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All Journal Ekonomi Bisnis Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Ilmiah Poli Rekayasa Elektron Jurnal Ilmiah VANOS Journal of Mechanical Engineering Education Edukasi: Jurnal Pendidikan INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Ensiklopedia of Journal Journal of Applied Engineering and Technological Science (JAETS) MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering PAKAR Pendidikan Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat MSI Transaction on Education AEEJ : Journal of Automotive Engineering and Vocational Education INJECTION: Indonesian Journal of Vocational Mechanical Engineering JTPVI: Jurnal Teknologi dan Pendidikan Vokasi Indonesia
Toto Sugiarto
Universitas Negeri Padang
Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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Journal : Jurnal Ilmiah Poli Rekayasa

 1 
Pemodelan Sistem dengan Metode Neural Network Back Propagation Modeling System Using Neural Network Backpropagation Dwi Sudarno Putra; Toto Sugiarto -; Meri Azmi -
Jurnal Ilmiah Poli Rekayasa Vol 11, No 2 (2016)
Publisher : Pusat Penelitian dan pengabdian kepada Masyarakat (P3M) Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (531.824 KB) | DOI: 10.30630/jipr.11.2.24

Abstract

A system can be modeled using mathematical formulation analysis method. Analysis of the system starts from the value of inputs, processes, noise, and output processes. then sought mathematical approach. This method is very complex, especially for some of the processes that have a higher order. In this article the author tries to discuss a method to perform modeling on a system by using Back Propagation Neural Network method.In the neural network method we require the initial data for network training process. Network training process is intended to gain weight. The weight is then identified with a model system. This paper has demonstrated that the modeling system can be done by the method of back propagation neural network 
Analisis Dan Perancangan Alat Pendeteksi Tekanan Kerja Pompa Bahan Bakar Efi (Electronic Fuel Injection) Dengan Menggunakan Sensor Tekanan Toto Sugiarto -; Andrizal - -; Yandri Anto -
Jurnal Ilmiah Poli Rekayasa Vol 11, No 1 (2015)
Publisher : Pusat Penelitian dan pengabdian kepada Masyarakat (P3M) Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (775.296 KB) | DOI: 10.30630/jipr.11.1.20

Abstract

An EFI cars (Electronic Fuel Injection) is not equipped with a pressure fuel pump indicator, so the driver does not know the the fuel pump performance. If the fuel pump work under the standard, the engine is not responsive to acceleration, engine temperature can be increased and the performance of the engine is lowered. This research are trying to design a fuel pump pressure detector using a pressure sensor. The research methodology used is the Research and Development (R & D) consisting of product design, design validation, design revisions, and test products. This study used three product design because the design of the first and the second has not been as expected. The third design uses a comparator circuit as the controller three indicator LED (Light Emitting Diode). If the fuel pump working pressure of 2,1kg / cm²-2.5 kg / cm², the three colored LED green, yellow and red lights up. If the fuel pump working pressure of 1.6 kg / cm²-2,0kg / cm², the two LED will light up the colors yellow and red. If the fuel pump working pressure of 1,0kg / cm²-1.5 kg / cm², it is only one red LED are illuminated. A comparison between the changes in pressure to produce the output voltage linear data and repeatibility = 0.
Co-Authors A G Halim Abdullah, MT, Dr. Rijal Afdhal Zikri Ahmad Arif Alfan Rifani Ambiyar, Ambiyar Andre Kurniawan Andrizal Andrizal Arwizet Arwizet, Arwizet Asmar Yulastri Azhar Afrizal Bahrul Amin Deded Purnama Dedi Setiawan Donny Fernandez Dori Yuvenda Dwi Sudarno Putra Erzeddin Alwi Fadhilah Fadhilah Fahmi Otriza Fajar Hanifudin Afifi Fathi Aulia DZ Fauzan Zaharbaini Fazrin Hidayat Fazrin Hidayat Ganefri . Giyano Lesmana Hamid Nasrullah Hasan Maksum Hendra Dani Saputra Ichsan Elfandy Irma Yulia Basri Lasyatta Syaifullah M Joko M Nasir M Nasir M. Abdi Rizal M. Yasep Setiawan Martias Martias Masykur Masykur Meri Azmi Meri Azmi - Miswardi Muhammad Mukhbit Nurdin Muslim Muslim Nanda, Iffarial Nindito Nizwardi Jalinus Nurdin Hendri Nuzul Hidayat, Nuzul Purwanto, Wawan Putra, Randi Purnama R. Chandra Refdinal, Refdinal Remon Lapisa Rian Arianto Ridwan Ridwan Rizki Fajri Rudy Anarta Wagino Wagino Wagino Wakhinuddin S Wakhinuddin S Wakhinuddin Wakhinuddin Waskito Yandri Anto - Yanto, Doni Tri Putra Yolli Fernanda Yuke Permata Lisna Zamzami