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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 30 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): June : International Journal of Electrical Engineering, Mathematics and Compute
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): June : International Journal of Electrical Engineering, Mathematics and Compute
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): June : International Journal of Electrical Engineering, Mathematics and Compute
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): June : International Journal of Electrical Engineering, Mathematics and Compute
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): September : International Journal of Electrical Engineering, Mathematics and Co
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.
Implementation of the K-Means Method for Segmentation of Student Data Based on Learning Style: A Case Study in the Informatics Study Program Gunawan Prayitno
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 3 (2025): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i3.309

Abstract

Adapting to students’ learning styles is a key factor in enhancing the effectiveness of higher education, particularly in Informatics programs where learning preferences vary widely. This study aims to segment students based on their learning styles using the K-Means clustering algorithm, guided by the VARK model (Visual, Auditory, Read/Write, Kinesthetic). Data were collected from 130 Informatics students, including information on their learning preferences, and processed through normalization techniques. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, and subsequent cluster interpretation was conducted. The results identified three dominant clusters, each representing distinct learning behavior patterns. These clusters were analyzed to recommend tailored instructional strategies for each group. Specifically, Visual learners were found to benefit from graphic-heavy materials, Auditory learners preferred lectures and discussions, Read/Write learners thrived on written content and detailed notes, while Kinesthetic learners responded best to hands-on activities. The findings support the development of adaptive, data-driven teaching approaches that align with the actual learning tendencies of students in Informatics. Moreover, the study demonstrates that the K-Means method is effective in systematically identifying student learning profiles, which can be used to inform instructional improvements. This personalized approach to teaching could significantly enhance learning outcomes by providing students with the most effective educational experiences tailored to their individual learning styles
UI/UX Design of the Bangkitku Waste Bank Information System in Jambi City Using Design Thinking Sopia Ranty; Reni Aryani; Zainil Abidin; Noneng Marthiawati; Winny Laura
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 1 (2025): March : International Journal of Electrical Engineering, Mathematics and Comput
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i1.310

Abstract

Waste management is increasingly critical due to the rising waste generated by community activities driven by a consumptive lifestyle. A key solution to this issue is the implementation of waste bank programs, though community participation and operational efficiency remain challenges, as seen with the Bangkitku Waste Bank in Jambi City. This study focuses on designing the user interface for the Bangkitku Waste Bank Information System using the design thinking method and evaluating the usability of the prototype. The design process followed the five stages of design thinking: empathize, define, ideate, prototype, and test, with data collected through interviews and observations. The analysis involved tools such as empathy maps, user personas, sitemaps, and user flows, with prototypes created using Figma. Usability testing was conducted with 10 participants, including administrators and customers, resulting in high usability scores—98 for administrators and 97 for customers. The majority of participants found the system easy to use, as indicated by responses on the Single Ease Question (SEQ) survey. The prototype met key usability criteria, improving both operational efficiency and community engagement in waste bank management. The findings demonstrate the system's potential to foster sustainable environmental practices and enhance the effectiveness of waste bank management.
Analysis of the Impact of Urban Sprawl on Groundwater Reserves in Kendari City Using Google Earth Engine (2000–2024) Asramid Yasin
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 3 (2025): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i3.311

Abstract

This study analyzes the impact of urban sprawl on groundwater reserves in Kendari City using the platform Google Earth Engine (GEE) with analysis period of 2000 and 2024. Urban sprawl is characterized by an increase in built-up land area estimated through the Normalized Difference Built-Up Index (NDBI), while groundwater reserves are projected through estimated baseflow groundwater runoff obtained from FLDAS ( Famine Early Warning Systems Network Land Data Assimilation System ) data. The results show a significant increase in NDBI values from 2000 to 2024, indicating a massive expansion of built-up areas. Conversely, baseflow values have decreased consistently, with the average baseflow decreasing from 0.00002685 kg/m²/s (2000) to 0.00001894 kg/m²/s (2024), reflecting pressure on the aquifer system due to reduced infiltration areas. Pearson correlation analysis revealed a significant weak negative effect between NDBI and baseflow in 2000 (r = -0.219; p-value = 0), which changed to a weak positive effect in 2024 (r = 0.126; p-value = 0), indicating a shift in hydrological dynamics due to the accumulated impacts of urbanization. This finding confirms that urban sprawl has reduced groundwater recharge capacity and threatened the sustainability of clean water supplies. The study recommends the need for sustainable spatial planning policies and groundwater conservation strategies to mitigate these negative impacts.
Smart Protection System in Power Distribution Using Internet of Things (IoT) Technology Prasetyo, Yuli; Kumala Mahda H; R. Oktav Yama H; Narava Kansha P
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 3 (2025): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i3.312

Abstract

The reliability of power distribution systems is a crucial factor in ensuring stable electricity supply for industrial, commercial, and household users. Conventional protection systems often face limitations in terms of real-time monitoring, remote control, and adaptive responses to fault conditions, which can result in longer outage durations and higher operational costs. This research aims to develop a smart protection system for power distribution using Internet of Things (IoT) technology to enhance system reliability. The proposed method integrates IoT-enabled sensors, microcontrollers, and communication modules to monitor critical parameters such as voltage, current, and frequency in real time. Data are transmitted to a cloud-based platform for analysis and decision-making, enabling rapid detection of abnormalities and remote tripping of circuit breakers. The prototype was tested under various fault scenarios, including short circuits and overloads, and demonstrated faster response times compared to conventional systems. Results show that the IoT-based protection system improved fault detection accuracy, reduced downtime, and provided predictive maintenance insights through data analytics. The synthesis of these findings highlights that integrating IoT into protection mechanisms not only increases operational reliability but also supports the transition toward smart grids. In conclusion, the developed system proves effective in addressing the limitations of traditional protection systems by offering real-time monitoring, automation, and enhanced decision-making for modern power distribution networks.
Analysis of the Impact of Load Imbalance on Neutral Current and Power Losses Caused by Neutral Current in Transformer 1, 30 MVA, 70/20 kV at Bungaran Substation Azis, Abdul; Perawati; Yudi Irwansi; Muhammad Rizal
International Journal of Electrical Engineering, Mathematics and Computer Science Vol. 2 No. 3 (2025): September : International Journal of Electrical Engineering, Mathematics and Co
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijeemcs.v2i3.318

Abstract

Power transformers are crucial in the electrical distribution system, and their operational stability is significantly affected by load imbalance among phases. Load imbalance can lead to the flow of neutral current through the neutral conductor, causing additional power losses in the transformer. This study analyzes the impact of load imbalance on neutral current and power losses at Transformer 1 (30 MVA capacity, 70/20 kV) at the Bungaran Substation. Data such as phase current, neutral current, and power losses were measured at 12:00 and 21:00. At 12:00, the transformer’s full-load current was 839.17 A with a loading of 28.44% and a load imbalance of 0.74%, resulting in a neutral current of 4.36 A (1.83% of load current). The power loss due to neutral current was 12.64 W (4.36×10-5 %), and the loss due to neutral current flowing to the ground was 760 W (2.62×10-3 %). At 21:00, the full-load current decreased to 834.46 A, with a loading of 29.36% and a higher load imbalance of 1.36%. This caused a neutral current of 7.94 A (3.24%), with a power loss of 41.90 W (1.43×10-4 %) and a ground power loss of 2.52 W (8.60×10-3 %). The power losses were minimal compared to the transformer’s capacity, having little effect on system efficiency. However, maintaining load balance is essential for system efficiency and transformer longevity.

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