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This Author published in this journals
All Journal IJoICT (International Journal on Information and Communication Technology) JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering)
Sukamto
Politeknik Negeri Madiun
Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Mechanical Engineering

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Pengendalian Kecepatan Motor Induksi Menggunakan Kontroler Logika Fuzzy Sukamto
JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) Vol. 4 No. 1 (2019): JOURNAL OF ELECTRICAL, ELECTRONICS, CONTROL, AND AUTOMOTIVE ENGINEERING (JEECAE
Publisher : Pengelolaan Penerbitan Publikasi Ilmiah (P3I) Politeknik Negeri Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32486/jeecae.v4i1.370

Abstract

Motor induksi dengan kelebihannya seperti konstruksi yang sederhana, kokoh, murah serta perawatan mudah, banyak digunakan dalam dunia industri. Namun ada kelemahannya yaitu motor induksi tidak dapat mempertahankan kecepatan secara konstan apabila ada perubahan beban. Untuk mengatasi kelemahan tersebut, dalam penelitian ini dibuat suatu rangkaian kontroller Logika Fuzzy, yang berbasis pada metode Field Oriented Control (FOC). Dengan metode ini arus medan dan arus torka dapat di kontrol secara terpisah. Dalam penelitian ini yang dikontrol arus torka, sedangkan arus medan dibuat konstan Untuk mengetahui keterandalan dari rangkaian kontroller ini dilakukan pengujian terhadap setpoint 2000 - 3000 rpm dan diperoleh error steady state rata-rata (ess) = 0,228 %. Berdasarkan hal tersebut dapat dikatakan kontroller Logika Fuzzy yang dirancang dapat mengendalikan kecepatan motor induksi.
Convolutional Neural Network Implementation with AlexNet Architecture for Face Recognition Denny Hardiyanto; Dyah Anggun Sartika; Imam Junaedi; Sukamto
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.839

Abstract

In today's digital era, the process of facial recognition has a very big role. Face recognition has benefits for authentication and identification processes. The development of facial recognition research continues to be carried out with the aim of being able to get the right algorithm, more accurate, faster processing, to be able to recognize faces from various angles. In this study, a performance test was performed on the Convolutional Neural Network (CNN) algorithm with the AlexNet architecture, which is one of the deep learning algorithm developments for facial recognition. AlexNet has 8 convolution layers so that it will not leave even the slightest feature of the object. The process of training and testing the system uses the MATLAB programming language. The number of datasets used is 400 image data which is divided into 360 training image data and 40 test image data. The 400 data come from 4 classes of facial images that have been labeled with names and each classes have 100 images. The training process produces an accuracy of 100% and the testing process produces an accuracy of 95%.
Co-Authors Denny Hardiyanto Dyah Anggun Sartika Imam Junaedi