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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING International Journal of Engineering, Science and Information Technology Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Ginting, Manan
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Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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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
Implementation of Gauss Elimination Method on Electrical Circuits Using Python Karo Karo, Ichwanul Muslim; Karo Karo, Justaman Arifin; Yunianto; Hariyanto; Falah, Miftahul; Ginting, Manan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 1 (2024): October 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i1.536

Abstract

Problem solving in engineering, science and other disciplines often requires complex and in-depth analysis. A problem that requires effort is determining the value of electric current in a circuit. Generally, the determination of the current value in a circuit with the calculation of Kirchoff's theorem. This research presents an alternative approach to determine the value of electric current in a circuit by combining the Gauss Elimination method and Kirchoff's theorem. The determining process support by Python. This method is effective in finding unknown values in a system of linear equations through matrix operations. A deep understanding of currents in electrical circuits is essential in the design, analysis, and maintenance of electrical systems. The application of the Gauss Elimination Method becomes important in determining the value of current in complicated electrical circuits. The Gauss Elimination Method is able to solve electrical circuit problems using matrix principles to achieve accurate and relevant solutions
Hybrid Deep Fixed K-Means (HDF-KMeans) Zuhanda, Muhammad Khahfi; Kohsasih, Kelvin Leonardi; Octaviandy, Pieter; Hartono, Hartono; Kurnia, Dian; Tarigan, Nurliana; Ginting, Manan; Hutagalung, Manahan
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.913

Abstract

K-Means is one of the most widely used clustering algorithms due to its simplicity, scalability, and computational efficiency. However, its practical application is often hindered by several well-known limitations, such as high sensitivity to initial centroid selection, inconsistency across different runs, and suboptimal performance when dealing with high-dimensional or non-linearly separable data. This study introduces a hybrid clustering algorithm named Hybrid Deep Fixed K-Means (HDF-KMeans) to address these issues. This approach combines the advantages of two state-of-the-art techniques: Deep K-Means++ and Fixed Centered K-Means. Deep K-Means++ leverages deep learning-based feature extraction to transform raw data into more meaningful representations while employing advanced centroid initialization to enhance clustering accuracy and adaptability to complex data structures. Complementarily, Centered K-Means improve the stability of clustering results by locking certain centroids based on domain knowledge or adaptive strategies, effectively reducing variability and convergence inconsistency. Integrating these two methods results in a robust hybrid model that delivers improved accuracy and consistency in clustering performance. The proposed HDF-KMeans algorithm is evaluated using five benchmark medical datasets: Breast Cancer, COVID-19, Diabetes, Heart Disease, and Thyroid. Performance is assessed using standard classification metrics: Accuracy, Precision, Recall, and F1-Score. The results show that HDF-KMeans outperforms traditional K-Means, K-Means++, and K-Means-SMOTE algorithms across all datasets, excelling in overall accuracy and F1 Score. While some trade-offs are observed in specific precision or recall metrics, the model maintains a solid balance, demonstrating reliability. This study highlights HDF-KMeans as a promising and effective solution for complex clustering tasks, particularly in high-stakes domains like healthcare and biomedical analysis.
Co-Authors Gultom, Golfrid Hariyanto HARIYANTO HARIYANTO Hartono Hartono Hutagalung, Manahan Ichwanul Muslim Karo Karo Karo karo, Justaman Arifin Kelvin Leonardi Kohsasih Kurnia, Dian Maulidna, Maulidna Miftahul Falah Muhammad Khahfi Zuhanda Nelza, Novia Pieter Octaviandy, Pieter Rahmansyah, Abdul Azis Tarigan, Enda Rasilta Tarigan, Nurliana Yunianto Yunianto Yunianto