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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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Fraud detection using TabNet* classifier: a machine learning approach Mary, G. Anish; Sudha, S.
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp601-613

Abstract

Detecting fraudulent transactions is a big challenge in the digital financial world. Transaction volumes are growing quickly, and new attack methods often outstrip traditional detection systems. Current fraud-detection models usually lack clarity and do not perform reliably on unbalanced real-world datasets. This highlights the urgent need for clear and explainable deep-learning methods for tabular financial data. This paper presents an interpretable deep learning framework built on the TabNet classifier. It uses attention-driven feature selection, sparse representation learning, and sequential decision reasoning to model complex interactions among transactional, demographic, and geographical factors. The model was tested on a real-world credit card transaction dataset with 23 features. It achieved 99.69% accuracy, a 0.975 F1-score, and a 0.956 ROC-AUC. This performance outperforms benchmark models such as random forest, XGBoost, LightGBM, and logistic regression. In addition to outstanding predictive results. Furthermore, interpretability is enhanced by TabNet's attention-based feature attribution. This facilitates the clear understanding of model decisions, supporting its use in regulated financial environments where precision and responsibility are crucial.
An investigation of different low-power circuits and enhanced energy efficiency in medical applications R, Prabhu; Rajagopal, Sivakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp478-493

Abstract

This research investigates the application of low-power circuits in medical devices and imaging systems. The primary goal is to address the growing demand for energy-efficient solutions in medical applications. There is an increasing need for energy-efficient solutions due to the development of medical technologies, particularly implanted and battery-operated medical devices. This paper explores the integration of adiabatic logic as a critical enabler for achieving low power consumption in medical applications. The study looks into different low-power circuit designs and technologies that optimize power usage without sacrificing performance. Adiabatic circuits offer a promising substitute for conventional circuitry in low-energy design. The research examines several low-power circuit designs and technologies that maximize power efficiency without compromising functionality. In low-energy design, adiabatic circuits present a possible alternative to traditional circuitry. Adiabatic logic aims to create energy-efficient digital circuits that consume significantly less power than conventional complementary metal-oxide-semiconductor (CMOS) circuits. We accomplish this by recovering and recycling energy that would otherwise be lost as heat and carefully controlling energy flows during switching events. Adiabatic logic is precious in battery-operated and energy-constrained devices.
RAC: a reusable adaptive convolution for CNN layer Hung, Nguyen Viet; Huynh, Phi Dinh; Thinh, Pham Hong; Nguyen, Phuc Hau; Hoang, Trong-Minh
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp753-763

Abstract

This paper proposes reusable adaptive convolution (RAC), an efficient alternative to standard 3×3 convolutions for convolutional neural networks (CNNs). The main advantage of RAC lies in its simplicity and parameter efficiency, achieved by sharing horizontal and vertical 1×k/k×1 filter banks across blocks within a stage and recombining them through a lightweight 1×1 mixing layer. By operating at the operator design level, RAC avoids post-training compression steps and preserves the conventional Conv–BN–activation structure, enabling seamless integration into existing CNN backbones. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on CIFAR-10 using several architectures, including ResNet-18/50/101, DenseNet, WideResNet, and EfficientNet. Experimental results demonstrate that RAC significantly reduces parameters and memory usage while maintaining competitive accuracy. These results indicate that RAC offers a reasonable balance between accuracy and compression, and is suitable for deploying CNN networks on resource-constrained platforms.
Stable and accurate customer churn prediction: comparative analysis of eight classification algorithms Haris, Vincent Alexander; Arsyad, Muhammad Ilyas; Adi Nugraha, Nathanael Septhian; Dani, Yasi; Ginting, Maria Artanta
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp655-665

Abstract

Predicting customer churn is a challenging problem in many subscription-based industries, though it is considered more cost-effective than acquiring new customers. In this research, customer churn is predicted using a public dataset from an internet service provider, with 72,274 instances and 55% churn rate. The main contribution is to provide a comprehensive comparison of the stability and performance of eight classification algorithms in customer churn prediction using a large-scale public dataset. The research process includes data collection, data preprocessing, feature engineering, and model evaluation. The metrics evaluation presents test accuracy, accuracy gap, precision, recall, F1-Score, and ROC AUC, with stratified K-Fold cross-validation. Since the proportion of churn and non-churn in the dataset is relatively balanced, the F1-score is considered as the primary evaluation metric, as it provides a balanced assessment of precision and recall for both classes. The results show that CatBoost and XGBoost are the most effective models that achieve high F1-scores of 94.97% and 94.92%, respectively.
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 Chehili, Hamza; Bougourzi, Nourhene; Makhlouf, raida malak; Taib, hadjer; Bensaada, Mustapha
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 Dlamini, Thembelihle; A. Mulatu, Mengistu; Vilakati, Sifiso
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.

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