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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 66 Documents
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Search results for , issue "Vol 37, No 1: January 2025" : 66 Documents clear
Building knowledge graph for relevant degree recommendations using semantic similarity search and named entity recognition Zineb, Elkaimbillah; Zineb, Mcharfi; Mohamed, Khoual; Bouchra, El Asri
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.pp463-474

Abstract

Career guidance is a critical and often daunting process, particularly during the transition from high school to higher education within the Moroccan education system. Faced with a vast array of university programs and career options, students frequently struggle to make informed decisions that align with their aspirations and skills. To address this challenge, our research introduces an innovative system that combines semantic similarity search with knowledge graph (KG) construction to enhance the precision and personalization of academic recommendations. By utilizing Sentence-BERT (SBERT) for semantic similarity, we generate embedding vectors that capture nuanced relationships between student profiles and degree descriptions. Subsequently, named entity recognition (NER) is applied to extract essential information such as skills, fields of study, and career opportunities from these profiles and descriptions. The extracted entities and their interrelationships are then structured into a coherent KG, stored in a Neo4j database, enabling efficient querying and visualization of complex data connections. This approach provides a transparent and explainable framework, ultimately delivering tailored advice that aligns with students’ individual needs and educational goals.
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.
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%.
Enhancing malware detection through self-union feature selection using gray wolf optimizer Abualhaj, Mosleh M.; Shambour, Qusai Y.; Abu-Shareha, Ahmad Adel; Al-Khatib, Sumaya N.; Amer, Amal
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.pp197-205

Abstract

This research explores the impact of malware on the digital world and presents an innovative system to detect and classify malware instances. The suggested system combines a random forest (RF) classifier and gray wolf optimizer (GWO) to identify and detect malware effectively. Therefore, the suggested system is called RFGWO-Mal. The RFGWO-Mal system employs the GWO for feature selection in binary and multiclass classification scenarios. Then, the RFGWO-Mal system uses a novel self-union feature selection approach, combining features from different subsets of binary and multiclass classification extracted using the GWO optimizer. The RF classifier is then applied for classifying malware and benign data. The comprehensive Obfuscated-MalMem2022 dataset was utilized to evaluate the suggested RFGWO-Mal system, which has been implanted using Python. The suggested RFGWO-Mal system achieves significantly improved results using the novel self-union feature selection approach. Specifically, the RFGWO-Mal system achieves an outstanding accuracy of 99.95% in binary classification and maintains a high accuracy of 86.57% with multiclass classification. The findings underscore the achievement of a self-union feature selection approach in enhancing the performance of malware detection systems, providing a valuable contribution to cybersecurity.
Design of stress detector with fuzzy logic method (GSR and heart rate parameters) Fajrin, Hanifah Rahmi; Sasmeri, Sasmeri; Prilia, Levina Riski; Untara, Bambang
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.pp56-68

Abstract

Stress is a condition of tension that affects emotions, thought processes, and the physical or psychological state of humans due to pressure from within or from outside a person, which can interfere with activities that can cause various diseases. Therefore, a tool is made to detect stress levels so that a person can monitor their condition and prevent stress from getting more severe and detrimental to the health of the body and mind. The stress level detection tool is designed using a galvanic skin response (GSR) sensor to detect skin response through a person's sweat glands and a heart rate sensor to detect heart rate. Furthermore, the reading results will be processed by microcontroller and then the stress level decision will be made using the fuzzy logic method and will be classified into Relax, Anxiety, Calm, and Stress. Based on the test results, the GSR parameter has the highest accuracy of 99.78%, and the heart rate parameter has the highest accuracy of 99.63%.
Attention based English to Indo-Aryan and Dravidian language translation using sparsely factored NMT Dwivedi, Ritesh Kumar; Nand, Parma; Pal, Om
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.pp250-256

Abstract

Neural machine translation (NMT) is a sophisticated technique that employs a large, singular neural network to learn and execute automatic translation tasks. Unlike statistical machine translation systems, NMT handles the entire translation process in an end-to-end manner, removing the need for additional components. This approach has shown significant promise in translation quality and has become the prevalent method. In this study, we apply sparsely factored NMT to English and several Indo-Aryan (Hindi, Bengali) and Dravidian (Tamil, Malayalam) language pairs. Specifically, we develop the machine translation system using an attention-based mechanism. A significant problem with traditional transformers is the huge memory requirement. Therefore, a sparsely factored NMT (SFNMT) is used to reduce the memory requirement but also improves the training time, thereby, reducing the computing time. In this paper, take inspiration from Vaswani transformer and modify it to get the best results. The system’s performance was evaluated using the BLEU metric. The proposed model indtrl achieves a BLUE score of 32.13 (en→hi), 29.31 (en→be), 31.21 (en→ta), 21.12 (en→ml) and 32.67 (en→hi), 29.38 (en→be), 31.75 (en→ta), 21.17 (en→ml) without backtranslation and with backtranslation. To evaluate the performance of the system, we compared the results with those of existing systems. The developed system demonstrated a marginally higher BLEU score than both AnglaMT and Google translate.
Internet of things enabled landfill pollution gas monitoring Junus, Mochammad; Putradi, David Fydo; Soelistianto, Farida Arinie; Anshori, Mohammad Abdullah; Ardiansyah, Rizky
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.pp48-55

Abstract

Due to the increasing concern on how to manage wastes and ensure environmental safety across the globe, a new tool that assists in the monitoring of methane, humidity, and temperature in the landfill using internet of things (IoT) has been created. This system uses ESP32 microcontroller and MQ-4 and DHT-22 sensors to measure environmental conditions at three different spots in a landfill. The samples of data are collected at three times a day, that is, in the morning at 7:00 am, at midday at 12:00 pm and in the evening at 5:00 pm and the data is transmitted to an online sheet where the public can access it in real time hence increasing transparency in the management of wastes. The tool shows a very good precision and effectiveness and the parameters are 94. 6% data integrity over three months testing period. The first findings show that the mean methane concentration is the highest at midday, which is related to the temperature and underlines the role of temperature in the methane emission process. The presented IoT based monitoring system also enhances the accuracy and efficiency in the monitoring of landfill gas and at the same time reduces the intervention of human effort and increases the capability to make prompt adjustments to changes in the environment. Used as an instrument for obtaining accurate and easily understandable data, it is hoped that this tool will in some way help to enhance global environmental health and safety standards, and help pave a way for methane storage for renewable energy purposes.
Optimizing carrier transport properties in the intrinsic layer of a-Si single and double junction solar cells through numerical design Prayogi, Soni; Hamdani, Dadan; Darminto, Darminto
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.pp69-77

Abstract

This research aims to improve the performance of a-Si: H solar cells, particularly in terms of carrier transport properties, through a numerical design approach utilizing AFORS-HET simulation software. By performing a series of rigorous computer simulations, we explore the potential regulation of the intrinsic layer thickness, carrier mobility, loading factor, and density of states (DoS) distribution in the solar cell's intrinsic layer. Recombination losses are reduced, and light absorption efficiency is significantly increased when the intrinsic layer thickness is adjusted, as shown by simulation findings. Moreover, reduction of transit times and enhancement of the total efficiency of the solar cells depend on increased carrier mobility. Parameters can be adjusted to attain optimal performance under various operating situations by adjusting the DoS and load factors. Furthermore, the simulations provide insightful information about the interactions between the junctions in solar cells with double junctions. Our results of this research provide an important contribution to efforts to develop more efficient and sustainable a-Si: H solar cells and emphasize the importance of numerical design approaches in photovoltaic technology.

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