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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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Big data and ensemble learning for effective student orientation in Morocco Badrani, Morad; Marouan, Adil; Kannouf, Nabil; Chetouani, Abdelaziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1904-1910

Abstract

Guiding high school students toward suitable educational paths is a complex challenge, particularly influenced by academic performance. In Morocco, first-year high school students in the scientific branch face a crucial decision when selecting between science mathematics (SM), physics (SF), and Science of Life and Earth (SVT) paths. This decision is critical as it can significantly impact their future academic and professional success. To address the issue of suboptimal student orientation, this study proposes an automated, personalized approach leveraging big data technology. By employing ensemble learning techniques, including random forest and neural network models, we developed a classification system to predict students’ optimal paths based on their academic performance. Our models achieved exceptional performance, with precision, accuracy, recall, and F-measure scores of approximately 98.59%, 98.60%, 98.60%, and 98.58%, respectively. This research demonstrates the potential of our approach to enhance educational support and decision-making, ultimately improving student outcomes and reducing dropout rates caused by wrong orientation.
Optimize the position of distributed generations in distribution grid by using improved loss sensitivity factor Phan, Dinh Chung; Luu, Ngoc An
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1370-1378

Abstract

This research proposed a method to determine the optimal position of distributed generations in a distribution grid. The method is improved from the loss sensitivity factor method. An algorithm is developed to determine both the position and size of distributed generations. This algorithm is validated via IEEE 33 bus distribution grid in two cases of distributed generation size including unknown size and constant size. The results were analyzed and compared to other previous algorithms including loss sensitivity factor-based algorithm and other algorithms. Results indicated the optimal position of each distributed generation to minimize the power loss. Results also indicated that with the proposed algorithm, the loss reduction rate (LRR) is the highest in comparison to that with other previous algorithms.
A hybrid SATS algorithm based security constrained optimal power flow using FACTS devices Cherukupalli, Kumar; Chinda, Padmanabha Raju
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1388-1396

Abstract

In the realm of power systems, achieving optimal operation while ensuring security remains a paramount challenge. The security constrained optimal power flow (SCOPF) problem deals with optimizing power system operations while taking into account security limitations. Flexible alternating current transmission system (FACTS) is a system consisting of static equipment used for transmitting electrical energy in the form of AC. The static synchronous series compensator (SSSC) is a specific form of series FACTS device. The unified power flow controller (UPFC) is a FACTS device that is connected in parallel and series with a transmission line. In this research, hybrid simulated annealing and tabu search (hybrid SATS) algorithm is designed to solve SCOPF problems that involve use of FACTS devices. The combination of simulated annealing and tabu search is intended to improve algorithm's pace of convergence and the quality of its solutions. Hybrid SATS with FACTS devices are used to investigate line flow limit violations during single line failures and ensure power flows remain within their security limitations. The efficacy of proposed algorithm is demonstrated through case studies utilizing IEEE 30 bus system. These case studies demonstrate algorithm's capabilities to achieve optimal and secure power system functioning to demonstrate its effectiveness.
Improving imbalanced class intrusion detection in IoT with ensemble learning and ADASYN-MLP approach Soni, Soni; Remli, Muhammad Akmal; Mohd Daud, Kauthar; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1209-1217

Abstract

The exponential growth of the internet of things (IoT) has revolutionized daily activities, but it also brings forth significant vulnerabilities. intrusion detection systems (IDS) are pivotal in efficiently detecting and identifying suspicious activities within IoT networks, safeguarding them from potential threats. It proposes a ensemble approach aimed at enhancing model performance in such scenarios. Recognizing the unique challenges posed by imbalanced class distribution, the research employs three sampling techniques LightGBM adaptive synthetic sampling (ADASYN) with multilayer perceptron (MLP), XGBoost ADASYN with MLP, and LightGBM ADASyn with XGBoost to address class imbalance effectively. Evaluation confusion matrix performance metrics underscores the efficacy of ensemble models, particularly LightGBM ADASYN with MLP, XGBoost ADASYN with MLP, and LightGBM ADASYN with XGBoost, in mitigating imbalanced class issues. The LightGBM ADASYN with MLP model stands out with 99.997% accuracy, showcasing exceptional precision and recall, demonstrating its proficiency in intrusion detection within minimal false positives negatives. Despite computational demands, integrating XGBoost within ensemble frameworks yields robust intrusion detection results, highlighting a balanced trade-off between accuracy, precision, and recall. This research offers valuable insights into the strengths with different ensemble models, significantly contributing to the advancement of accurate and reliable IDS in realm of IoT.
An improved surface solar radiation estimation model using integrated meteorological-air quality data Boottarat, Prakaykaew; Bin Salim, Mohd Azli; Photong, Chonlatee
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp347-356

Abstract

This paper proposes an improved high-precision surface solar radiation estimation model using the integration of the local meteorological data and air quality index based linear regression analysis. The proposed model was evaluated and compared to 8 conventional models and one generated by the commonly used PVsyst simulation software. The actual solar radiation, meteorological data and air quality index collected over 10 years (during 2011-2021) from standard measuring stations located at the northern zone of Thailand were used for developing the models while the collected data year 2022 were used for validating the developed models compared to the conventional models. The statistical error estimations in terms of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used for the precision evaluation. The study found that the proposed models achieved better prediction results and the highest precision for monthly estimating of solar radiation than the other models by having the highest estimation precision of 94.70-97.19% compared to 87.53-96.74% of the conventional models and 90.38-95.96% of the PVsyst program.
Optimized deep neural network based vulnerability detection enabled secured testing for cloud SaaS Vallabhaneni, Rohith; Somanathan Pillai, Sanjaikanth E. Vadakkethil; Vaddadi, Srinivas A.; Addula, Santosh Reddy; Ananthan, Bhuvanesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1950-1959

Abstract

Based on the information technology service model, an on-demand services towards user becomes cost effective, which is provided with cloud computing. The network attack is detected with research community that pays huge interest. The novel proposed framework is intended with the combination of mitigation and detection of attack. While enormous traffic is obtainable, extract the relevant fields decide with Software-as-a-service (SaaS) provider. According to the network vulnerability and mitigation procedure, perform deep learning-based attack detection model. The golf optimization algorithm (GOA) done the selection of features followed by deep neural network (DNN) detect the attacks from the selected features. The correntropy variational features validates the level of risk and performs vulnerability assessment. Perform the process of bait-oriented mitigation during the phase of attack mitigation. The proposed approach demonstrates 0.97kbps throughput with 0.2% packet loss ratio than traditional methods.
Improving communication between can-sized satellite and ground control station for accuracy of data acquisition Bin Zohari, Mohd Hakimi; Yin, Lam Hong; Bin Mokhtar, Mohd Hezri
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp197-204

Abstract

The can-sized satellite, ScoreSAT satellite is a small communications satellite. ScoreSAT helps to develop a platform for finding directions and the exact spot where a lack of communications signal occurs, as well as a real-time visual feed for analysis of communication during and after landing. The project focuses on the design of ScoreSAT and provides a real-time system for capturing real-time data during descent. The objective of the real-time system is to improve the accuracy and location of ScoreSAT data collection, which can provide pressure, humidity, temperature, altitude, latitude, and longitude readings. The main components of this platform are the hardware design that comprises the flight controller, GPS module, and telemetry kit, the software design, which are Mission Planner, and the real-time system (RTS). Based on the entire research, the compact design of the ScoreSAT and ground station was developed to provide alternative meteorological parameter monitoring to complement primary meteorology ground observation such as weather station and radiosonde and to enhance the reliability of the remote sensing observation for environmental studies concerning the factors determining the environment and atmospheric.
Battery charging system for electric vehicle Kotmire, Nayan J.; Kakade, Anandrao B.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1400-1408

Abstract

Selecting the appropriate charger for electric vehicles (EVs) is crucial for enhancing performance, with non-isolated DC-DC converters playing a significant role in charging EV batteries. The efficient conversion of input power into output as per the requirement is main perspective in the design of DC-DC converters. This paper delves into the landscape of non-isolated DC-DC converters utilized in EV charging, emphasizing their pivotal role. Additionally, it introduces a novel approach by incorporating machine learning-based pulse width modulation (PWM) control for the buck DC-DC converter. By integrating machine learning algorithms into the control scheme, the efficiency and performance of the charging system can be greatly enhanced, resulting in improved overall EV operation. This innovative application of machine learning not only optimizes charging efficiency but also enables adaptability to varying input/output conditions, ultimately leading to more efficient and effective charging processes for EVs.
Predicting vulnerability for brain tumor: data-driven approach utilizing machine learning Effendi, Yutika Amelia; Sofiah, Amila; Hidayat, Niko Azhari; Ebrie, Awol Seid; Hamzah, Zainy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1579-1589

Abstract

Brain tumors, whether benign or malignant, present a complex and multifaceted challenge in healthcare, affecting individuals across various age groups. Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating artificial intelligence (AI) technology. This research explores machine learning models such as support vector machines (SVMs), multi-layer perceptrons (MLPs), and logistic regression (LR) for the early detection of brain tumors. Evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to assess model performance. The results indicate that the SVM outperforms other models, providing a robust foundation for predictive accuracy. To enhance accessibility and usability, the research also integrates these models into a mobile application predictor. The application is beneficial for assisting individuals in early detection by identifying potential risk factors and symptoms that may lead to a brain tumor. In conclusion, the integration of machine learning through a mobile application represents a transformative approach to personalized healthcare. By empowering individuals with cutting-edge technology, this research strives to enhance early detection and decision-making regarding potential brain tumor risks and symptoms, ultimately contributing to improved patient outcomes and quality of life.
An improved PSO-based approach for the photovoltaic cell parameters identification in a single diode model Amaidi, Maria; Zaaraoui, Lassaad; Mansouri, Ali
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp749-759

Abstract

The future power of photovoltaic systems (PVS) is gaining significant attention due to its rising potential. This has resulted in a substantial amount of research emphasizing the importance of optimizing the PVS efficiency. However, the identification of PV cell model parameters remains a challenging task, mainly due to the characteristics of PV cells and their dependence on varying meteorological conditions. In this work, we present a novel methodology based on an improved new multi objective particle swarm optimization (NMOPSO) algorithm for the PV cell parameters identification. The main goal is to minimize the root mean square error (RMSE) and to calculate the series resistance (Rs) by means of its non-linear equation form. The applied algorithm uses an evolving and adaptive search strategy to enhance both speed of convergence for the parameter identification process precision. Through extensive simulations, we demonstrate that proposed approach outperforms current methods in terms of accuracy, precision, and PV parameters extraction.

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