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International Journal of Electrical and Intelligent Engineering
Published by Universitas Islam Negeri Maulana Malik Ibrahim Malang
ISSN : -     EISSN : 31107079     DOI : https://doi.org/10.18860/ijeie
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
International Journal of Electrical and Intelligent Engineering is an open access journal. The International Journal of Electrical and Intelligent Engineering IJEIE is a scholarly journal with a strong presence in Asia and seeks to engage a global audience. The journal mission is to promote the convergence of electrical engineering intelligent systems and automation technologies across various engineering disciplines. It aims to publish pioneering research in electrical engineering electronics engineering sensor networks robotics automation and intelligent systems. The journal invites research papers and literature reviews that explore both theoretical innovations and practical engineering implementations. Special attention is given to innovative technologies system architectures and the application of smart technologies in real world engineering environments. The IJEIE is a resource for academics engineers system architects and industry professionals specializing in electrical engineering robotics automation sensor technologies and intelligent systems. The topics covered include electrical engineering technologies electronics systems automation and control systems robotics sensor systems power systems intelligent control smart grid IoT industrial IoT machine learning artificial intelligence cyber physical systems embedded systems intelligent transportation renewable energy smart manufacturing human machine interaction and advanced materials.The topics covered by the International Journal of Electrical and Intelligent Engineering include, but are not limited to, the following: Electrical engineering technologies Electronics engineering systems Automation and control systems Robotics and robot applications Sensor systems and applications Power systems control and distribution Intelligent control systems Smart grid technologies IoT and industrial IoT applications Machine learning and AI for engineering systems Cyber-physical systems Embedded systems and real-time computing Intelligent transportation systems Renewable energy technologies Smart manufacturing systems Automation in manufacturing Advanced fabrication technologies Human-machine interaction Intelligent systems in healthcare Robotic process automation in industries Advanced materials and electronic components
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 12 Documents
 1   2 
Development of an Intelligent Human Following Robot as an Automated Shopping Assistant Al Fathir Zaki Mustofa
International Journal of Electrical and Intelligent Engineering Vol 1, No 2 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i2.39891

Abstract

The human following robot technology presented in this research offers an innovative solution for modern shopping needs. Designed to automatically follow human movement within a distance of 1–15 cm, the robot stops when the distance exceeds this range, ensuring safe operation. As shopping activities in the modern era increasingly demand efficiency and convenience, this robot has strong potential to function as an environmentally friendly personal assistant, helping users carry groceries in supermarkets and malls without relying on plastic bags. The system is built using an Arduino Uno as the central controller, supported by a motor driver, DC motors, wheels, a servo motor, an ultrasonic sensor, and infrared sensors. The ultrasonic sensor measures human distance, while infrared sensors detect the position of objects on the right and left sides. Sensor data is processed to adjust motor direction and speed, enabling the robot to follow users safely. The development of this robot is expected to enhance shopping experiences and support broader applications of robotics in daily life.
Effect of Using PID Control in Switched Inductor Boost Converter Mochamad Shofwan Rizqulloh; Unggul Wibawa; Lunde Ardhenta; Alisa Zahrani Farady Daud
International Journal of Electrical and Intelligent Engineering Vol 1, No 1 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i1.34206

Abstract

Among the different kinds of boost converters is the switched inductor boost converter. But the Switched Inductor Boost Converter still has drawbacks, like output voltage results that still overshoot, a lengthy time to attain a steady state, and output voltage that fluctuates in response to input voltage changes. This shortcoming can be addressed in this investigation by using a PID controller. The PID parameter is obtained using the Direct Synthesis method, which includes Kp, Ki, and Kd. The state space averaging method is employed for the converter modeling. By applying the PID controller to the Switched Inductor Boost Converter while simulation is carried out using MATLAB-Simulink, the output voltage's transient response is improved where PID control can eliminate overshoot on output voltage response and speed up settling time by 1.35 times and also improves system's resistance to variations in input voltage and load value, allowing it to sustain the output voltage and lower momentary voltage change up to 68.3045% and speeds up recovery time up to 2.5287 times faster when input voltage changes occurs and lower overshoot value up to 39.2809% and speeds up recovery time up to 2.708 times faster when load changes occurs.
Design and Evaluation of a Blockchain-Based Smart Contract Model for Wellness Tourism Services Sunu Jatmika; Samsul Arifin
International Journal of Electrical and Intelligent Engineering Vol 1, No 2 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i2.38690

Abstract

Spa services in wellness tourism often face limitations in transparency, service integration, and customer trust in operational flows. This study develops a blockchain-based smart contract model that integrates five key indicators: reservations, cancellations, customer satisfaction, inventory, scheduling, and finance. A literature review of 113 articles yielded 25 key references, with significant trends such as the occurrence of the keyword “customer reservation” 10,100 times (2020–2024). Linear regression, correlation analysis, and ANOVA methods were used to test the research results. Linear regression predicts the relationship between variables, while correlation measures the strength of the relationship. The calculation results show a Pearson correlation coefficient of 0.93 (α = 0.05), indicating a very strong linear relationship. ANOVA shows significant differences between groups. These findings confirm that blockchain-based smart contracts are effective in digitally automating spa service workflows, strengthening transparency, and improving customer satisfaction
Deepfake Image Detection Using Transfer Learning Method Tsalatsatun Nur Rohmah; Dewi Purnamasari; Kurniawati Kurniawati; Didin Herlinudinkhaji; Yunifa Miftachul Arif; Santiago Criollo-C
International Journal of Electrical and Intelligent Engineering Vol 1, No 2 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i2.40796

Abstract

The development of Artificial Intelligence (AI) technologies, particularly deep learning has led to the emergence of innovative applications such as deepfake technology, which enables the realistic manipulation of digital images and videos. While this technology offers positive applications in fields such as entertainment and education, it also poses significant risks of misuse, particularly in the dissemination of false information and violations of privacy. Therefore, deepfake detection has become a crucial aspect in preserving the authenticity of digital content. This study aims to analyze the effectiveness of transfer learning methods in detecting deepfake images using VGG16, VGG19, and ResNet50 architectures. The research employs a dataset of deepfake and real images sourced from Kaggle, comprising 10,826 facial images with a resolution of 256 × 256 pixels, evenly balanced between authentic and manipulated content. The data are split in an 80:20 ratio for training and testing purposes. Each model is trained using identical parameter configurations. The performance evaluation of the models was conducted using confusion matrix metrics, including accuracy, precision, recall, and F1-score. The results indicate that the VGG16 model achieved the best performance, with an accuracy of 76%, followed by VGG19 at 72%, and ResNet50 at 58%. VGG16 also outperformed the other models in terms of precision, recall, and F1-score, demonstrating more effective performance in identifying visual manipulation patterns. In contrast, ResNet50 exhibited the lowest performance, which may be attributed to its architectural complexity not being optimally aligned with the characteristics of the dataset. It can be concluded that the transfer learning approach using the VGG16 model is more effective in detecting deepfake images on this dataset. This study also highlights the importance of selecting appropriate architectures and fine-tuning models to the characteristics of the data.
Blind Spot Detection to Prevent Serious Accidents for Children in Cyclists Utari Sanaba; Virhan Mujahid Syufie; Rasyeedah Binti Mohd Othman; Muhammad Richard Dzaki Dharma
International Journal of Electrical and Intelligent Engineering Vol 1, No 1 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i1.34781

Abstract

Bicycles are a mode of transportation used not only to move a person from one point to another but also commonly as a means of exercise. The lack of a safety system on bicycles increases the risk of accidents and theft, especially for children who have not yet developed stable emotional control while cycling. The use of ultrasonic sensors can be applied as detectors to provide early warnings when a cyclist is about to switch lanes, considering the wide blind spot area for cyclists. In this research, the system includes an application that can control a smart lock and the boundaries of the detector using the TCP/IP protocol via SIM900A, allowing the bicycle owner to manage the device's condition and monitor the bicycle's location. After going through several stages of testing and analyzing the results, it can be concluded that the transmission of GPS position data was successfully stored and accurately reflected in the database, with errors of 0.0012614% and 5.41295E-05% for latitude and longitude data, respectively. The blind spot detector successfully measured the distance of walls and motor vehicles located within the cyclist's blind spot area with an accuracy of 85.54%, and the safety system—comprising the lock and LED indicator—was also successfully activated when a vehicle approached the cyclist within the blind zone.
Plug-in Electric Vehicle Charging Station Placement using Hybrid Genetic Algorithm-Particle Swarm Optimization Ardhito Primatama; Hadi Suyono; Rini Nur Hasanah
International Journal of Electrical and Intelligent Engineering Vol 1, No 2 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i2.33951

Abstract

Plug-in electric vehicles are seen to be one way to address environmental problems. Plug-in electric vehicle penetration causes additional issues for the distribution network as the network's load rises. When determining the best location and management plan for a charging station, the primary considerations are power loss, voltage stability, and distribution network dependability. Roads and electrical grids are involved in the challenging task of charging station planning. The charger placement problem examined in this paper was resolved using the Hybrid between Genetic Algorithm and Particle Swarm Optimization (HGAPSO). The HGAPSO strikes an excellent mix between exploration and exploitation. Moreover, HGAPSO reduces the possibility of getting trapped in local optima and early convergence. In comparison to other metaheuristics like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), simulation results demonstrate the effectiveness of the HGAPSO in resolving the charger location problem
Android-Based Weed Identification and Herbicide Recommendation Using Convolutional Neural Networks Ahmad Izzuddin; Ryan Prayuga Ardiansyah; Andrik Sunyoto; Dyah Ariyanti; Ira Aprilia
International Journal of Electrical and Intelligent Engineering Vol 1, No 2 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i2.40594

Abstract

Weed infestation reduces crop yield and quality, while inappropriate herbicide selection often limits effective control. This paper presents the design and implementation of an Android-based decision-support application for weed identification and herbicide recommendation using a smartphone camera. Weed images are classified using a lightweight Convolutional Neural Network with a MobileNetV2 architecture optimized for mobile deployment. Herbicide recommendations are generated using the Cosine Similarity method to associate identified weed characteristics with suitable control agents. The system is modeled using the Unified Modeling Language (UML) to ensure modularity and scalability. Experimental results show that the proposed CNN model achieves a classification accuracy of 96%. The integrated on-device image acquisition and intelligent recommendation enable practical field deployment, providing an efficient tool to support weed management decisions.
Improving Random Forest Performance for Botnet Attack Detection in IoT Big Data Using Remove Frequent Values Filter Imam Marzuki; Mas Ahmad Baihaqi; Hartawan Abdillah; Dwi Iryaning Handayani; Nurhidayati Nurhidayati
International Journal of Electrical and Intelligent Engineering Vol 1, No 1 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i1.34533

Abstract

This research aims to enhance the performance of the Random Forest algorithm in classifying big data within the Internet of Things (IoT) domain, specifically for detecting botnet attacks. The study utilizes the N-BaIoT dataset, comprising 150,000 instances of IoT network traffic categorized into normal and anomalous (botnet) data. To optimize classification outcomes, a preprocessing technique—the “remove frequent values” filter—is applied to reduce redundancy and improve computational efficiency. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that this filter improves classification accuracy from 99.976% to 99.998%, with precision, recall, and F1-score all reaching 1.000. Cross-validation was conducted to ensure the robustness of these results. These findings suggest that even lightweight preprocessing techniques can significantly enhance machine learning performance in IoT big data classification tasks. 
3D Object Movement Transformation Using FPS and TPS Camera View Modes in OpenGL MAULINA SAFITRI; RAMA YUSUF MAHENDRA; Rasyeedah Binti Mohd Othman; Riffani Fathia Annisa
International Journal of Electrical and Intelligent Engineering Vol 1, No 2 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i2.34209

Abstract

Object transformation in three-dimensional space is a fundamental component in the development of interactive and realistic 3D modeling systems, particularly for control-based visual simulations and gaming applications. This study investigates the use of two camera viewpoint modes First-Person Shooter (FPS) and Third-Person Shooter (TPS) in a 3D object movement simulation implemented using OpenGL. The system is developed in Python using the Pygame library and applies basic object transformations, including translation, rotation, and scaling, based on homogeneous coordinates. Both camera modes are evaluated within the same simulation environment consisting of a car object, boundary walls, and obstacles. Experimental results show that the TPS camera mode provides better navigation performance by reducing collision frequency and offering a broader view of the environment, while the FPS camera mode delivers a more immersive experience with limited spatial visibility. Comparative graphs of navigation completion time and collision count highlight clear performance differences between the two camera modes. These results indicate that camera viewpoint selection significantly affects navigation efficiency and user experience in 3D visualization systems. The proposed simulation can serve as a foundation for visual-based control systems, virtual training environments, and educational applications involving spatial navigation.
Artificial Intelligence Application of Back-propagation Neural Network in Cryptocurrency Price Prediction Muhammad Sahi; Galan Ramadan Harya Galib
International Journal of Electrical and Intelligent Engineering Vol 1, No 1 (2025)
Publisher : Department of Electrical Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ijeie.v1i1.33800

Abstract

This study explores the use of Deep Learning and Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), for cryptocurrency price prediction. Given the high volatility of crypto markets, traditional models often underperform. A backpropagation-based ANN with a 7-5-1 architecture is proposed and tested using historical Bitcoin data. The model achieves high accuracy, with a Mean Squared Error (MSE) of 4.0431e-04, equivalent to 99.96% accuracy, demonstrating its ability to capture complex nonlinear patterns. However, overfitting remains a concern, emphasizing the need for robust generalization and feature selection. The results validate the potential of ANN in crypto forecasting and encourage further research using diverse features and assets.

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