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All Journal Jurnal Ilmiah Poli Rekayasa Enthalpy : Jurnal Ilmiah Mahasiswa Teknik Mesin Jurnal Teknik Mesin Jurnal Teknik Industri Terintegrasi (JUTIN) Andalas Journal of Electrical and Electronic Engineering Technology International Journal of Advanced Science Computing and Engineering JAPEPAM, Jurnal Pengabdian kepada Masyarakat
Sir Anderson
Politeknik Negeri Padang
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Andalas Journal of Electrical and Electronic Engineering Technology

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The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer Roza Susanti; Zas Ressy Aidha; Surfa Yondri; Sir Anderson; Tri Oktaviandra
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.27

Abstract

This study uses a backpropagation neural network to determine the evenness of the chayote chip dough. The Tcs3200 Color Sensor mounted on the stirrer alt is used as a sensor to determine the color of the chayote emping dough. A regression score of 1 indicates that the input and target data match in the test results of the artificial neural network, which has an objective error (MSE) value of 0.0096306 achieved in the 313th epoch. Changes in RGB color readings on the TCS sensor from min values ??<40 and max values>52 in mixing dough are influenced by distance and light intensity which will be converted in the form of frequency.
The Use of Artificial Neural Networks (ANN) in the Chayote Chips Dough Mixer Roza Susanti; Zas Ressy Aidha; Surfa Yondri; Sir Anderson; Tri Oktaviandra
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 2 No. 2 (2022): November 2022
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v2i2.27

Abstract

This study uses a backpropagation neural network to determine the evenness of the chayote chip dough. The Tcs3200 Color Sensor mounted on the stirrer alt is used as a sensor to determine the color of the chayote emping dough. A regression score of 1 indicates that the input and target data match in the test results of the artificial neural network, which has an objective error (MSE) value of 0.0096306 achieved in the 313th epoch. Changes in RGB color readings on the TCS sensor from min values ??<40 and max values>52 in mixing dough are influenced by distance and light intensity which will be converted in the form of frequency.
Co-Authors Afriyani, Sicilia Ahmad, Fahdatul Andrianto Andrianto Anisa, Fitri Anwar Kasim Dian Wahyu EFENDI Elisma, Netri Fedri Hidayat Figo Zubrianto Hafni, Ferdi Nanda Hanif Hanif harfardi harfardi Haris Haris, Haris Insani, Farhan Filza Junaidi - - Khairul Amri Khairul Amri Latifa Hanum Maimuzar Maimuzar Muchlisinalahuddin Nasirwan Nasirwan Nofriadi Nofriadi Nusyirwan Nusyirwan Rahmi, Nurfitri Rama Pranata Roza Susanti Ruzita Sumiati Sirnikson - - Surfa Yondri Tri Oktaviandra Veny Selviyanty Verdian, Riza Yul Antonisfia Yuliarman Yuliarman Zas Ressy Aidha