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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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Innovative automation and optimization of solar-powered water purification using siemens programmable logic controller and human-machine interface Bouraiou, Ahmed; Dekhane, Azzeddine; Benghanem, Mohamed; Rahli, Chouaib
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1285-1297

Abstract

This study presents a novel approach to optimizing water purification systems at the Zaouiet Kounta solar power plant through the integration of advanced automation and supervision technologies. By utilizing a siemens programmable logic controller (PLC) and human-machine interface (HMI) programmed via the totally integrated automation (TIA) Portal software, the project aimed to significantly enhance the performance of water production and distribution systems. The objectives included improving operational efficiency, reducing manual intervention, and increasing system reliability and precision. The results presented herein show significant improvements in operational efficiency, system reliability, and automation in a challenging environmental context. This research provides a comprehensive case study that not only highlights the feasibility of using Siemens PLC and HMI systems in solar-powered water purification systems but also proposes scalable solutions for similar industrial applications.
Enhancing cybersecurity in 5G networks systems through optical wireless communications Alodat, Iyas Abdullah; Al-Khateeb, Shadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp250-257

Abstract

In this paper we will discuss with the recent global deployment of 5G networks, it has become imperative to ensure secure and reliable communications in addi tion to basic responsibility. Given that standard radio frequency (RF) communi cations have security flaws such as eavesdropping, signal jamming, and cyber attacks, wireless optical communications (WOC) offers a viable alternative. Us ing technologies such as visible light communications (VLC) and the free space optics (FSO) technologies, 5G networks can enhance the speed and efficiency of data transmission, while simultaneously enhancing cyber security. In addition to discussing the advantages of wireless on-chip communication technology com pared to RF solutions and the challenges that need to be addressed, this paper examines how WOC technology can enhance cyber security in 5G networks.
Optimization of photovoltaic pumping system using neuro fuzzy inference system ANFIS control technique Abdelhaq, Laoufi; Moulay-Idriss, Chergui; Chekroun, Soufyane
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1270-1284

Abstract

In recent years, artificial intelligence has become increasingly used due to the development of microcontrollers. In this paper, we propose an intelligent technique that employs the adaptive neuro-fuzzy inference system (ANFIS). We use this approach to improve the conventional direct torque control (DTC), which relies on a PI controller for the induction machine, and to enhance the conventional MPPT control based on the Perturb and Observe algorithm. The overall goal is to improve the performance of the photovoltaic pumping system. In this work, we apply ANFIS control to maximum power point tracking (MPPT-ANFIS). Additionally, we simultaneously optimize the efficiency of the DTC by applying ANFIS control (DTC-ANFIS). We present the results by comparing the photovoltaic pumping system using ANFIS control with the conventional photovoltaic pumping system, using MATLAB/Simulink. The results show that ANFIS control significantly improves the photovoltaic system compared to the conventional control, offering excellent dynamic performance of the induction motor and better utilization of photovoltaic solar energy. However, the ANFIS has some drawbacks, such as high computational time consumption and challenges in implementing a database.
Predictive control strategy for a novel 15-level inverter with reduced power components El Ansari, Taoufiq; El Gadari, Ayoub; Ounejjar, Youssef
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp33-44

Abstract

This paper proposes a novel fifteen-level H-PTC inverter topology controlled by model predictive control (MPC), which reduces the number of components. The design employs only two DC sources, nine switches, including one bidirectional switch, and a single capacitor. The system’s performance is validated through MATLAB/Simulink simulations under various scenarios, such as steady-state operation, load variations, nonlinear loads, and sudden supply voltage disturbances. Compared to existing topologies, the proposed inverter demonstrates hardware simplicity, high output quality, and enhanced dynamic robustness. Notably, it features very low total standing voltage (TSV) and a minimized cost function value of 2.05. For a load characterized by R = 20 Ω and L = 20 mH, the total harmonic distortion (THD) of the load current is 0.88%, confirming excellent power quality without the need for output filters. The MPC controller ensures a fast dynamic response and strong adaptability, making this topology ideal for modern energy conversion applications.
Incipient anomalous detection in a brain using the IBIGP algorithm Nait Chalal, Mohamed Hichem; Yagoubi, Benabdellah; Henni, Sidahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp119-127

Abstract

The detection of an incipient anomalous growth of tissue in a brain is often a difficult task. Various algorithms for brain anomalous detection have been suggested abundantly in the existing literature. In the last decade, many detection methods have been suggested to improve and facilitate abnormal tissue detection. However, the most attractive techniques to many researchers are maybe those that are magnetic resonance imagery (MRI)- based algorithms. A technique known as the inverse of the belonging individual Gaussian probability (IBIGP) is applied to MRI in this work in order to mitigate incipient anomalous tissue detection in a brain. This study demonstrates that the IBIGP technique, applied to the MRI image, is extremely effective in early detecting an anomalous change in the brain MRI image. Although this technique is still in its infancy, it has a great potential to enhance brain anomalous early detection.
The role of artificial intelligence in advancing the performance of information retrieval Alrabea, Adnan; Ahmad Alhaj, Abdullah; Senthil Kumar, A. V.
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1478-1485

Abstract

The motivation behind applying artificial intelligence (AI) in information retrieval (IR) is that the current methodologies include algorithms designed by researchers, leaving space for the applicability of genetic AI algorithms in IR. While different algorithms designed by developers rely on the originality or performance of the algorithm, precise results are achieved through integrating AI algorithms with traditional algorithms. The proposed methodology introduces document structure weighting with optimized performance. It is enabled by employing genetic algorithm and genetic programming for learning optimal weights in ranking document components. The Croft probabilistic ranking, vector space inner product models, and the BM25 standard were compared with each other after AI integration. Genetic algorithm and genetic programming were applied in the stemming and thesaurus forming processes of these models. Inducing genetic algorithm and genetic programming into the specified models increased the mean average precision of the Croft model and the vector space method by approximately 5% while there were no observable result improvements in BM25. It was found that applying genetic algorithm and genetic programming in learning synonyms and stemming rules, respectively, increased the overall performance of IR models, emphasizing the need for AI in IR.
Quality of services LoRaWAN satellite communication Purnama, Iwan; Dwi Putra, Muhammad Taufik; Samsinar, Samsinar; Aulia, Masyitah; Shina, Ibnu; Yuliyus Maulana, Yudi; Benny Louhenapessy, Bendjamin; Dominggus Lekalette, Johanis; Parulian Sitompul, Peberlin; Manik, Timbul; Nendra Wibawa, Lasinta Ari; Prasetyo Adi, Puput Dani; Sinaini, La; Lestari, Pratiwi; Sulaeman, Yaya; Rohman Setiawan, Iwan; Nugraha, Budi; Yati, Emi; Sadiyah, Lilis; Jatmiko, Irwan; Sacipto, Rian; Sariningrum, Ros
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i3.pp1401-1416

Abstract

This research discusses research that focuses on the capabilities of satellitebased LoRa, for satellite positions orbiting in low earth orbit (LEO). The expectation of low power wide area network (LPWAN) satellite can find the quality of transmitting data using LoRaWAN which is part of LPWAN and can provide quality of service (QoS) with high-quality real-time sensor data, low latency, long-range, low-power, no attenuation signal, no problem with obstacles in terrestrial areas, and other benefits that can be widely optimized. This article uses a comprehensive analysis of mathematical calculations as well as precise and accurate simulations for the actual development of satellite-based LPWAN. The satellite-based IoT is unlimited in terms of distance, to provide good services to all IoT users in the world. The comparison with terrestrial measurements is analyzed in detail, especially the signal attenuation factor that causes a lot of signal loss and data is not well received. Several methods are used to help reduce collision data, such as adaptive data rate (ADR) which can reduce collisions by 30%.
Development of a machine learning model with optuna and ensemble learning to improve performance on multiple datasets Efendi, Akmar; Fitri, Iskandar; Nurcahyo, Gunadi Widi
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp375-386

Abstract

Machine learning, a subset of artificial intelligence (AI) is vital for its ability to learn from data and improve system performance. In Indonesia, advancements in ML have significant potential to boost competitiveness and foster sustainable development. However, issues like overfitting and suboptimal parameter settings can hinder model effectiveness. This study aims to improve the classification performance of ML models on various datasets. Advanced techniques like hyperparameter tuning with Optuna and ensemble learning with extreme gradient boosting (XGBoost) are integrated to enhance model performance. The study evaluates the performance of K nearest neighbors (KNN), support vector machine (SVM), and Gaussian naïve Bayes (GNB) algorithms across three datasets: academic records from the Islamic University of Riau (UIR), diabetes data from Kaggle, and Twitter data related to the 2024 elections. The findings reveal that the GNB algorithm outperforms KNN and SVM across all datasets, achieving the highest accuracy, precision, recall, and F1-score. Hyperparameter tuning with Optuna significantly improves model performance, demonstrating the value of systematic optimization. This study highlights the importance of advanced optimization techniques in developing high-performing ML models. The results suggest that robust algorithms like GNB, combined with hyperparameter tuning and ensemble learning, can significantly enhance classification performance.

Page 914 of 914 | Total Record : 9138

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