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29,939 Article Results

Analysis and modeling of a pneumatic artificial muscle system

10.11591/ijeecs.v39.i2.pp874-884
Vinh-Phuc Tran , Nhut-Thanh Tran , Chi-Ngon Nguyen , Chanh-Nghiem Nguyen
Hysteresis is a common challenge in achieving precise position control of pneumatic artificial muscles (PAMs). Accurate modeling of this phenomenon is essential for the development of efficient PAM control systems. This study evaluates four mathematical models for modeling PAM dynamics: Nonlinear AutoRegressive with eXogenous inputs (NARX), BoxJenkins (BJ), Prandtl-Ishlinskii (PI), and second-order underdamped system and one zero (P2UZ). To assess the effectiveness of these models, experiments were conducted with reference input signals of varying amplitudes. The accuracy and goodness of fit of these models were evaluated based on root mean square error (RMSE) and coefficient of determination. Results show that the P2UZ model achieved the highest fitness (97.15%) and the lowest RMSE (1.80 mm), followed closely by the NARX model with 96.83% fitness and an RMSE of 1.90 mm. The PI and BJ models demonstrated lower performance, with the BJ model showing the lowest fitness (90.79%) and the highest RMSE (3.25 mm). These findings provide valuable insights for improving PAM control and PAM-based automation systems by highlighting the strengths and limitations of each model.
Volume: 39
Issue: 2
Page: 874-884
Publish at: 2025-08-01

Impact of multipath delay and co-channel interference on MIMO and STBC-MIMO

10.11591/ijeecs.v39.i2.pp941-951
Ujwala Bongale , Vinayak Patil , Ganesh Sable
This paper presents a comparative analysis of conventional multiple-input multiple-output (MIMO) systems and space-time block coding (STBC) enhanced MIMO systems across three distinct wireless channel scenarios: flat Rayleigh fading, multipath delay, and multipath delay with co-channel interference. The ability of STBC to exploit multiple independent signal paths between the transmitter and receiver reduces the likelihood of signal fading, which is tried to represent through this work. Using MATLAB simulations, we evaluate system performance under realistic channel conditions, focusing on the key metric of bit error rate (BER). Results show that STBC significantly improves reliability and reduces BER compared to conventional MIMO, particularly under multipath and interference-laden environments.
Volume: 39
Issue: 2
Page: 941-951
Publish at: 2025-08-01

Interpretable machine learning for academic risk analysis in university students

10.11591/ijai.v14.i4.pp3089-3098
Mukti Ratna Dewi , Mochammad Reza Habibi , Bassam Babgei , Lovinki Fitra Ananda , Brodjol Sutijo Suprih Ulama
Higher education institutions often grapple with issues related to academic risk among their students. These academic risks encompass low academic performance, study delays, and dropouts. One approach to address these challenges is to predict students’ academic performance as accurately as possible by leveraging advanced computational techniques and utilizing academic and non-academic student data. This research aims to develop a model that accurately identifies students with high potential for academic risk while explaining the contributing factors to this phenomenon in the Faculty of Vocational Studies, Institut Teknologi Sepuluh Nopember (ITS). The prediction model is constructed using the light gradient boosting machine (LightGBM) method and is subsequently interpreted using the Shapley additive explanations (SHAP) value. Additionally, an oversampling method, based on synthetic minority oversampling technique (SMOTE), is implemented to address imbalances in the dataset. The proposed approach achieves 96% and 97% accuracy and specificity rates, respectively. Analysis based on SHAP values reveals that extracurricular activities, choice of major, smoking habit, gender, and friendship circle are among the top five factors impacting students’ academic risk.
Volume: 14
Issue: 4
Page: 3089-3098
Publish at: 2025-08-01

Solving k-city multiple travelling salesman using genetic algorithm

10.11591/ijai.v14.i4.pp2741-2752
Alikapati Prakash , Uruturu Balakrishna , Thangaraj Manogaran , Thenepalle Jayanth Kumar
This paper addresses a novel variant of the classical multiple traveling salesman problem (MTSP) i.e. k-city multiple traveling salesman problem (k-MTSP). The problem can describe as follows. Let there are n cities, m salesman positioned at depot city and a predefined positive value k. The distance between each pair of cities is known. The objective of the k-MTSP is to determine a collection of m closed tours for salesman, which covers exactly k (including depot city) of n cities such that the total distance covered is minimum. The k-MTSP can be seen as a combination of both subset selection and permutation characteristics. From the through literature review, it is found that this study on k-MTSP is first of its kind to the best of author’s knowledge. The paper introduces a zero-one integer linear programming (0-1 ILP) formulation alongside an efficient genetic algorithm (GA), designed to address k-MTSP. No comparative studies carried out due to the absence of existing studies on k-MTSP. However, the developed GA is tested over various benchmark test cases from TSPLIB and results are reported, which may potentially serve as basis for further comparative studies. Overall findings demonstrate that the GA consistently produces best solutions within reasonable computational times for relatively smaller and medium test cases, suggesting its robustness and effectiveness in tackling the k-MTSP. However, to enhance consistency and efficiency, particularly for larger datasets, further algorithm improvements are necessary.
Volume: 14
Issue: 4
Page: 2741-2752
Publish at: 2025-08-01

Comparison of robust machine learning algorithms on outliers and imbalanced spam data

10.11591/ijeecs.v39.i2.pp1130-1144
Dodo Zaenal Abidin , Jasmir Jasmir , Errisya Rasywir , Agus Siswanto
Effective spam detection is essential for data security, user experience, and organizational trust. However, outliers and class imbalance can impact machine learning models for spam classification. Previous studies focused on feature selection and ensemble learning but have not explicitly examined their combined effects. This study evaluates the performance of random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost) under four experimental scenarios: (i) without synthetic minority over-sampling technique (SMOTE) and outliers, (ii) without SMOTE but with outliers, (iii) with SMOTE and without outliers, and (iv) with SMOTE and with outliers. Results show that XGBoost achieves the highest accuracy (96%), an area under the curve-receiver operating characteristic (AUCROC) of 0.9928, and the fastest computation time (0.6184 seconds) under the SMOTE and outlier-free scenario. Additionally, RF attained an AUCROC of 0.9920, while GB achieved 0.9876 but required more processing time. These findings emphasize the need to address class imbalance and outliers in spam detection models. This study contributes to developing more robust spam filtering techniques and provides a benchmark for future improvements. By systematically evaluating these factors, it lays a foundation for designing more effective spam detection frameworks adaptable to real-world imbalanced and noisy data conditions.
Volume: 39
Issue: 2
Page: 1130-1144
Publish at: 2025-08-01

Designing an automated matching model to enhance recruitment process

10.11591/ijeecs.v39.i2.pp1081-1091
Sahar Idwan , Ebaa Fayyoumi , Haneen Hijazi , Izzeddin Matar
Detecting qualified candidates for a vacant position is a difficult task, especially when there are numerous applicants. This delays team development in finding the appropriate individual at the right moment. Adopting a well-structured selection process will create opportunities for new aspects and ideas. In this paper, the matching job applicant (MJA) model is developed to assist all parties, the employers and the employees simultaneously by providing a fair, transparent unbiased solution constructed by using a mathematical machine. This provides a clear justification in the decision-making process in addition to advising the applicants with the most suitable positions that fits their qualifications.
Volume: 39
Issue: 2
Page: 1081-1091
Publish at: 2025-08-01

Development of mobile-based Batak script recognition application using YOLOv8 algorithm

10.11591/ijeecs.v39.i2.pp1013-1026
Iustisia Natalia Simbolon , Herimanto Herimanto , Ranty Deviana Siahaan , Samuel Adika Lumbantobing , Grace Natalia Br Sitepu
The Batak people are one of the ethnic groups that pass down many values and traditions to each generation, including the written tradition known as the Batak script. The Batak Toba people, in particular, have the Batak Toba script as part of their local wisdom that needs to be preserved and maintained. However, the use of the Batak script has significantly declined in the current era. To prevent the loss of this heritage, preservation through technology is necessary. This research utilizes a deep learning approach using the YOLOv8 algorithm to detect images of script objects, provide the coordinates of the script locations, and perform object recognition based on the dataset. The final result of this research is an Android-based application that can detect the Batak Toba script in real time and upload images. The research process involves experiments on several hyperparameters, such as epochs with a value of 200, confidence threshold, and IoU with a value of 0.5. The model evaluation shows excellent results, with a precision of 0.945, recall of 0.902, mAP@0.5 of 0.954, and a high confidence score from the application's detection.
Volume: 39
Issue: 2
Page: 1013-1026
Publish at: 2025-08-01

An implementation of GAN analysis for criminal face identification system

10.11591/ijeecs.v39.i2.pp963-972
Ayesha Sarosh , Govindu Komali , Vishnu Vardhan Battu , Laxmaiah Kocharla , Eswaree Devi Kopparavuri , Ooruchintala Obulesu , Praveen Mande , Amanulla Mohammad
In recent times, the criminal activities are growing at an exponential rate. For the prevention of crime, one of the main issues that are before the police are accurate identification of criminals and on the other hand the availability of police officers are not adequate. The most tedious task is tracking the suspect once a crime was committed. Over the years, several technical solutions have been presented to detect the criminals however most of them were not effective. One of the most significant characteristics for the identification of a person is face. Even identical twins have their own unique faces. Face identification is a challenging topic in computer vision because the human face is a dynamic entity with a high degree of visual variation. In this area, identification accuracy and speed are significant challenges. Hence to solve these issues, an implementation of generative adversarial network (GAN) analysis for criminal face identification system is presented. GAN is used for the identification of criminals. Recall, precision, accuracy, and F1-score are used to assess the performance of the presented technique. Compared to previous models, this model will achieve better performance for criminal face detection.
Volume: 39
Issue: 2
Page: 963-972
Publish at: 2025-08-01

Real-time recognition of Indonesian sign language SIBI using CNN-SVM model combination

10.11591/ijeecs.v39.i2.pp1198-1210
Satriadi Putra Santika , Stefanus Benhard , Yulyani Arifin , Andry Chowanda
Real-time Sistem Isyarat Bahasa Indonesia (SIBI) sign language recognition plays a crucial role in improving accessibility for individuals with hearing and speech impairments. Despite advancements in SIBI recognition research, challenges remain in ensuring model stability and accuracy in realtime settings, particularly in handling gesture variations and classification inconsistencies. This study addresses these challenges by developing a convolutional neural network-support vector machine (CNN-SVM) combination model, integrating MediaPipe for hand coordinate extraction, CNN for feature extraction, and SVM for classification. To improve generalization and prevent overfitting, data augmentation is applied to expand the dataset. The model's performance is further enhanced through hyperparameter optimization (HPO) and post-processing techniques such as multi-window majority voting (MWMV) and SymSpell. Experimental results show that the CNN-SVM model trained on augmented data with HPO achieves 91% testing accuracy, outperforming both standalone CNN and SVM models. Furthermore, MWMV improves recognition stability, while SymSpell enhances spelling errors, ensuring more meaningful outputs. The system is integrated with OpenCV for real-time recognition, but current deployment remains limited to local execution. Future work will focus on developing lightweight models for web-based and mobile applications, making the system more accessible and scalable.
Volume: 39
Issue: 2
Page: 1198-1210
Publish at: 2025-08-01

Enhancing the effectiveness of CAPTCHA using an improved visual cryptography scheme

10.11591/ijeecs.v39.i2.pp1121-1129
Chihi Hasnae , Chahboun Asaad
Traditional CAPTCHA systems, designed to distinguish humans from bots, are increasingly ineffective due to advancements in artificial intelligence (AI), particularly deep learning and optical character recognition (OCR) technologies, which enable bots to bypass these systems. This paper proposes a new CAPTCHA authentication method that combines enhanced visual cryptography with traditional techniques to improve security. Visual cryptography divides information into visually distinct shares, reinforcing CAPTCHA’s defenses against automated attacks, especially those using deep learning. This approach not only strengthens security but also improves user experience by adjusting the time required to complete CAPTCHA challenges, addressing usability concerns associated with traditional systems. Overall, the proposed method offers a more secure, efficient, and user friendly solution for online authentication.
Volume: 39
Issue: 2
Page: 1121-1129
Publish at: 2025-08-01

Study of design thinking and software engineering integration in education and training

10.11591/ijeecs.v39.i2.pp1384-1398
Muhammad Ihsan Zul , Suhaila Mohd. Yasin , Dadang Syarif Sihabudin Sahid
Integrating design thinking (DT) with software engineering (SE) is widely applied in industry, serving as a reference for SE in education and training. The industry has various integration models, but researchers and educators mainly adapt them for education. A clear understanding of DT-SE integration models is essential to figuring out their implementation. This study examines existing DT-SE integration models, challenges, and integration methods using Kitchenham’s framework in education and training. The paper was collected from ScienceDirect, IEEEXplore, Scopus, ACM, SpringerLink, and Google Scholar, yielding 593 initial publications, with 43 selected for in-depth analysis. Findings indicate that the d.school model is the most widely adopted DT model. Key challenges include team dynamics, process management, complexity, and cultural factors. DT is integrated into requirements engineering (RE) due to its user-centered nature, though only two studies explicitly describe DT-SE integration models, both applied early in SE processes. These findings suggest educational practices align with industry trends in model adoption and integration focus. Educators and practitioners can use these insights to design or adapt integration models suitable for education and training by shaping curricula that emphasize user-centered design, collaboration, and the extension of DT practices beyond RE-strengthening its impact for education and training.
Volume: 39
Issue: 2
Page: 1384-1398
Publish at: 2025-08-01

The development of contextual chat interactions with retrieval-augmented generation system for facilitating learning hadith

10.11591/ijeecs.v39.i2.pp987-995
Rio Nurtantyana , Yudi Priyadi , Eko Darwiyanto
This study explores the development and implementation of a retrieval-augmented generation (RAG) system using the large language model (LLM) to enhance the learning of hadith through a chat interface for high school students. This study addresses challenges in optimizing RAG configurations and problems associated with traditional educational methods that lack interactivity. In addition, the RAG system was designed to replace real teacher interactions, offering a chat feature that provides contextual answers to real-life scenarios related to Hadith. Various configurations were tested, with a focus on the Matn component, achieving a high accuracy score with a mean of .754 and demonstrating efficiency in context relevance with a mean of .797. Results indicated significant accessibility using our RAG system for learning hadith via WhatsApp’s chat interface. Hence, this study highlights the potential of RAG systems in transforming educational environments and offers insights into the development of technology for interactive Hadith learning solutions.
Volume: 39
Issue: 2
Page: 987-995
Publish at: 2025-08-01

Advanced deep attention neural inference network for enhanced arrhythmia detection and accurate classification

10.11591/ijeecs.v39.i2.pp1164-1175
H. Sumitha , M. Devanathan
Arrhythmias are irregular heartbeats that can lead to severe health risks, including sudden cardiac death, necessitating accurate and timely detection for effective treatment. Traditional diagnostic methods such as stress tests, resting electrocardiograms (ECGs), and 24-hour Holter monitors are limited by their monitoring capacity and often result in delayed diagnoses, compromising patient safety. To address these challenges, this paper introduces the deep attention neural inference network (DANIN) methodology. DANIN integrates one-dimensional ECG signals with two-dimensional spectral images using multi-modal feature fusion, capturing comprehensive cardiac information in both temporal and frequency domains. The methodology employs advanced deep attention network-based models for superior feature extraction, recognizing intricate patterns and long-range dependencies within the data. Additionally, the inclusion of an inference model system enhances interpretability and usability, making the model highly suitable. Further, DANIN is evaluated considering the MIT-BIH dataset, and extensive comparative analysis with state-of-the-art techniques demonstrates that DANIN significantly improves accuracy, precision, recall, and F1-score, highlighting its potential to revolutionize arrhythmia detection and improve patient outcomes.
Volume: 39
Issue: 2
Page: 1164-1175
Publish at: 2025-08-01

Digital and academic libraries through cloud computing

10.11591/ijeecs.v39.i2.pp896-905
Karthika Sivanandham , Dominic John , Sivankalai Sivankalai
In an era characterized by the dominance of digital information, libraries have undergone significant transformations, evolving from traditional brickand-mortar institutions to dynamic hubs of digital knowledge. The emergence of digital libraries, which give users access to vast collections of digital resources, has facilitated this evolution. However, effective management of digital resources poses numerous challenges, including issues related to storage, preservation, and accessibility. In response, cloud computing has developed as a powerful solution for addressing these challenges and revolutionizing how libraries operate. Cloud computing reduces the need for expensive infrastructure expenditures and increases flexibility and scalability by allowing libraries to store, manage, and access digital resources remotely over the internet. This paper examines the intersection of digital libraries and cloud computing, examining the role of cloud computing in modern libraries and its implications for the future of information management. By analyzing current trends, case studies, and best practices, this paper provides insights into the benefits and challenges of adopting cloud computing in the context of academic libraries.
Volume: 39
Issue: 2
Page: 896-905
Publish at: 2025-08-01

Handling missing values and clustering industrial liquid waste using K-medoids

10.11591/ijeecs.v39.i2.pp1411-1420
Ratih Hafsarah Maharrani , Prih Diantono Abda'u , Ganjar Ndaru Ikhtiagung , Nur Wahyu Rahadi , Zaenurrohman Zaenurrohman
The textile industry is a significant contributor to environmental pollution due to its wastewater, which contains hazardous substances such as dyes, heavy metals, and chemicals that can severely harm aquatic ecosystems. Effective management of this wastewater is crucial to mitigate its environmental impact. This study focuses on classifying industrial liquid waste data using the K-medoids clustering method, chosen for its robustness to noise and outliers compared to K-means. To address challenges in wastewater data processing, such as missing values and varying data scales, two approaches are compared: replacing missing values with zero and K-nearest neighbors (KNN) imputation, alongside Z-score normalization for data uniformity. The clustering quality is evaluated using the Davies-Bouldin index (DBI) for cluster variations of k=2, 3, 4, and 5. The results show that the best clustering quality is achieved at k=2, with the smallest DBI values obtained using KNN imputation (0.139) and zero replacement (0.149). The superior performance of KNN imputation highlights its effectiveness in handling missing data. These findings provide valuable insights into the characteristics of textile industry wastewater pollution, offering a robust framework for effective wastewater management. The study concludes with practical recommendations for policymakers and industry stakeholders to adopt advanced data-driven approaches for sustainable wastewater treatment strategies.
Volume: 39
Issue: 2
Page: 1411-1420
Publish at: 2025-08-01
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