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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING
Ginting, Manan
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Computer Science & IT Electrical & Electronics Engineering

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Sistem Start Direct Online Motor Induksi 3 Phase Berbasis Internet of Things Tarigan, Enda Rasilta; Rahmansyah, Abdul Azis; Gultom, Golfrid; Ginting, Manan
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol. 8 No. 1 (2024): Journal of Electrical and System Control Engineering
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v8i1.12624

Abstract

Digital technology continues to develop until now such as in Industry 4.0. In the industry, many still use conventional systems such as Induction Motor Starters using the Direct on Line (DOL) System. This study aims to build a system to be able to upgrade conventional systems to digital-based systems. The method used is testing by making a design and then comparing the data obtained to see the response time. From the results of the study, the system can work well. Both testing on conventional systems and online systems. In conventional systems, the system works faster with an average response time of less than 1 second, while with an online system, the response depends on the internet network used so that an average of 1.6 seconds is obtained. In addition, the results of testing using internet of things software show that the system is integrated between conventional systems and digital systems where when the induction motor is ON, a current reading of around 60A will be obtained on the IoT software display and when the motor is OFF, the current reading will drop to close to 0 Ampere.
Comparison of Activation Functions on Convolutional Neural Networks (CNN) to Identify Mung Bean Quality Karo Karo, Ichwanul Muslim; Karo Karo, Justaman Arifin; Ginting, Manan; Yunianto, Yunianto; Hariyanto, Hariyanto; Nelza, Novia; Maulidna, Maulidna
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13107

Abstract

Mung bean production levels by farmers in Indonesia are not stable. When there is a surplus, the stock of mung beans in the warehouse will accumulate, the storage factor affects the quality of mung beans. Indicators of quality mung beans can be seen from the color and size through direct observation. However, the aspect of view and assessment and the level of health of each observer is a human error in the classification of mung bean quality so that the results are less than optimal. One alternative way to identify object quality is to use deep learning algorithms. One of the popular deep learning algorithms is convolution neural network (CNN). This study aims to build a model to classify the feasibility of mung beans. The process of building the model also goes through the image preprocessing stage. In the process of building the model, there are ten setup parameters and four setup data used to produce the best model. As a result, the best CNN algorithm model performance is obtained from data setup I, with accuracy, precision, recall and F1 score above 75%. In addition, this study also analyzes Rel U and Adam activation functions on CNN algorithm on model performance in identifying mung bean quality. CNN algorithm with Adam activation function has 92% accuracy, 92.53% precision, 91.9% recall, and 92.19% F1 score. In addition, the performance of CNN algorithm with Adam activation function is superior compared to CNN algorithm with Adam activation function and previous study
Co-Authors Gultom, Golfrid HARIYANTO HARIYANTO Ichwanul Muslim Karo Karo Karo karo, Justaman Arifin Maulidna, Maulidna Nelza, Novia Rahmansyah, Abdul Azis Tarigan, Enda Rasilta Yunianto Yunianto