Pendekatan artificial neural network untuk memonitor proses pengendalian kualitas multi atribut = Artificial neural network approach to monitor multi attribute quality control process / Aditya Putra Setyana
| Main Author: | Aditya Putra Setyana, author |
|---|---|
| Format: | Masters Doctoral |
| Terbitan: |
, 2015
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| Subjects: | |
| Online Access: |
http://lib.ui.ac.id/file?file=digital/2016-3/20414190-T44483-Aditya Putra Setyana.pdf |
Daftar Isi:
- [Proses produksi di industri modern yang semakin kompleks dapat menyebabkan proses berautokorelasi dan juga berkorelasi antar variabel. Untuk memonitor proses tersebut diperlukan metode yang tepat untuk menghindari terjadinya tipe error I (false alarm) maupun tipe error II dalam process monitoring. Pendekatan Artificial Neural Network (ANN) diketahui mampu menangani masalah yang kompleks pada proses multi variabel maupun multi atribut yang berkorelasi. Penelitian ini mengaplikasikan ANN untuk memonitor proses pengendalian kualitas multi atribut dengan data riil pengendalian kualitas suatu perusahaan manufaktur dan membandingkan performa ANN tersebut dengan multi-attribute control chart yang sudah ada dari perhitungan average run length, Production process in modern industry, which is getting more complex, could make process autocorrelated and also correlated between each variable. It is necessary to use the right method on process monitoring to prevent type I error (false alarm) and type II error. Artificial Neural Network (ANN) approach is known as capable method to handle complex problem on correlated mutlivariable or multi-attribute process. This study applies ANN to monitor multi-attribute quality control process using real quality control data from a manufacture company, and also compares the performance of ANN with the existing multiattribute control chart based on average run length calculation]