Deteksi Osilasi Kontrol Proses Industri dengan Menggunakan Jaringan Saraf Tiruan - Review

Nukman Tsaqib Tsanya; A.N.I. Wardana; Nazrul Effendy

doi:10.21776/jeeccis.v16i3.1468

Authors

Nukman Tsaqib Tsanya Gadjah Mada University
A.N.I. Wardana Gadjah Mada University
Nazrul Effendy Gadjah Mada University

DOI:

https://doi.org/10.21776/jeeccis.v16i3.1468

Keywords:

Deteksi, Industri, Osilasi, Jaringan Saraf Tiruan

Abstract

Osilasi kontrol loop adalah salah satu masalah yang sering terjadi di proses industri. Osilasi menyebabkan variabel proses tidak dapat dipertahankan pada kondisi yang diinginkan yang akan menyebabkan kerugian finansial pada industri. Selama beberapa tahun terakhir sudah banyak penelitian yang dilakukan, dan salah satu pendekatan yang semakin banyak digunakan adalah menggunakan jaringan saraf tiruan (JST). Tulisan ini memberikan gambaran tentang karakterisktik JST dan penerapannya untuk deteksi dan diagnosis osilasi kontrol pada proses industri. Dari beberapa studi yang dipelajari, arsitektur JST yang paling umum dipilih untuk proses deteksi osilasi adalah multilayer perceptron (MLP), convulational neural network (CNN) dan recurrent neural network (RNN). Tiap arsitektur tersebut memiliki karakteristik dan fungsi yang berbeda. MLP memiliki karakteristik yang sederhana, dan fleksibel dibandingkan jaringan lainnya. CNN bekerja sangat baik untuk melakukan teknik deteksi dengan menggunakan pengenalan pola. Sementara RNN sangat baik digunakan untuk mendeteksi sistem dinamis pada proses industri.

Author Biographies

Nukman Tsaqib Tsanya, Gadjah Mada University

Departemen Teknik Nuklir dan Teknik Fisika

A.N.I. Wardana, Gadjah Mada University

Departemen Teknik Nuklir dan Teknik Fisika

Nazrul Effendy, Gadjah Mada University

Departemen Teknik Nuklir dan Teknik Fisika

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