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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 36 Documents
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Search results for , issue "Vol 41, No 2: February 2026" : 36 Documents clear
Hybrid AES-LEA encryption: a performance and security analysis Mehdy, Hala Shaker; Rusli, Mohd Ezanee; Hoomod, Haider Kadhim
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.pp532-545

Abstract

The advanced encryption standard-lightweight encryption algorithm (AESLEA) hybrid algorithm (ALESA) addresses a critical gap in cryptographic systems by solving the inherent trade-off between high security and computational efficiency. While the AES offers robust security, its complex operations result in high latency and energy costs, making it less suitable for resource-constrained environments. Conversely, lightweight alternatives like the LEA provide high speed but potentially weaker diffusion properties. This paper proposes a novel hybrid encryption model that strategically integrates AES and LEA by replacing AES’s computationally intensive MixColumns transformation with a streamlined LEA-based operation. This solution delivers the best of both paradigms: the security strength of AES and the operational efficiency of LEA, while also demonstrating superior statistical security by passing all NIST tests with higher p-values and maintaining near-optimal entropy. The hybrid ALESA algorithm thus presents an ideal, balanced solution for applications requiring both strong security guarantees and high performance, particularly in IoT and large-scale data encryption scenarios.
An automatic stock price movement prediction using circularly dilated convolutions with orthogonal gated recurrent unit Rajendran, Durga Meena; Kalianandi, Maharajan; Ananthan, Bhuvanesh
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.pp823-832

Abstract

Recently, stock trend analysis has played an integral role in gaining knowledge about trading policy and determining stock intrinsic patterns. Several conventional studies reported stock trend prediction analysis but failed to obtain better performance due to poor generalization capability and high gradient vanishing problems. In light of the need to forecast stock price trends using both textual and empirical price data, this research proposed a novel hybridized deep learning (DL) model. Preprocessing, feature extraction, and prediction are the three effective stages that the created research goes through in order to properly estimate the stock movements. Data cleaning, which helps improve data quality, is calculated in the preprocessing step. Next, we use the created CDConv-OGRU technique-hybridized circularly dilated convolutions with orthogonal gated recurrent units-to extract features and make predictions. Python serves as the platform for processing and analyzing the created approach. This research uses a publicly accessible StockNet database for testing and compares results using a number of performance metrics, including accuracy, recall, precision, Mathew’s correlation coefficient (MCC), and f-score. In the experimental part, the created approach obtains a total of 95.16% accuracy, 94.8% precision, 94.89% recall, 95% confidence interval, and 0.9 MCC, in that order.
Enhancing predictive maintenance capabilities by integrating artificial intelligence: systematic review G. N, Thippeswamy; S, Neelambike; M. B, Sanjay Pande
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.pp782-790

Abstract

Organizations are under pressure to increase productivity and lower operating costs because facility operations and maintenance (O&M) account for a significant portion of a facility's life-cycle cost. By facilitating real-time monitoring and data-driven decision-making, artificial intelligence (AI) has become a promising catalyst for enhancing predictive maintenance. In order to investigate how AI can be combined with predictive maintenance to lower operational and maintenance overhead, this systematic review examines peer-reviewed studies that have been published in the last five years. Using an evidence-based review methodology and adaptive structuration theory (AST), the study synthesized results from 14 excellent publications. Unbiased maintenance planning, cost-effective resource utilization, and AI-enabled operational visibility emerged as three key themes. According to the review, AI-driven predictive maintenance greatly increases operational effectiveness and reduces costs; however, successful implementation necessitates better data governance and organizational preparedness.
Integrating contrastive and generative AI with RAG for responsible and fair CV classification Chafi, Soumia; Kabil, Mustapha; Kamouss, Abdessamad
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.pp710-719

Abstract

The automation of curriculum vitae (CV) classification raises major challenges related to accuracy, fairness, and the heterogeneity of candidate documents. Existing approaches often address these dimensions separately and struggle to reduce demographic bias while maintaining high predictive performance. This study addresses this gap by proposing a hybrid pipeline that combines contrastive learning for representation with a lightweight generative model within a retrieval-augmented generation (RAG) framework. The method is evaluated on a large dataset of 50,000 CVs, using standard classification metrics as well as fairness indicators based on reductions in demographic disparities and equality of opportunity. Experiments show that our approach achieves an accuracy of 95.6% and a fairness index of 0.94, reducing gender-related disparities from 4.8% to 0.3%. These results demonstrate that it is possible to simultaneously improve predictive performance and fairness through a multi-level fairness strategy. The proposed system thus represents a practical and responsible solution for integrating AI into recruitment processes.
Contextualized clinical anomaly detection with explainable AI and patient modeling Elketroussi, Amel; Djebbar, Bachir; Bekkouche, Ibtissem
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.pp614-623

Abstract

This study aims to reduce alarm fatigue and improve the clinical relevance of alerts in intensive care by combining sequential modeling, patient contextualization, explainable artificial intelligence (XAI), and probability calibration. To this end, we leverage the adult cohorts from MIMIC-III/IV, segmented into four-hour windows, explicitly handling missing data and constructing a context vector that integrates demographics, comorbidities, and therapeutic interventions. The approach relies on a tabular autoencoder, an long short-term memory (LSTM) autoencoder, and a transformer, complemented by an adjustment layer based on auditable clinical rules, local explanations (LIME/SHAP), and post-hoc calibration (temperature scaling). Evaluation involves receiver operating characteristic (ROC)/precision–recall (PR) area under the curve (AUC), F1-score, sensitivity and specificity, as well as calibration metrics (ECE, Brier score), alert burden, ablation studies, robustness tests, and subgroup fairness analyses. Across all experiments, the complete model (+Context+XAI+Calibration) outperforms baselines in AUPRC and F1, reduces alert burden, and improves calibration while providing understandable explanations. Specifically, the proposed model improves ROC AUC from 0.74 to 0.89 and reduces alert burden by approximately one third compared to clinical thresholds.
Evaluating test case minimization with DB K-means Sharma, Sanjay; Choudhary, Jitendra
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.pp555-563

Abstract

This paper evaluates a new method for test case minimization using clustering methods. Clustering is a method used on data sets to generate clusters of the same behavior; thus, unnecessary and redundant data sets are removed. Hence, minimized data sets are generated that represent the same coverage as the original data sets. This is achieved by a new method based on clustering that separates data sets into two sets, outlier and non-outlier, after reducing redundant test cases, combines minimized data sets named DB K-means. The methods individually worked on outlier and non-outlier data sets and removed redundant data sets to minimize test cases. The result of the proposed method is better than the simple clustering method used for test case minimization. The software development would only be complete with software testing. Enhancing software quality requires testing numerous test cases, a laborious and time-consuming process, testing a program using a set of inputs known as test cases. Test case minimization approaches are critical in software testing, as they optimize testing resources and provide comprehensive coverage. Minimization is the process of choosing a subset of test cases that accurately captures the behavior of the entire test suite to minimize duplicacy and increase efficiency.
Enhanced soil moisture sensing using graphene-coated copper electrodes Nuralam, Nuralam; Muzakki, Rizdam Firly; Kusumastuti, Sri Lestari
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.pp470-477

Abstract

Soil moisture monitoring is essential for precision agriculture to optimize irrigation and increase crop productivity. Traditional conductivity-based sensors often face limitations such as low sensitivity, slow response, and measurement instability. This study presents a simple and effective enhancement method by applying a graphene coating on copper electrodes using the drop casting technique. Experimental evaluations were conducted on natural soil samples at varying moisture levels. The graphene-coated sensor exhibited a significantly higher sensitivity of 23.0 Ω/% compared to 12.0 Ω/% for the uncoated sensor, a faster response time of approximately 5 seconds, and improved measurement consistency with a reduced standard deviation of ±15 Ω. Graphene's superior electrical conductivity and strong water affinity are key factors contributing to this performance improvement. These findings indicate that graphene-coated sensors offer a promising solution for reliable, cost-effective soil moisture monitoring in smart farming systems.
The Bender’s decomposition model to optimize temporary waste disposal sites based on general algebraic modeling system Octarina, Sisca; Puspita, Fitri Maya; Cahyono, Endro Setyo; Yuliza, Evi; Simanjuntak, Pebriyanti; Supadi, Siti Suzlin
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.pp666-679

Abstract

Waste constitutes a substantial problem in urban and residential locales, as the volume of refuse escalates in tandem with population increase, deteriorating community quality of life. One solution to this problem is to provide temporary waste disposal sites (TWDS). This research discussed optimizing TWDS in the Sukarami Subdistrict, Palembang City, which consists of seven villages. The current TWDS in the Sukarami Subdistrict is irregular, with some sites located close together and others far apart. The optimization problem is solved by formulating the set covering problem (SCP) model, namely the set covering location problem (SCLP), the p-Median problem, and the Bender’s decomposition model. All models were solved using the general algebraic modeling system (GAMS) software. The research introduces a Bender’s decomposition model based on the SCLP model. The Sukarami Subdistrict has 29 TWDS located in only five villages. Using the SCLP and Bender’s decomposition models, the study identified 19 optimal TWDS in the Sukarami Subdistrict. Based on the solution of the p-Median problem, there are seven TWDS that can meet each village’s demand. This study recommends the optimal TWDS obtained from the Bender’s decomposition model. Additionally, two TWDS are recommended to be added, each in Sukodadi and Talang Betutu villages.
Joint angle prediction and joint-type classification in human gait analysis using explainable deep reinforcement learning N. R., Deepak; P. T., Soumya Naik; P. R., Ambika; Ahamed, Shaik Sayeed
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.pp564-578

Abstract

Human gait analysis is a key component of rehabilitation, prosthetics, and sports science, especially for clinical evaluation and the development of adaptive assistive technologies. Accurate joint-angle estimation and dependable joint-type classification remain difficult because of the complex temporal behavior of gait signals and the limited interpretability of many deep learning (DL) approaches. While recent techniques have enhanced predictive accuracy, their clinical applicability is often limited by insufficient transparency and adaptability in learning mechanisms. To overcome these limitations, this work proposes an integrated framework that unifies DL, reinforcement learning (RL), and explainable artificial intelligence (XAI). Stochastic depth neural networks (SDNN) are applied for joint-angle regression, whereas deep feature factorization networks (DFFN) are used for multi-class joint-type classification. Optimization is achieved using Q-learning (QL) and mutual information maximization (MIM), ensuring stable convergence and improved learning efficiency. To improve interpretability, the framework incorporates counterfactual and contrastive explanations, feature ablation studies, and prediction probability analysis. Experimental findings show that the SDNN MIM model attains an R2 score of 0.9881, with RL rewards increasing from 0.997 to 0.999 during regression training. For joint-type classification, the DFFN MIM model achieves an accuracy of 0.95, with reward values improving from 0.90 to 0.98. These results demonstrate the effectiveness of the proposed framework in delivering accurate and interpretable gait predictions, supporting its relevance to biomechanics, healthcare, personalized rehabilitation, and intelligent assistive systems.
A hybrid edge–cloud computing framework for low-latency, energy-efficient, and sustainable smart city applications Saluja, Kamal; Khaneja, Tanya; Gupta, Sunil; Goyal, Reema; Leong, Wai Yie
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.pp791-799

Abstract

Smart-city applications demand ultra-low latency, high reliability, and sustainable operation, which are difficult to achieve using cloud-only or edge-only computing paradigms. This study suggests a carbon-conscious architecture for managing smart cities’ intelligent job offloading between the edge and the cloud. This is made possible by the Internet of Things and driven by reinforcement learning (RL). A deep Q-network (DQN) is used to dynamically assign tasks to cloud servers and edge nodes based on how much energy they use, how long it takes to send data over the network, and how much bandwidth they have. A lightweight permissioned blockchain layer makes sure that data is correct across all of its parts, and carbon-aware scheduling puts low-carbon resources first. EdgeCloudSim is used to test the system with real-world smart city workloads. When compared to systems that simply use the cloud, the proposed solution showed a 64.6% drop in average latency, a 24.2% drop in energy use, and a 15% drop in carbon emissions. Combining artificial intelligence (AI)-driven orchestration with scheduling that takes sustainability into account in a hybrid edge-cloud environment yields positive outcomes.

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