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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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Hybrid energy storage system for fast and efficient electric vehicle charging Fang, Liew Hui; Fahmi Romli, Muhammad Izuan; Abd Rahim, Rosemizi bin
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.pp45-60

Abstract

The rapid adoption of electric vehicles (EVs) necessitates efficient and fast charging solutions to meet growing energy demands. This study introduces a hybrid energy storage system (HESS) designed to enhance EV charging performance. By integrating batteries and supercapacitors, the HESS leverages their complementary characteristics, optimizing energy storage and delivery. The primary problem addressed is the inefficiency and prolonged charging times of conventional EV charging infrastructure. A dynamic control strategy manages power flow between batteries and supercapacitors, significantly reducing charging times and improving system efficiency. This approach reduces battery size and optimizes power quality, utilizing a device with three 18650 lithium-ion batteries and four high-capacity supercapacitors. Simulations using MATLAB/Simulink and Proteus software demonstrate a charging time of 57 minutes for the storage system and 4.74 hours for a full EV battery charge, outperforming traditional methods. This project contributes to the design and implementation of a HESS for EVs, facilitating both efficient and fast charging capabilities.
Enhancing sales volume using machine learning algorithms Elsayed Aboutabl, Amal; Mahmoud Moawad, Ola; Mohamed Abd-Elwahab, Ahmed
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.pp1618-1629

Abstract

In today's highly competitive business landscape, companies face a significant challenge in making accurate decisions based on vast amounts of historical data. Reliance on human data analysis often leads to biases and errors, hindering the ability to extract effective insights for sales forecasting. To address this challenge, this research presents an advanced model that integrates 14 machine learning (ML) regression algorithms, including XGBRegressor and LGBMRegressor, to provide accurate sales predictions using a comprehensive global store dataset. The results demonstrate that XGBRegressor and LGBMRegressor achieved the highest test accuracy (92%) and the lowest error rates, proving their ability to handle complex prediction tasks efficiently. This high accuracy in sales forecasting enables companies to make more effective strategic decisions, such as optimizing inventory management, allocating resources optimally, and exploring new growth opportunities. Consequently, the use of these advanced algorithms directly contributes to increasing sales volume and achieving a sustainable competitive advantage.
Comparative analysis of fractional-order sliding mode and pole placement control for robotic manipulator Bennaoui, Ahmed; Benzian, Salah; Alsolbi, Idrees Nasser; Ameur, Aissa
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.pp90-98

Abstract

Fractional-order sliding mode control (FOSMC) is benchmarked against pole placement control (PPC) on a nonlinear two-link manipulator subjected to identical trajectories and 10 N·m square disturbances. Quantitative head-to-head evidence against industrial PPC is scarce, leaving engineers uncertain when fractional designs justify their added complexity. We derive the plant via Lagrange dynamics, implement both controllers in Python, and evaluate tracking and torque effort using SciPy-based simulations. Under the adopted fractional derivative approximation, FOSMC attains RMSEs of 0.458 rad (q1) and 0.453 rad (q2) whereas PPC limits the errors to 0.365 rad and 0.337 rad. The fractional design, however, requires lower mean torques of 69.2/29.0 N·m compared to PPC’s 86.1/41.4 N·m, exposing a precision–energy trade-off that now favours PPC on accuracy and FOSMC on actuation effort. The benchmark delivers deployable evidence that fractional sliding surfaces shift torque demand even when their tracking performance lags, and it motivates hardware-in-the-loop validation to close the identified accuracy gap.
A TOT: tri-optimized-tariff based strategic residential load management with greedy optimization in IEEE33-bus system: a case study with renewable energy penetration Goswami, Kuheli; Kumar Sil, Arindam
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.pp1199-1211

Abstract

The efficiency of a load management system in terms of its energy performance index (EPI) depends on its capacity to enhance the reliability, resilience, and cost effectiveness of the existing system. Artificial intelligence (AI) is crucial in this shift from classical to AI-based power system planning, optimizing renewable energy (RE) and reducing gridstress. On the other hand, proper placement of resources is essential to achieve benefits and reduce transmission losses. Utility sectors of different states has revealed that in certain areas amongst different type of loads, domestic loads accounts for a substantial proportion of energy consumption. Therefore, the present work deals with optimum load scheduling, integration of RE, energy storage (ES) and proposed tri-optimized-tariff (TOT) for prosumers. We have found that the weighted-K-nearest-neighbor (KNN) method excels in selecting features for household appliances and ES scheduling. The composite greedy optimization (CGO) technique outperforms existing methods in optimization. These results demonstrate the efficiency and real-world potential of our model. We have conducted a case study and developed an AI-based strategic-residential-load-managementsystem (SRLMS), which we have tested on the IEEE33 bus system, showing cost effectiveness and improved EPI for prosumers. This work encourages the development of a harmonious relationship between utility-sectors and prosumers.
Enhancing wind energy prediction accuracy with a hybrid Weibull distribution and ANN model: a case study across ten locations in Java Island, Indonesia Fithri, Silvy Rahmah; Hesty, Nurry Widya; Wijayanto, Rudi P.; Pranoto, Bono; Wijaya, Prima Trie; Faqih, Akhmad; Kusuma, Wisnu Ananta; Nurrohim, Agus; Sugiyono, Agus; Yudiartono, Yudiartono
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.pp180-190

Abstract

Accurate wind speed forecasting is essential for optimizing renewable energy (RE) systems, especially in coastal and island regions with high variability. This study proposes a hybrid predictive model that combines Weibull distribution parameters with artificial neural networks (ANN) to enhance forecasting accuracy. Using ten years of hourly NASA POWER data from 10 locations across Java Island, 24 scenarios were tested with varying combinations of Weibull and meteorological variables. Results demonstrate that incorporating both Weibull shape (k) and scale (c) parameters significantly improves performance, with the best configuration (Scenario 1) achieving a MAPE of 0.44% in Garut. Excluding one or both parameters sharply reduced accuracy, with errors rising up to 35.12%. Beyond technical accuracy, the findings emphasize the practical relevance of Weibull-informed ANN models for energy planning. Reliable forecasts support better wind resource assessment, grid integration, and investment decisions, reducing uncertainties that often hinder wind power deployment. By providing accurate and stable predictions across diverse locations, this approach offers policymakers and planners a robust tool to accelerate RE development and meet national energy targets.
Towards adapting the consensus proof of authentication algorithm for IoT Aghroud, Mohamed; El Gountery, Yassin; Oualla, Mohamed; El Bermi, Lahcen
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.pp439-452

Abstract

The Internet of Things (IoT) represents an increasingly sophisticated paradigm which interconnects heterogeneous devices, enabling continuous data exchange and automation. However, IoT systems face significant challenges related to scalability, limited device resources, and data security. Blockchain technology provides an effective foundation for addressing such challenges thanks to its decentralized structure and consensus algorithms. This work focuses on improving the blockchain consensus protocol or consensus algorithm referred to as proof of authentication (PoAh) for adaptation to IoT networks using smart contract. It also presents a comparison of various existing consensus algorithms and explores different blockchain open-source platforms and their adaptation to IoT. Although experimental validation remains part of future work, the conceptual design and theoretical analysis presented here lay the groundwork for the future implementation and evaluation of the improved PoAh within real IoT use cases.
Fuzzy medical expert system for prediction of prostate cancer Wantoro, Agus; Rusliyawati, Rusliyawati; Sutyarso, Sutyarso; Hadibrata, Exsa
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.pp1466-1477

Abstract

We developed the fuzzy medical expert system (F-MES) based on fuzzy inference system (FIS) Mamdani using a different approach to prostate cancer risk (PCR) prediction. The difference in our research is that we modify the membership function on the input variable according to medical standards. We used the same input variables as the previous study, namely age, prostate-specific antigen (PSA), prostate volume (PV), and percentage (%) free PSA (%FPSA). The data on the input variable is used as input into F-MES and displays the output in the form of a percentage (%) of PCR. If the PCR is >50%, then the patient is advised to undergo a biopsy test. We conducted an analysis with the doctor to create a simple domain and rule base of 24 rules. Our number of rules is lower than previous studies of 80 and 240, but our prediction results are better the F-MES evaluation used the same 56 patients, that the F-MES we developed had an accuracy of 857%. This score is better than previous studies of 75% and 76%. Our F-MES is simple but effective and can be used as a supporting tool in decision-making in medical diagnosis.
A hybrid DWT-DCT-SVD watermarking scheme using arnold transform Huynh, Van-Thanh; Nguyen, Thai-Son; Vo, Thanh-C
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.pp1659-1668

Abstract

In telemedicine, medical images and electronic patient records (EPRs) are frequently transmitted and stored, making them vulnerable to tampering and theft. To ensure data security and copyright protection, this paper proposes a hybrid watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). The method uses a two-level DWT to decompose the image, applies DCT to selected sub-bands, and embeds two watermarks. The first is a logo used for ownership verification, and the second is an EPR encrypted with the Arnold transform for privacy protection. SVD is then used to enhance robustness. Experimental results show that the proposed scheme achieves better image quality and stronger resistance to common attacks compared with existing watermarking methods.
Survey on prediction, classification and tracking of neurodegenerative diseases Dhavalgi, Veena; Ranganatha, H R
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.pp367-374

Abstract

Neurodegenerative diseases (NDD) such as Alzheimer's, Parkinson's, and Huntington's disease are complex conditions that progressively impair neurological function. In recent years, machine learning (ML) techniques have shown considerable promise in the prediction, tracking, and understanding of these diseases, offering potential for earlier diagnosis and better patient outcomes. However, despite the advances, significant challenges remain in accurately predicting and classifying NDD due to their heterogeneous nature and the complexity of underlying biological processes. This survey aims to explore the current developments in the prediction and classification of neurodegenerative diseases using ML. The primary objective is to analyze various methods and techniques employed in the early diagnosis of NDD, focusing on ML algorithms, neuroimaging techniques, and biomarker analysis. The survey systematically reviews and categorizes existing studies, highlighting their methodologies, strengths, and limitations. Through an extensive literature review, the survey identifies key challenges such as the need for large, high-quality datasets, the integration of multi-modal data, and the interpretability of ML models. Findings suggest that while ML holds significant potential for advancing NDD research, addressing these challenges is crucial for its successful application. The survey concludes with a discussion on future research directions, emphasizing the importance of interdisciplinary approaches and the development of robust, transparent, and generalizable ML models for the early detection and diagnosis of neurodegenerative diseases.
Low complexity blind selective mapping in orthogonal frequency division multiplexing: utilizing linear combination Abdul Wahab, Aeizaal Azman; Muhammad Adnan, Nur Qamarina; Mohamad Hamzah, Firdaus
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.pp1698-1706

Abstract

Orthogonal frequency division multiplexing (OFDM) is a cornerstone in wireless communications for its spectral efficiency and robustness against multipath fading. However, its deployment is constrained by the high peakto-average power ratio (PAPR), which demands complex power amplifiers and increases system costs. Selective mapping (SLM) is a popular distortion less method for PAPR reduction but suffers from high computational complexity and data rate losses due to side information (SI) transmission. This paper proposes a low-complexity, blind SLM method utilizing linear combination, which reduces computational complexity by generating alternative candidate signals without additional inverse fast fourier transform (IFFT) operations. A maximum likelihood estimation (MLE)-based blind receiver recovers transmitted signals without SI, preserving data rate integrity. The proposed method achieves comparable PAPR and bit error rate (BER) performance to conventional SLM (C-SLM) while significantly reducing computational operations. Simulations demonstrate the efficiency of the method across various configurations, making it a strong candidate for next-generation communication systems like 5G and beyond.

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