aplikasi Desktop, Product, Classification, Support Vector Machine (SVM), Gray Level Co-Occurrence (GLCM), Camera, Desktop Application
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5105.888 Web sites--Design. Web site development.
Divisions:	Faculty of Vocational > 36304-Automation Electronic Engineering
Depositing User:	Hendry Primanto Adji Putra
Date Deposited:	10 Mar 2022 06:47
Last Modified:	10 Mar 2022 06:47
URI:	http://repository.its.ac.id/id/eprint/94803

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