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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 9,199 Documents
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Exploring word embeddings and clustering algorithms for user reviews Sidek, Zuleaizal; Syed Ahmad, Sharifah Sakinah
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1017-1024

Abstract

The rapid advancement of information technology has led to a significant surge in the volume of unstructured textual data. This has posed a major problem in terms of analyzing, organizing, and automatically clustering text for research purposes, which is crucial for extracting valuable insights. The process of manually clustering the unstructured data, such as customer reviews on the Internet, which capture the opinions of customers regarding products, services, and social events, requires significant financial resources, manpower, and time. Most of the studies are directed towards the analysis of sentiment in user reviews. In order to address the issues effectively, automated text clustering could assist in categorizing reviews into various themes, thereby simplifying the analysis process. Therefore, in this paper, we present and compare the result of experiment the combination of five text clustering techniques, namely K-means, fuzzy C-mean (FCM), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), and latent semantic analysis (LSA) with different embedding techniques, namely term frequency–inverse document frequency (TF-IDF), Word2Vec, and global vectors (GloVe). The experiments revealed that LDA is a reliable algorithm as it consistently produces good results across three-word embeddings. The highest Silhouette score recorded in the experiments was 0.66 using LDA and Word2Vec as word embedding. Simultaneously, the application of LSA in conjunction with Word2Vec yields superior outcomes, as evidenced by a Silhouette score of 0.65.
Behavioral analysis across multiple domains using machine learning and deep learning models Suryakant, Suryakant; P K, Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1124-1133

Abstract

Behavioral analysis.using machine learning (ML) and deep learning (DL) has become critical across healthcare, finance, cybersecurity, education, and marketing. This systematic review synthesizes advancements in ML- and DL-driven behavioral analysis (2019-2025) across five key domains. Our findings reveal that Deep Learning techniques achieve superior predictive accuracy (85-97% in healthcare imaging anomaly detection), while Machine Learning remains preferred for interpretability in finance (accuracy: 78-92%, with explainability advantage). A major trade-off emerges: DL models demonstrate higher accuracy but require substantial labeled data and computational resources, whereas ML models offer transparency but limited scalability. This review contributes by: (1) systematically analyzing domain-specific performance metrics and model evolution; (2) providing comparative synthesis of ML vs. DL approaches with quantitative benchmarking; (3) identifying critical challenges (data quality, privacy, algorithmic bias, interpretability); and (4) proposing actionable future directions, including Explainable AI, Federated Learning, and multimodal fusion. We adopt PRISMA-guided methodology examining 100+ peer-reviewed studies, revealing that hybrid ML-DL architectures represent the emerging best practice for balancing accuracy with interpretability.
ARX based cipher with S-box augmentation: statistical and differential evaluation Rajput, Manita; Chaudhari, Pranali
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp946-953

Abstract

With the growth of internet of medical things (IoMT), the continuous transfer of vital biomedical data requires lightweight encryption with strong resistance to statistical and differential attacks. The Speck cipher is a suitable candidate because of its low memory and execution time. However, its vulnerability to differential cryptanalysis limits wider use in healthcare environments. In this work, a hybrid lightweight algorithm is proposed by integrating the PRESENT substitution box within the Speck64/96 round structure. The substitution layer was evaluated at three different positions in the round function. Statistical and differential analyses were performed on four sets of plaintext data, each containing 1,000 test pairs. Index of coincidence (IoC), entropy, and avalanche effect were used as the primary statistical metrics. Differential trail strength was assessed using ciphertext differences and round-wise differential probability (DP). The experimental results show that the proposed version, named Speckpres_S, achieves a 6.02% reduction in IoC, a 3.8% improvement in entropy, and a 1.7% rise in avalanche effect when compared with Speck64/96. The differential trail becomes weaker, with a 46% reduction in trail probability and a 12–15% increase in trail weight across all datasets. The execution time remained within IoMT limits. This indicates stronger resistance to differential attacks with predictable diffusion. The study demonstrates that Speckpres_S improves security while maintaining practical latency and throughput for IoMT applications. Although execution time increases marginally, the gain in differential resistance and statistical performance makes the proposed algorithm a more robust option for transmitting sensitive biomedical parameters.
Fuzzy logic-based load balancing for voltage symmetry in distribution networks Saleem, Adeel; Ilkhombek Khosiljonovich, Kholiddinov; Mutalibjon Qizi, Kholiddinova Mashkhurakhon; Mutalibjon Qizi, Begmatova Mukhlisakhon; Mirzokhid, Sharobiddinov
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp873-884

Abstract

This paper introduces a load balancing approach based on fuzzy logic to enhance the efficiency of power distribution networks. The unbalance of voltages and an unequal load of the phases continue to be the problematic situation of the low-voltage distribution networks, particularly as the percentage of photovoltaic (PV) systems is growing. The results of such conditions include a deviation of voltage, higher losses of power, faster equipment aging, and lower power quality. This paper proposes a fuzzy logic–based phase load balancing approach that explicitly integrates voltage symmetry requirements defined by the GOST 13109-97 power quality standard. Unlike optimization-based and heuristic methods, the proposed fuzzy logic controller (FLC) redistributes phase currents using linguistic rules derived from voltage unbalance coefficients and phase current conditions, without iterative optimization procedures. Simulation results obtained in MATLAB/Simulink demonstrate a reduction of the voltage unbalance factor (VUF) by approximately 25–30% and a decrease in active power losses by 12–15% compared to the initial unbalanced operating state. The proposed method offers low computational complexity, fast response, and high interpret-ability, making it suitable for real-time implementation in smart distribution networks with distributed PV generation.
Synthetic inertia controller of a wind power plant as a means of increasing the stability of electric power systems Farkhadovich, Makhmudov Tokhir; Nasiriddin o’gli, Ramatov Adxam
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1117-1123

Abstract

The article discusses the use of wind power plants as sources of synthetic inertia to enhance power system stability and reduce frequency fluctuations. This research explores the feasibility of implementing a synthetic inertia controller in wind power plants to decrease the magnitude of frequency oscillations during transient operating conditions. The growing integration of wind farms into modern power grids leads to a reduction in the overall kinetic energy, or inertia, available in the system. As a result, the grid may become more vulnerable to disturbances. When the system inertia is too low, frequency stability can be affected, especially when large generating units suddenly fail or disconnect from the grid. In general, a lower level of inertia in the system causes larger frequency deviations following an imbalance in active power. To overcome this issue, a synthetic inertia regulator for wind power plants has been developed, enabling wind turbines to support the grid and reduce the depth of frequency drops during transient events.
A smart-contract framework for patient identity management in digital health platforms Prihantoro, Cahyo; Candra Febrianto, Dany; Istighosah, Maie; Lestrasi Ma’ruf, Ahmad Uffi; Rohman Winarno, Angger Taufiqur; Islami Sulistya, Yudha
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1025-1039

Abstract

Asmart-contract framework for patient identity management in digital health platforms. A major gap in current digital health ecosystems is the absence of a portable and verifiable patient identity layer across fragmented electronic health record (EHR) systems. The problem addressed is the lack of a portable, verifiable, and patient-centric identity layer across fragmented electronic health record systems, which weakens access accountability and privacy. The proposed solution couples fast healthcare interoperability resources (FHIR) with self-sovereign identity (SSI), storing FHIR payloads off-chain in the InterPlanetary file system (IPFS) and committing only encrypted pointers and policies on Polygon smart contracts. Patient identifiers and content addresses are protected with AES-256 GCMauthenticated encryption and elliptic-curve key wrapping (ECIES) for both the healthcare administrator and the patient. A web implementation in Next.js using thirdweb automates wallet creation, keystore handling, encryption, and on-chain commits. In evaluation with 50 synthetic registrations, success reached 100 percent, median end-to-end latency was 5.86 seconds, mean on-chain latency 3.77 seconds, average transaction fee 0.0401 POL/MATIC, encryption time 13.9 milliseconds, and all decryptions validated. The results indicate practical feasibility for portable identity and auditable access, with on-chain latency as the main bottleneck to be reduced through batching, cheaper layers, and broader field trials. However, this study is limited because the evaluation uses only synthetic data and singleprovider testing, without real-world patients or multi-institutional environments. Zero-knowledge proofs (ZKP) are discussed conceptually as future integration and are not implemented or benchmarked in this work.
Intelligent artificial neural network-based control for solar electric vehicle charger Damodharan, Rajeshkumar; Kumar S, Pradeep
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp885-893

Abstract

The performance of electric vehicle (EV) charging systems in response to sudden changes in solar irradiation and dynamic battery load variations. EV chargers must have effective power conversion and flexibility as the use of renewable energy sources increases. This paper suggests a charging system based on resonant converters that minimizes heat and losses in EV charging stations by enabling high-efficiency, soft-switching power transfer. For modern EV applications, the ability to manage large voltage fluctuations ensures reliable, quick, and portable charging. The artificial neural networks (ANN) controller overcomes the drawbacks of conventional Perturb and Observe (P&O) for solar DC-DC converters and PI control for resonant converter approaches. MATLAB simulation results demonstrate that the proposed system outperforms traditional techniques in terms of an ANN based controller, which enhances maximum power point tracking (MPPT) efficiency to 98.6%, reduces oscillations near the maximum power point by approximately 80%, and increases total EV charging efficiency by 3%. The ANN-based control to EV charging infrastructure greatly enhances overall system dependability and real-time responsiveness, making it a good fit for subsequent smart grid and renewable energy applications.
FGMPSO: a hybrid firefly-gradient-MOPSO framework for high-dimensional feature selection Batoul Rashed, Alwatben
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp1082-1094

Abstract

When working with high-dimensional datasets, selecting the most relevant features is essential for improving both model clarity and processing efficiency, all while keeping predictive accuracy intact. In response to this challenge, the study introduces firefly-gradient-multi-objective particle swarm optimization (FGMPSO), an advanced hybrid technique that blends the firefly algorithm, gradient descent (GD), and multi-objective particle swarm optimization (MOPSO). This approach is specifically designed to identify an optimal subset of features that balances dimensionality reduction with strong classification performance. The method was evaluated on eight benchmark datasets and compared against multiple PSO-based feature selection techniques. The empirical results demonstrated that FGMPSO consistently achieved superior or competitive classification accuracy while selecting significantly fewer features. Notably, in several datasets, FGMPSO not only reduced dimensionality but also outperformed other methods in terms of classification accuracy. This efficiency is attributed to the intelligent exploration of the search space by the firefly algorithm, refinement via GD, and effective trade-off optimization enabled by MOPSO. The findings suggest that FGMPSO is a robust and scalable solution for feature selection, particularly suitable for complex and high-dimensional datasets. Its adaptability, convergence speed, and balance between dimensionality reduction and accuracy position it as a valuable tool in modern machine learning pipelines.
Study of performance the 3-phase induction motor that drives by using scalar and vector control with variable speed loading Alabedalkhamis, Omran; Karahan, Baran; İdiz, İbrahim; Alptekin, Hüseyin; Ediz Erol, Enver
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp894-911

Abstract

Induction motor performance and efficiency greatly depend on the applied control technique, particularly in variable- and fixed-speed industrial applications. This paper aims to comparatively assess scalar control and vector control strategies for three-phase squirrel-cage induction motors. Using a simulation-based approach in MATLAB/Simulink, scalar control with sinusoidal pulse width modulation (SPWM) and vector control with space vector modulation (SVM) are built and analyzed under constant, variable, and bidirectional speed loading situations characteristic of a drive system. The results demonstrate that vector control provides greater speed regulation (about 93% compared to scalar control), reduced torque ripple (about 97% compared to scalar control), lower current stress (about 94% compared to scalar control), and improved dynamic responsiveness compared to scalar control, especially during transient operation. The paper is limited to numerical simulations. This paper’s biggest contribution is a clear, practical comparison which provides performance- and cost-oriented guidelines for selecting appropriate induction motor control strategies in severel applications.
Leveraging CNN to analyze facial expressions for academic engagement monitoring with insights from the multi-source academic affective engagement dataset C. T., Noora; Tamil Selvan, P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp977-999

Abstract

The dynamics of student engagement and emotional states significantly influence learning outcomes. Positive emotions, stemming from successful task completion, contrast with negative emotions arising from learning struggles or failures. Effective transitions to engagement occur upon problem resolution, while unresolved issues lead to frustration and subsequent boredom. Facial engagement monitoring is crucial for assessing students’ attention, interest, and emotional responses during learning. Recent advancements in convolutional neural networks (CNNs) show promise in automatically analyzing facial expressions to infer engagement levels. This study proposes a CNN-based approach utilizing the multi-source academic affective engagement dataset (MAAED) to categorize facial expressions into boredom, confusion, frustration, and yawning. By extracting features from facial images, this method offers an efficient and objective means to gauge student engagement. Recognizing and addressing negative affective states, such as confusion and boredom, is fundamental in creating supportive learning environments. Through automated frame extraction and model comparison, this study demonstrates reduced loss values with improving accuracy, showcasing the effectiveness of this method in objectively evaluating student engagement. Facial engagement monitoring with CNNs, using the MAAED dataset, is pivotal in understanding human behavior and enhancing educational experiences. The CNN model, trained on MAAED annotated facial expressions, accurately classifies engagement categories. Experimental results underscore the CNN-based approach’s efficacy in monitoring facial engagement, highlighting its potential to enrich educational environments and personalized learning experiences.

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