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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 25 Documents
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Search results for , issue "Vol 41, No 3: March 2026" : 25 Documents clear
Level of detail in UML models and its impact on model comprehension: a replication study Nugroho, Ariadi; Chaudron, Michel R.V
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.pp1095-1104

Abstract

This replication study examines the impact of level of detail (LoD) in unified modeling language (UML) on model comprehension, replicating a controlled experiment, which involved 53 MSc students at Eindhoven University of Technology. Using the same UML model and experimental design, we conducted the study with 23 MSc Computer Science students at Bina Nusantara University, Indonesia. Consistent with the original findings, higher LoD was found to enhance comprehension correctness. However, the effect on comprehension efficiency was weaker and not statistically significant, likely due to the smaller sample size and contextual differences in subjects’ backgrounds. Furthermore, we found a potential disconnect between perception and actual comprehension performance in the subjects receiving UML model with low LoD. Specifically, while they viewed the model favourably, their actual understanding may have been impaired by the limited information and therefore the perceived clarity and ease of comprehension are not reflective of the true comprehension. Overall, this study reinforces the importance of LoD in UML modeling and highlights the need for further replication, particularly in contexts involving professional software engineers.
IoT-enabled digital twin with renewable energy for sustainable mudless eel aquaculture Ferdiansyah, Muhammad; Mariya, Lika; Rahman, Taufik; Dwiono, Sugeng
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.pp912-923

Abstract

This research develops and tests a digital twin (DT)-based smart aquaculture system for mud-free eel farming through the integration of IoT sensing, artificial intelligence (AI)-based prediction, edge computing, and solar energy-based automation. The approach used is experimental systems engineering, which includes system design, hardware and software implementation, virtual replication, and physical-digital two-way synchronization. The system utilizes ESP32-based pH, temperature, dissolved oxygen (DO), ammonia (NH₃), and turbidity sensors, MQTT communication, and Raspberry Pi edge computing. Water quality prediction is performed using long short-term memory (LSTM) and random forest regression. The dataset consists of 30 days of real-time data covering water quality, actuator activity (aerator, pump, feeder), and energy production and consumption by IoT sensors and energy meters. Results show that LSTM excels by R² = 0.94; RMSE = 0.14; MAPE <5% and synchronization latency <1.5 seconds. Solar energy integration reduces energy consumption by 54 67%, whilst automation increases eel survival rate by 78% to 91%. The novelty of this research lies in the first integrated implementation of DT, AIoT, and solar energy-based automation in mud-free eel farming. The proposed framework provides a precise, scalable, and sustainable solution for the development of modern aquaculture.
Enhanced prediction of chronic kidney disease onset through machine learning techniques John Parreño, Samuel; Joy Anter, Maria Cristine
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.pp966-976

Abstract

Chronic kidney disease (CKD) is a global health concern that often progresses silently to severe complications. This study aims to enhance CKD prediction using machine learning models: support vector machines (SVM), extreme gradient boosting (XGBoost), k-nearest neighbors (k-NN), and a stacking model. The dataset, sourced from the UCI machine learning repository, includes clinical and demographic attributes from 200 patients. After preprocessing, the final dataset comprised 161 samples and 143 features. SVM achieved perfect classification performance with 100% accuracy, precision, and recall. XGBoost followed closely with an accuracy of 97.44% and a kappa statistic of 0.9451. The k-NN model delivered strong performance, achieving 92.31% accuracy. The stacking model outperformed all individual models, achieving perfect accuracy. The models demonstrated high sensitivity and specificity, indicating their effectiveness in distinguishing CKD from non-CKD cases. These findings emphasize the potential of machine learning in CKD diagnosis. Early detection can lead to improved clinical outcomes by enabling timely interventions and personalized treatment strategies. Future research should emphasize comprehensive feature engineering and larger, more diverse datasets to improve predictive accuracy and generalizability. Incorporating machine learning models in nephrology could significantly advance CKD detection and management.
A multimodal framework for explainable chest X-ray report generation Hamza Chehili; Nourhene Bougourzi; raida malak Makhlouf; hadjer Taib; Mustapha Bensaada
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.pp1060-1069

Abstract

Chest X-ray (CXR) interpretation remains a challenging task due to overlapping anatomical structures, variability in disease presentation, and increasing clinical workload. Existing automated report-generation models provide promising results but often lack explicit interpretability, limited clinical alignment, and insufficient comparative evaluation with established baselines. This study proposes an explainable multimodal framework that combines a dual CNN encoder (ResNet-50 and EfficientNet-B0) with the Gemma-3 1B language model fine-tuned using low-rank adaptation (LoRA). Visual explanations are produced through Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance transparency in the decision process. Unlike prior image-to-text pipelines, our approach follows a findings-guided paradigm and integrates both visual and textual cues during generation. Experiments conducted on public datasets demonstrate consistent improvements over representative vision-language baselines reported in recent literature, with notable gains in BLEU, ROUGE, METEOR, and BERTScore. Generated reports show improved factual completeness and clinically relevant region-level attention. Limitations include the absence of evaluation against emerging foundation models and the need for anatomical- level explainability metrics. Future work will extend benchmarking to models such as M2-Transformer, MedCLIP-GPT, and R2Gen, and will explore clinical validation in real-world workflows.
Satellite-based assisted-offloading for energy-constrained edge networks Thembelihle Dlamini; Mengistu A. Mulatu; Sifiso Vilakati
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.pp935-945

Abstract

As the need for global broadband internet connectivity increases, there is a need to consider the use of non-terrestrial networks (NTNs) to extend the network coverage to protected areas (e.g., national parks). Usually, protected areas are prohibited from having power lines thus lacking wireless connectivity. To over come this challenge, energy can be provided through the use of green energy from a solar photovoltaic (PV) system. Then, a green energy-based base station (BS) can be deployed within the area in order to provide mobile connectivity to visitors, as well as also using the NTNs to handle excess traffic or take over the traffic in the event the BS does not have sufficient green energy from stor age. In this paper, a hybrid wireless communication system is proposed to in clude BS sites located in a protected area and satellites in the low earth orbits (LEO), coupled with new offloading strategies, with the main goal of optimizing the trade-off between energy consumption and end-to-end delay for the green energy-based BS sites. For accuracy of our simulations, we consider real data from a solar photovoltaics system, traffic workloads, visitor’s location data, and satellite orbits from Starlink constellations. Our results demonstrate that the co existence of the BS and satellite achieve energy savings from 59% to 34%, with an average system delay of 0.83 seconds and a packet drop rate that ranges from 8.3% to 2.7%, when compared with our benchmark.
Thermal effects of curing parameters on the natural frequency of GNP/Ag ink composites Md Hkhir, Khirwizam; Azmmi Masripan, Nor; Photong, Cholatee; Watson, Alan; Azli Salim, Mohd
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.pp845-858

Abstract

This research examines how curing temperature and duration influence the electrical and mechanical behavior of hybrid graphene nanoplatelet and silver (GNP/Ag) conductive ink. The ink was formulated from GNPs, silver flakes and silver acetate printed on copper substrates, then cured 240 °C, 250 °C, and 260 °C for one to three hours. Electrical resistance was measured using a Two-Point probe, while natural frequency was obtained from experimental modal analysis (EMA) on stainless-steel (SUS304) cantilever beams laminated with printed ink. The results show that the higher curing temperature and longer curing time reduce resistivity and increase natural frequency, with the best performance observed at 260 °C for 3 hours (8.4×10⁻⁶ Ω.m and a 4.2 Hz increase). These findings confirm that a direct relationship between conductivity and stiffness, where conditions that promote stronger particle bonding also raise structural rigidity. The main contribution of this research is the joint evaluation of curing effects on both electrical and vibrational responses, offering a combined electro-mechanical perspective that is not often explored in GNP/Ag ink research. The results provide practical guidance for selecting curing conditions based on the required balance between conductivity and mechanical stability in flexible and stretchable electronic applications.
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 Manita Rajput; Pranali Chaudhari
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.

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