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Comparison methods in a decision support system for determining JavaScript frameworks

10.12928/telkomnika.v24i1.27241
Rofif Aghna; Sunan Kalijaga State Islamic University Yogyakarta Fakhri Diya , Agus; Sunan Kalijaga State Islamic University Yogyakarta Mulyanto
The selection of an appropriate JavaScript framework in web-based software development often leads to errors when the chosen framework is incompatible with the design. The ability to make decisions quickly, accurately, and precisely is therefore a key factor in successful software design. Addressing this need, the present study analyzes the accuracy of the analytical hierarchy process-weight product (AHP-WP), analytical hierarchy process-technique for order preference by similarity to ideal solution (AHP TOPSIS), and analytical hierarchy process-simple multi-attribute rating technique (AHP-SMART) methods in determining the most suitable JavaScript framework according to the International Organization for Standardization (ISO) 9126 classification. To evaluate accuracy, the mean absolute percentage error (MAPE) was applied as a cost function to measure the error percentage of each method. The analysis was conducted on ten popular JavaScript frameworks selected based on their popularity and usage trends. The evaluation considered six quality criteria: functionality, reliability, usability, efficiency, maintainability, and portability. The results show the ranking of each alternative for all methods. Accuracy measurement using MAPE revealed that the AHP-WP method produced the smallest error percentage (37.77645%), compared to AHP-TOPSIS (47.12566%) and AHP-SMART (46.4041%). Accordingly, the AHP-WP method is recommended for decision support system (DSS) development.
Volume: 24
Issue: 1
Page: 95-110
Publish at: 2026-02-01

An AI-driven framework for efficient and accurate calibration of electricity meters using extreme gradient boosting

10.11591/ijai.v15.i1.pp154-163
Rosalina Rosalina , Nunik Afriliana
Calibration testing plays a vital role in electricity meter manufacturing to guarantee measurement accuracy and compliance with industry standards. In practice, however, conventional calibration methods are often hindered by lengthy test cycles and the high cost of expanding test bench capacity. This study proposes a data-driven approach to address these limitations by applying machine learning techniques to optimize calibration testing. An extreme gradient boosting (XGBoost) regression model, enhanced through systematic hyperparameter tuning and feature engineering, was developed to predict calibration outcomes using data obtained from existing production test benches. When evaluated under real manufacturing line conditions, the proposed method shortened calibration runtime by about 55% compared with manual procedures relying on power supply units (PSU) and standard meter calculations, while maintaining reliable measurement accuracy. The framework also achieved lower root mean square error (RMSE), demonstrating improved predictive performance. In addition to reporting these results, the study describes the preprocessing pipeline, model selection process, and optimization strategy, providing a practical and replicable framework for integrating artificial intelligence (AI) into industrial calibration processes.
Volume: 15
Issue: 1
Page: 154-163
Publish at: 2026-02-01

Hybrid AES-LEA encryption: a performance and security analysis

10.11591/ijeecs.v41.i2.pp532-545
Hala Shaker Mehdy , Mohd Ezanee Rusli , Haider Kadhim Hoomod
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.
Volume: 41
Issue: 2
Page: 532-545
Publish at: 2026-02-01

Enhancing predictive maintenance capabilities by integrating artificial intelligence: systematic review

10.11591/ijeecs.v41.i2.pp782-790
Thippeswamy G. N , Neelambike S , Sanjay Pande M. B
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.
Volume: 41
Issue: 2
Page: 782-790
Publish at: 2026-02-01

A comparative MRI-based study of ResNet-152 and novel deep learning approaches for early Alzheimer’s disease classification

10.12928/telkomnika.v24i2.27576
Kelvin Leonardi; STMIK TIME Kohsasih , Octara; STMIK TIME Pribadi , Andy; STMIK TIME Andy , Daniel; STMIK TIME Smith Sunario
Alzheimer’s disease (AD) is the leading cause of dementia, making early-stage detection essential for timely intervention. Most prior studies have focused on binary AD classification, which limits sensitivity to disease progression. This study addressed this gap by evaluating whether tailored convolutional neural network (CNN) architectures could improve stage-aware classification using a publicly available magnetic resonance imaging (MRI) dataset containing 35,984 images across four diagnostic categories. The dataset underwent grayscale conversion, resizing, contrast enhancement, normalization, and class balancing prior to model development. Four models were trained and compared: ResNet-152, a custom multiclass CNN, a one-vs-one (OvO) model, and a one-vs-rest (OvR) model. Performance was measured using accuracy, precision, recall, F1 score, and confusion-matrix–based metrics. The custom multiclass CNN achieved the strongest performance, yielding the highest accuracy and balanced results across all evaluation metrics. These findings demonstrate the value of systematically comparing decomposition strategies for multi-stage Alzheimer’s detection and highlight the potential of the proposed approach to enhance early diagnostic support. Future work may incorporate multimodal inputs or hybrid architectures to improve sensitivity to subtle structural changes and further strengthen clinical applicability.
Volume: 24
Issue: 2
Page: 536-548
Publish at: 2026-01-30

An enhanced NLP approach for BI-RADS extraction in breast ultrasound reports using deep learning

10.11591/ijeecs.v41.i1.pp191-199
Ahmed Sahl , Shafaatunnur Hasan , Maie M. Aboghazalah
Breast cancer stands as one of the top causes of death around the globe, making the accurate interpretation of breast ultrasound reports vital for early diagnosis and treatment. Unfortunately, key findings in these reports are often buried in unstructured text, complicating automated extraction. This study presents a deep learning-based natural language processing (NLP) approach to extract breast imaging reporting and data system (BI-RADS) categories from breast ultrasound data. We trained a recurrent neural network (RNN) model, specifically using a BiLSTM architecture, on a dataset of reports that were manually annotated from a hospital in Saudi Arabia. Our approach also incorporates uncertainty estimation techniques to tackle ambiguous cases and uses data augmentation to boost model performance. The experimental results indicate that our deep learning method surpasses traditional rule-based and machine-learning techniques, achieving impressive accuracy in classification tasks. This research plays a significant role in automating radiology reporting, aiding clinical decision-making, and pushing forward the field of breast cancer research.
Volume: 41
Issue: 1
Page: 191-199
Publish at: 2026-01-01

Artillery fire control based on artificial intelligence algorithm of unmanned aerial vehicle

10.11591/ijeecs.v41.i1.pp83-89
Azad Agalar Bayramov , Samir Suleyman Suleymanov , Fatali Nariman Abdullayev
The article presents the developed artillery fire remote control complex using unmanned aerial vehicles (UAVs) based on an artificial intelligence (AI) algorithm. The developed complex for artillery fire control includes sensor modules for assessing the environment, collecting and processing information, planning and decision-making, and developing a command for the commander of an artillery battalion, division, or brigade. The main advantage of the developed artillery fire control system using UAVs based on an AI algorithm is the most rapid decision-making without human intervention, based on a quick assessment of the environment, the type of enemy weapons, and their category of importance, and an assessment of the distance to the enemy’s military arms. An algorithm is proposed to minimize the power of artillery fire to suppress the enemy.
Volume: 41
Issue: 1
Page: 83-89
Publish at: 2026-01-01

A hybrid divisive K-means framework for big data–driven poverty analysis in Central Java Province

10.11591/ijeecs.v41.i1.pp258-269
Bowo Winarno , Budi Warsito , Bayu Surarso
Clustering is essential in big data analytics, especially for partitioning high dimensional socioeconomic datasets to support interpretation and policy decisions. While K-Means is widely used for its simplicity and scalability, its strong sensitivity to initial centroid selection often leads to unstable results and slower convergence. Previous hybrid approaches, such as Agglomerative–K-Means, attempted to address this issue by using hierarchical clustering for centroid initialization; however, these methods rely on bottom-up merging, which can produce suboptimal initial partitions and increase computational overhead for larger datasets. To overcome these limitations, this study proposes a hybrid divisive–K-Means (DHC) model that employs top-down hierarchical splitting to generate more coherent initial centroids before refinement with K-Means. Using a multidimensional poverty dataset from Central Java Province provided by the Indonesian Central Bureau of Statistics (BPS), the performance of DHC was evaluated against standard K-Means and Agglomerative–K-Means. The assessment included execution time, convergence iterations, and cluster validity indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz). Experimental results demonstrate that DHC reduces execution time by up to 97% and requires 40% fewer iterations than standard K-Means, while achieving comparable or improved cluster quality (e.g., CH Index increasing from 14.3 to 15.8). These findings indicate that the DHC model offers a more efficient and stable clustering solution, addressing the shortcomings of previous standard K-Means methods and improving performance for large-scale socioeconomic data analysis.
Volume: 41
Issue: 1
Page: 258-269
Publish at: 2026-01-01

Detection of COVID-19 using chest X-rays enhanced by histogram equalization and convolutional neural networks

10.11591/ijeecs.v41.i1.pp387-393
Nazif Tchagafo , Abderrahmane Ez-Zahout , Ahiod Belaid
The persistent global health crisis initiated by the COVID-19 pandemic continues to demand robust and high-throughput diagnostic solutions. While gold-standard methods, such as polymerase chain reaction (PCR) testing, are accurate, their scalability and turnaround time remain limitations in high volume settings. This paper introduces a novel deep learning framework designed for rapid and accurate detection of COVID-19 from chest X-ray (CXR) imagery. Our methodology leverages a convolutional neural network (CNN) architecture, augmented by a crucial pre-processing stage: histogram equalization. This step is vital for enhancing the subtle contrast features inherent in CXR scans, there by significantly improving the quality of the input data and facilitating superior feature extraction by the CNN. The model was trained and rigorously validated on a dedicated dataset. Performance was systematically quantified using a comprehensive confusion matrix, yielding key metrics such as precision and specificity, alongside the receiver operating characteristic (ROC) curve. The achieved results are highly encouraging, demonstrating a classification accuracy of 98.45%. This innovative approach offers a substantial acceleration of the diagnostic process, providing a non-invasive and highly effective complementary tool for clinicians. Ultimately, this advancement has the potential to streamline patient management protocols and alleviate diagnostic pressures on global healthcare infrastructures.
Volume: 41
Issue: 1
Page: 387-393
Publish at: 2026-01-01

Design and construction of an Arduino-based baby incubator simulator using IoT

10.11591/ijeecs.v41.i1.pp99-108
Liza Rusdiyana , Joel Juanda Jamot Damanik , Bambang Sampurno , Suhariyanto Suhariyanto , Mahirul Mursid , Ika Silviana Widianti
This study aims to create a baby incubator simulator equipped with an internet of things (IoT)-based temperature control system using Arduino UNO. We use a DHT22 sensor to measure temperature and humidity, as well as fuzzy logic to ensure more accurate and responsive temperature control. The Thinger.io platform enables real-time monitoring and control of the incubator, providing flexibility and ease of supervision. With fuzzy logic, the temperature control system can handle changes and uncertainties in the incubator environment, providing a smoother response compared to traditional on-off methods. Testing shows that this system has a very low error rate, with an error value of only 0.97%, meaning that the measured temperature is almost identical to the actual conditions inside the incubator. Additionally, the authors used mice as a model for premature infants in the testing. The results showed that the mice's body temperature increased gradually and stably in line with the incubator conditions, reaching the desired temperature within 90 minutes. This demonstrates that our temperature control system is capable of maintaining optimal environmental conditions for premature infants.
Volume: 41
Issue: 1
Page: 99-108
Publish at: 2026-01-01

Cyber physical systems maintenance with explainable unsupervised machine learning

10.11591/ijeecs.v41.i1.pp300-308
V. Durga Prasad Jasti , Koudegai Ashok , Ramarao Gude , Prabhakar Kandukuri , Surendra Nadh Benarji Bejjam , Anusha B.
As cyber-physical systems (CPS) continue to play a pivotal role in modern technological landscapes, the need for robust and transparent machine learning (ML) models becomes imperative. This research paper explores the integration of explainable artificial intelligence (XAI) principles into unsupervised machine learning (UML) techniques for enhancing the interpretability and understanding of complex relationships within CPS. The key focus areas include the application of self-organizing maps (SOMs) as a representative unsupervised learning algorithm and the incorporation of interpretable ML methodologies. The study delves into the challenges posed by the inherently intricate nature of CPS data, characterized by the fusion of physical processes and digital components. Traditional black-box approaches in unsupervised learning often hinder the comprehension of model-generated insights, making them less suitable for critical CPS applications. In response, this research introduces a novel framework that leverages SOMs, a powerful unsupervised technique, while concurrently ensuring interpretability through XAI techniques. The paper provides a comprehensive overview of existing XAI methods and their adaptation to unsupervised learning paradigms. Special emphasis is placed on developing transparent representations of learned patterns within the CPS domain. The proposed approach aims to enhance model interpretability through the generation of human-understandable visualizations and explanations, bridging the gap between advanced ML models and domain experts.
Volume: 41
Issue: 1
Page: 300-308
Publish at: 2026-01-01

Convolutional neural network DenseNet in classifying dyslexic handwriting images

10.11591/ijeecs.v41.i1.pp220-232
Chelsea Zaomi Pondayu , Widodo Widodo , Murien Nugraheni
Dyslexia is a specific learning disability (SLD) associated with word-level reading difficulties and often manifests in childhood handwriting through irregular spacing and inconsistent letter sizing, due to shared phonological and orthographic processing. Early identification is critical; however, traditional diagnostic procedures are time-consuming and unsuitable for large-scale screening. This study aimed to develop a handwriting analysis at the paragraph-level using a DenseNet121 convolutional neural network (CNN) model as a low-cost dyslexia screening tool for resource-constrained educational settings. One hundred English handwriting images were preprocessed and standardized into two hundred samples, with 70% of the dataset evaluated using 4-fold cross-validation and the remaining 30% used for testing. The model achieved 90% test accuracy and 92.86% training accuracy, significantly outperforming a random forest baseline that reached 83.57% train accuracy and 63.33% test accuracy, with statistical significance confirmed by McNemar’s test. The main contribution of this study is the demonstration that a lightweight, single-architecture DenseNet121 using paragraph-level analysis can achieve competitive performance compared to prior studies that relied on more complex hybrid models and character-level analysis, while requiring substantially lower computational resources and simplified pipeline. These findings indicate that DenseNet121 provides a robust and low-cost solution for preliminary dyslexia screening in resource-limited educational environments.
Volume: 41
Issue: 1
Page: 220-232
Publish at: 2026-01-01

Invisible watermarking as an additional forensic feature of e-meterai

10.11591/ijeecs.v41.i1.pp344-356
H.A Danang Rimbawa , Sirojul Alam , Joko W. Saputro , Teddy Mantoro
The e-meterai is an official digital product of the Indonesian government issued by the Directorate General of Taxation (DGT). Its usage has become increasingly widespread as conventional documentation transitions to digital formats, serving the same function as its printed counterpart. This product features a quick-response code embedded with unique Indonesian codes and offers overt, covert, and forensic features. This study aims to experiment with adding a forensic feature in the form of an invisible watermark. We employed two watermark embedding techniques, discrete Fourier transform (DFT) and scale-invariant feature transform (SIFT), to determine which is more suitable for this application. After embedding the watermark, we also simulate various attacks including gaussian noise, salt and pepper noise, averaging filter, rotation, translation, and speckle noise. For each attack, we calculated with normalized-cross correlation (NCC) values, obtaining 0.863 and 0.976 for the gaussian noise attack, 0.929 and 0.984 for the salt and pepper attack, 0.975 and 0.984 for the averaging filter attack, 0.173 and 0.097 for rotation attacks, 0.172 and 0.032 for translation attack, and 0.972 and 0.996 for speckle noise attack, using DFT and SIFT techniques, respectively.
Volume: 41
Issue: 1
Page: 344-356
Publish at: 2026-01-01

Ultra-high isolation dual-port circular patch antenna at 2.4 GHz

10.11591/ijeecs.v41.i1.pp140-152
Meriem Boucif , Fayza Bousalah , Hayat Benosman
Reliable wireless communication in the 2.4 GHz industrial, scientific, and medical band increasingly relies on antenna systems that can provide high inter-port isolation in multiple-input multiple-output (MIMO) configurations. This paper presents a circular microstrip patch antenna and its extension to a dual-port MIMO configuration designed for 2.4 GHz operation. The antenna is implemented on a low-loss substrate and evaluated using full-wave electromagnetic simulations to assess impedance matching, radiation performance, and MIMO diversity metrics. To enhance inter-port isolation in the array, an inverted U-shaped defected ground structure (DGS) is introduced between the two radiating elements. The optimized design achieves excellent matching around 2.4 GHz and ultra-high isolation of approximately -78.7 dB, while maintaining stable gain and radiation patterns across the operating band. These results indicate that the proposed antenna offers a simple and effective solution for compact, energy-efficient, and robust 2.4 GHz MIMO front ends in internet of things (IoT) and other shortrange wireless communication systems.
Volume: 41
Issue: 1
Page: 140-152
Publish at: 2026-01-01

Weighted fine-tuned BERT-based sparse RNN for fake news detection

10.11591/ijeecs.v41.i1.pp331-343
Asha Kathigi , Gautam Vinod Nair , Kruthika Kadurahalli Raghu , Kavya Pujar Prakash , Meghana Deepak Duttargi
Fake news refers to misinformation or false reports shared in the form of images, articles, or videos that are disguised as real news to try to manipulate people’s opinions. However, detection systems fail to capture diverse features of fake news due to variability in linguistic styles, contexts, and sources, which lead to inaccurate identification. For this purpose, a weighted fine-tuned-bidirectional encoder representation for transformer based sparse recurrent neural network (WFT-BERT-SRNN) is proposed for fake news detection using deep learning (DL). Initially, data is acquired from Buzzfeed PolitiFact, Fakeddit, and Weibo datasets to evaluate WFT BERT-SRNN. Pre-processing is established using stopword removal, tokenization, and stemming to eliminate unwanted phrases or words. Then, WFT-BERT is employed to extract features. Finally, SRNN is employed to detect and classify fake news as real or fake. Existing techniques like deep neural networks for Fake news detection (DeepFake), BERT with joint learning, and multi-EDU structure for Fake news detection (EDU4FD), Image caption-based technique, and fine-grained multimodal fusion network (FMFN) are compared with WFT-BERT-SRNN. The WFT-BERT-SRNN achieves a better accuracy of 0.9847, 0.9724, 0.9624, and 0.9725 for Buzzfeed, Politifact, Fakeddit, and Weibo datasets compared to existing techniques like DeepFake, BERT-joint framework, EDU4FD, Image caption-based technique, and FMFN.
Volume: 41
Issue: 1
Page: 331-343
Publish at: 2026-01-01
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