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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
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Challenges of implementing protection systems in smart grids: a review Anwari, Sabat; Fauziah, Dini; Lidyawati, Lita
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp715-729

Abstract

Based on the emergence of increasingly advanced technology, the conventional power grid can be upgraded to a smart grid by adding bidirectional communication, computer algorithms, and equipment that uses artificial intelligence (AI). A smart grid is a revolution in the current electricity network that can control the two-way generation and transmission process by utilizing an intelligent system so that the distribution of electric power can be handled optimally and in real time. The challenge of the smart grid is that there are distributed generators and microgrids that must be controlled in real time with rapidly changing loads. To meet these criteria, several points are proposed, i.e., finding an effective procedure to construct self-healing capability; developing a protection system based on AI; and proposing a systematic procedure to realize self-healing and protection systems with the help of a multi-agent system (MAS). Multi-agent systems are one of the AI approaches. Each agent can work independently and can also communicate with one another and with other devices on the network. Agents used as models can be classified into several categories, such as grid component agents, distributed resource agents, end-user agents, failure control agents, data analysis agents, and graphical visualization agents.
Novel five-patch compact microstrip Yagi-alike antenna for Ka-band applications Kumar Singh, Raj; Mamta, Kumari; Kumar Sinha, Navin; Kumar Choudhary, Vinay
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp878-887

Abstract

This paper discusses the process of designing and fabricating a novel compact microstrip patch Yagi-like antenna having five-patch radiating element at operating frequency 31 GHz with a bandwidth of 1 GHz. The developed design aims to optimize the antenna performance. The overall dimension of the antenna being 17× 14 × 0.8 mm3, based on RT Duroid 5880 substrate having dielectric loss tangent of 0.0009 and relative permittivity 2.2. The effectiveness of the performance of proposed design was evaluated using the electromagnetic solver Ansoft high-frequency structure simulator (HFSS) and validated by the laboratory measurements on the antenna prototype. The measured results are consistent with the simulation prediction. The designed antenna achieved directional radiation and the performances with voltage standing wave ratio (VSWR) < 1.32, return loss -17 dB and gain of 6 dBi. The measured results are compared with those existing in literature. The proposed antenna design has proven very effective in terms of the intended design and parameters which make it suitable for satellite application and wireless communication.
IT risks associated with information theft in the financial system - a systematic review Cabanillas-Allca, Frank; Chaquila-Muñoz, Sebastian; Iparraguirre-Villanueva, Orlando
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1339-1351

Abstract

This research paper systematically reviews the financial system’s computer security risks associated with information theft. The objective is to explore the security risks and their implications concerning information theft in the economic system. Three research questions were formulated to identify these risks, their nature, and potential consequences to achieve this objective. Fifty-five articles obtained from reliable databases linked to both study variables were analyzed using the PRISMA methodology. To ensure the validity and reliability of the information, various filters were applied, such as year, keywords, and elimination of duplicate articles. In addition, an exhaustive reading of the content of each article was carried out, organizing all the information through a systematization matrix. After a thorough review of the research articles, mostly written in English and representing 34.55% of the total in 2023, risks associated with the financial sector were identified, including malware, ransomware, phishing, distributed denial of service (DDoS), hybrid XSS, eavesdropping, and social engineering. Geographically, India leads with 14.55% of the articles, followed by South Korea and the United States, with 12.72% each, while the other countries have lower percentages. In conclusion, these risks coincide with previous research and the consequences they generate, highlighting the importance of this type of study for the basis of scientific research.
Intelligent active and reactive power control using multi-layer neural network based MPPT controller for grid tied solar PV system under fault conditions Fatima, Mehtab; Siddiqui, Anwar Shahzad; Sinha, Sanjay Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp1-14

Abstract

The integration of renewable energy sources, particularly grid-tied solar photovoltaic (PV) systems, into the modern power grid has become increasingly prevalent. However, ensuring the reliable and efficient operation of grid-tied PV systems under various grid conditions, including fault scenarios, poses a significant challenge. In the event of grid faults or disturbances, traditional control methods often fall short in maintaining stable and reliable operation. This paper introduces a multi-layer neural network (MLNN) based MPPT controller that adapts intelligently to grid fault conditions, mitigating the impact on the grid-tied PV system's performance and providing low voltage ride through (LVRT). The research employs a detailed simulation framework on MATLAB to validate the effectiveness of the proposed controller under fault conditions. The LVRT capability of the designed system was analyzed and validated according to Indian grid code. The proposed controller leverages its capacity to learn and make real-time decisions to optimize the active and reactive power outputs of the PV system as per the grid code. Simulation results demonstrate that the proposed controller not only improves the fault tolerance of grid-tied PV systems but also enhances their performance, ensuring a stable and continuous power supply in the face of grid disturbances.
A novel RGB image steganography algorithm using type-1 fuzzy logic Dhaka, Navita; Hooda, Meenakshi; Yadav, Vinita; Gill, Sumeet
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp123-133

Abstract

Steganography aims to conceal secret data within images without affecting image quality. Traditional methods often struggle with balancing simplicity, effectiveness and payload capacity while maintaining imperceptibility. Proposed algorithm: the paper proposed a novel steganographic mshEdgeRGB_T1 algorithm that combines Mamdani fuzzy type-1 logic with the least significant bit (LSB) method. The LSB method is chosen for its simplicity and effectiveness in hiding messages. The mshEdgeRGB_T1 algorithm focuses on embedding secret messages in edge pixels, detecting more edge pixels compared to other methods, thus increasing payload capacity. Findings: the algorithm’s performance is evaluated using metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE) and histogram analysis to measure the similarity between the cover and Stego images, quantifying the level of imperceptibility. Experimental analysis demonstrates that the mshEdgeRGB_T1 algorithm offers improved payload capacity, enhanced security and reduced imperceptibility compared to many existing methods. Conclusion: the proposed mshEdgeRGB_T1 algorithm effectively balances simplicity, payload capacity and image quality, making it a better use for image steganography.
Natural smart home automation system using LSTM based on household behaviour Susantok, Mochamad; Ahmad Po’ad, Farhana; Joret, Ariffuddin; Hilwa Salsabillah, Maulina
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp758-770

Abstract

A smart home automation system (SHAS) utilizing data-driven learning is an advanced internet of things (IoT) application aimed to learn household behavior to prevent miniatur circuit breaker (MCB) trips due to overload. Unlike traditional deterministic methods, this study leverages a layered AI model, featuring real-time data collection, long short-term memory (LSTM) based learning, and an automatic control system. The LSTM classification model generates precise ON/OFF control signals sent to IoT smartplugs, optimizing appliance usage and reducing the risk of electrical overload. Data from smartplug sensors, including appliance status and environmental factors like power consumption, temperature, and humidity, were collected every minute over three months, yielding 80,818 data points. The system's performance was evaluated on three appliances: Air Conditioner, Television, and Water Pump Machine. Results showed high accuracy for Television at 98% and Water Pump Machine at 97.6%, with slightly lower accuracy for Air Conditioner at 81.9%. This demonstrates the system's effectiveness in real-world applications. The scalability and adaptability of the Natural SHAS model to different appliances and environments mark a significant advancement in smart home automation, offering a practical solution for preventing electrical overload and improving household energy management.
Analysis of LLC resonant converter performance with PIDD2 controller for electric vehicle application K., Sathya; Guruswamy, K. P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp749-757

Abstract

The key uses of the latest developments is electric vehicles (EV’s). As a result, several researchers were drawn to EV’s control to propose appropriate controllers and predicted that control engineers face a challenge when it comes to regulating the LLC resonant converter output voltage. In this regard, the study proposes a PID Type modified controller for regulation of voltage across output in LLC resonant converter. The design and control procedure of this modified proportional integral derivative double derivative (PIDD2) is explained along with EDF modeling in LLC resonant converter. This work proposes to use two controllers to drive the voltage output of a resonant converter LLC to constantly track the desired value. Proportional integral derivative controller (PID) is the first, while the PIDD2 method is the foundation of the second. Every controller has undergone simulation testing and the results are compared based on how the evaluated controllers respond dynamically in accordance with settling time, rising time and overshoot.
Apache Spark based distributed clustering for big data analytic with application to 3D road network Sethy, Rotsnarani; Mahanta, Soumya Ranjan; Panda, Mrutyunjaya
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp335-346

Abstract

The vast amount of data stored nowadays has turned big data analytics into a very promising research field. Clustering is an essential step in data analysis, widely used for classification, collecting statistics, and acquiring insights in specific domains of knowledge. However, the most of existing algorithms based on Lloyd-Forgy’s method, have an enormously huge average-case complexity while clustering data sets with a large number of features, which may be superpolynomial time (NP-hard) and are severely constrained in terms of speed, productivity, and adaptability. Aiming to improve Lloyd-Forgy’s clustering performance, K-means++ algorithms, a variety of algorithm-level optimizations which is not been well-studied, is discussed along with very promising gaussian mixture model (GMM) and soft clustering based Fuzzy C-means (FCM). Further, for fast and distributed data processing and to leverage the benefits of big data platforms, such as Apache Spark, Spark-based clustering methods are applied on three-dimensional (3D) road network data set which is collected from UCI repository. However, Spark-based clustering research is still in infancy. The distributed computation tests are conducted by allocating two core processors and one databricks unit (DBU) with 15 GB memory and measuring execution times, as well as root mean square error (RMSE), mean absolute error (MAE), clustering accuracy, and silhouette values. The results are promising and provide new research directions in the field of spark-based clustering on big data.
Classification of weather conditions based on automatic weather station data using a multi-layer perceptron neural network Indrajaya, Muhammad Aristo; Sollu, Tan Suryani; Subito, Mery; Rahman, Yuli Asmi; Saputra, Erwin Ardias
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp540-550

Abstract

Weather is one of the important elements that greatly determines human activities, especially those related to economic factors. Therefore, understanding weather conditions using weather parameters as a reference is important for human life, so a method is needed to classify weather according to its category so that the information produced can be used for various needs. Determining weather conditions in an area will not run well without a reliable method that can analyze existing weather parameters. Therefore, in this study, the weather condition classification process was carried out using the multilayer perceptron algorithm, a type of neural network (NN) algorithm. All data analyzed were weather parameter data collected by mini weather stations placed on land. The weather parameters used were temperature, humidity, air pressure, wind speed, dew point, wind chill, daily rainfall, solar radiation, and UV index. This study was conducted in Palu city, Central Sulawesi Province, Indonesia. The classification process carried out by the multilayer perceptron algorithm was carried out on the Altair AI Studio application and produced an accuracy value of 93.87%, recall of 92.33%, and precision of 91.29%.
A novel secure and energy aware LOADng routing protocol for IoT: an application to smart agriculture Sana, Touhami; Mohamed, Belghachi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1005-1013

Abstract

In the burgeoning domain of the internet of things (IoT), efficient and secure communication protocols are crucial for the seamless operation of diverse applications. This paper proposes a novel routing protocol, termed secure and energy aware LOADng (SEA-LOADng), tailored for IoT deployments in the context of smart agriculture. The protocol is designed to address the unique challenges posed by agricultural environments, including limited energy resources and the need for robust security measures. The proposed protocol leverages LOADng, a lightweight and efficient routing protocol suitable for low-power and lossy networks characteristic of IoT deployments. Through innovative energy-aware mechanisms, it optimizes the power usage of IoT devices, thus prolonging their operational lifespan and reducing maintenance overhead. Moreover, stringent security measures are integrated into the protocol to safeguard sensitive data transmitted within the IoT network. To assess the efficacy of the proposed protocol, comprehensive simulations are carried out using realistic smart agriculture scenarios. The results demonstrate significant improvements in energy efficiency compared to LOADng protocol, while maintaining robust security against hello flood attack.

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