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International Journal of Electrical Engineering, Mathematics and Computer Science
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30481910     EISSN : 30481945     DOI : 10.62951
Core Subject : Science, Education, Engineering,
Computer Science & IT Engineering Mathematics
The scope of the this Journal covers the fields of Electrical Engineering, Mathematics and Computer Science. This journal is a means of publication and a place to share research and development work in the field of technology
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 25 Documents
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PID Tuning on Sediment Detection Boat Using Simulink Muhammad Kevin Hardiansyah; Sri Arttini Dwi Prasetyowati; Bustanul Arifin
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 2 (2025): International Journal of Electrical Engineering, Mathematics and Computer Scien
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i2.254

Abstract

The PG45 DC motor is a drive system used on sediment detection boat. To achieve the desired stability and speed, it is necessary to apply a control system to the sediment detection boat drive system. Control systems need to be tuned to ensure that they function properly and are responsive to changes. In order to complement the previous research, further research was carried out focusing on determining the PID control parameters on the angular speed of the PG45 DC Motor using Simulink. The PG45 DC motor works based on the Arduino programming algorithm that has been designed so that it can rotate at a predetermined speed. This research modeled the sediment detection ship system on Simulink with a similarity rate of 94.09%. The results of this study indicate that the tuning method used, namely trial and error, produces good control on the sediment detection ship system model that has been assembled in Simulink with the value of Kp = 100; Ki = 5; Kd = 15 obtained the value of rise time = 0.2474 seconds and settling time = 0.4104 seconds and overshoot = 0.2175%%.
Comparison of Multiple Linear Regression, Backpropagation and Fuzzy Mamdani Methods in Predicting the Revenue of PLN Takengon Unit Richasanty Septima; Hendri Syahputra; Husna Gemasih
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 2 (2025): International Journal of Electrical Engineering, Mathematics and Computer Scien
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i2.263

Abstract

The performance of data mining techniques has been proven accurate in many studies, but each method in data mining techniques has different accuracy depending on the type of data that is the object of research. Methods in data mining techniques are divided into several functions, namely: clustering, association, classification, and prediction, where each data mining technique objective has a superior method. Therefore, in this case the author will compare the performance of the multiple linear regression method, and neural networks with fuzzy mamdani in predicting the income of PLN Unit Takengon. In several studies, the Backpropagation method shows the highest accuracy compared to other methods. Then the prediction model with multiple linear regression also has the highest accuracy as well as the Fuzzy Mamdani method has high accuracy too. Therefore, the purpose of this study is to compare the three methods, so that it can be determined which method has a higher accuracy value. The results of this study indicate that the Back propagation method has the highest accuracy and the lowest average error, namely a MAPE value of 5.9% with an accuracy of 94.1% and an RMSE of 14398.14, followed by the multiple linear regression method obtaining a MAPE value of 6.9% with an accuracy of 93.1% and an RMSE of 15527.41, then for Fuzzy Mamdani obtaining a MAPE value of 7% with an accuracy of 93% and an RMSE of 16077.76.
Implementation of the Extreme Gradient Boosting(XGBoost) Method in the Classification of Recipients of Habitable Housing Rehabilitation in Central Aceh Regency Ira Zulfa; Hendri Syahputra; Fitranuddin Fitranuddin; Adellia Divandariga S
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 2 (2025): International Journal of Electrical Engineering, Mathematics and Computer Scien
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i2.268

Abstract

In Central Aceh Regency, many households still live in uninhabitable conditions. The government is running a program to rehabilitate habitable houses, but the selection of recipients is still done manually, causing inefficiency and inconsistency. This study implements the Extreme Gradient Boosting (XGBoost) algorithm to classify aid recipients automatically and accurately. Using a machine learning approach, data is collected based on variables of structural conditions, building materials, ventilation, lighting, and sanitation. Hyperparameter tuning is performed to optimize model performance. The implementation results show that XGBoost is able to support fair, efficient, and transparent decision making in housing assistance programs.
Federated Hybrid CNN GRU and COBCO Optimized Elman Neural Network for Real Time DDoS Detection in Cloud Edge Environments Danang Danang; Maya Utami Dewi; Greget Widhiati
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 2 (2025): International Journal of Electrical Engineering, Mathematics and Computer Scien
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i2.293

Abstract

Improvement amount Distributed Denial of Service (DDoS) attacks in cloud infrastructure and edge computing demands solution adaptive, distributed, and efficient detection in a way computing. Research This propose an optimized Federated Learning (FL) based DDoS detection model using Centroid Opposition-Based Bacterial Colony Optimization (COBCO) to training the Elman Neural Network (ENN). The proposed architecture consists of of two components Main: on the edge node side, a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) model is used to extraction feature local from traffic data network, while on the server side, model parameters from each node are collected and used for training an optimized ENN with COBCO. Approach This aim increase accuracy detection at a time maintain efficiency local data communication and privacy. In progress experimental, model tested use three benchmark datasets: NSL-KDD, CICIDS2017, and CICDDoS2019. The preprocessing process includes feature encoding categorical, normalization numeric, class balancing using SMOTE, as well as validation cross (k-fold). Initial results show that combination of FL, CNN–GRU, and COBCO–ENN produces improvement significant in accuracy and time convergence compared to approach conventional such as PSO, GA, and non- federative models. In addition, the proposed model capable maintain performance detection tall although executed in edge environment with limitations source Power.  Study This give contribution important in development system scalable, privacy-preserving, and adaptive intelligent DDoS detection to dynamics Then cross modern network. Integration of FL and COBCO in ENN training shows potential big for used in implementation real in cloud-edge infrastructure. In addition, the proposed model demonstrates strong scalability and adaptability, making it highly suitable for dynamic and evolving network environments.
Design of Microcontroller-Based Color Detection Device Diyajeng Luluk Karlina
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 3 (2025): International Journal of Electrical Engineering, Mathematics and Computer Scien
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i3.313

Abstract

This research presents the design and testing of an automatic color detection system using TCS3200 color sensor integrated with Arduino Uno microcontroller. The system was developed and tested using Wokwi virtual simulation platform before physical implementation. The TCS3200 sensor converts RGB light intensity reflected from objects into frequency signals, which are processed by Arduino Uno to classify colors into red, green, and blue categories. The system incorporates audio feedback using DFPlayer Mini module to provide sound notifications for detected colors. Testing results show that the system can accurately detect and classify primary colors with frequency-based thresholds: red (R<48 &R>37 & G<95 & G>85), blue (G<75 & G>65 & B<33 & B>23), and green (R<55 & R>40 & B<25 & B>5). The simulation validation demonstrates stable performance with consistent color recognition capabilities, making it suitable for industrial sorting applications and assistive technology for visually impaired individuals.

Page 3 of 3 | Total Record : 25

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