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Journal of Computing Theories and Applications
Published by Universitas Dian Nuswantoro
ISSN : -     EISSN : 30249104     DOI : 10.62411/jcta
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Journal of Computing Theories and Applications (JCTA) is a refereed, international journal that covers all aspects of foundations, theories and the practical applications of computer science. FREE OF CHARGE for submission and publication. All accepted articles will be published online and accessed for free. The review process is carried out rapidly, about two until three weeks, to get the first decision. The journal publishes only original research papers in the areas of, but not limited to: Artificial Intelligence Big Data Bioinformatics Biometrics Cloud Computing Computer Graphics Computer Vision Cryptography Data Mining Fuzzy Systems Game Technology Image Processing Information Security Internet of Things Intelligent Systems Machine Learning Mobile Computing Multimedia Technology Natural Language Processing Network Security Pattern Recognition Signal Processing Soft Computing Speech Processing Special emphasis is given to recent trends related to cutting-edge research within the domain. If you want to become an author(s) in this journal, you can start by accessing the About page. You can first read the Policies section to find out the policies determined by the JCTA. Then, if you submit an article, you can see the guidelines in the Author Guidelines or Author Guidelines section. Each journal submission will be made online and requires prospective authors to register and have an account to be able to submit manuscripts.
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 96 Documents
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A Mobile App Development for E-Waste ‎Management in Bahrain (Athar)‎‎ Adwan, Ehab Juma; Mohamed, Noor; Bureshaid, Hayat; Mohamed, Batool
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10930

Abstract

Bahrain still suffers from electronic solid waste annually due to ineffective waste ‎disposal management. Compared to other Gulf Cooperation Council (GCC) countries, Bahrain ‎generates the largest waste quantity per person, approximately 1.2 to 1.8 ‎million tons of hazardous waste annually. Therefore, the present research article aims to manage the e-waste discarding mitigation by arranging the waste collection, ‎presenting it for sale/purchase/donation (C2C, C2B, B2G), and organizing the waste recycling ‎process. Derived from this aim, the article explores the e-waste ‎phenomena and proposes the development of an e-waste mobile application named “Athar”. ‎ The significance of “Athar” lies in firstly, enabling citizens to live in a ‎clean environment with limited diseases. Secondly, assisting the ‎Ministry of Tourism attract more tourists. ‎ The research article embraces a seven-phased Agile-based SDLC method to analyze, design, implement, test, and evaluate Athar through the utilization of several research techniques, including questionnaires to collect user and system requirements, other techniques such as data flow diagrams, entity relationship diagram, ‎database schema, etc. for the system design, ‎OutSystems programming language to implement the mobile app, and one questionnaire based on ‎Nielsen heuristics for usability evaluation. Findings demonstrate the adequacy of the Athar application with an outstanding usability score of 89.1%.
IoT Security Using Machine Learning Methods with Features Correlation Htwe, Chaw Su; Myint, Zin Thu Thu; Thant, Yee Mon
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11179

Abstract

The Internet of Things (IoT) is an innovative technology that makes our environment smarter, with IoT devices as an integral part of home automation. Smart home systems are becoming increasingly popular as an IoT service in the home that connects via a network. Due to the security weakness of many devices, the malware is targeting IoT devices. After being infected with malicious attacks on smart devices, they act like bots that the intruders can control. Machine learning methods can assist in improving the attack detection process for these devices. However, the irrelevant features raise the computation time as well as affect the detection accuracy in the processing with many features. We proposed a machine learning-based IoT security framework using feature correlation. The feature extraction scheme, one-hot feature encoding, correlation feature selection, and attack detection implement an active detection mechanism. The results show that the implemented framework is not only for effective detection but also for lightweight performance. The proposed system outperforms the results with the selected features, which have almost 100% detection accuracy. It is also approved that the proposed system using CART is more suitable in terms of processing time and detection accuracy.
Optimizing Cloud Computing Performance by Integrating the Novel PSBR Service Broker Policy and Load Balancing Algorithms Mon, Nan Kham
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11221

Abstract

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).
Enhancing Cloud Task Scheduling with Multi-Objective Optimization Using K-Means Clustering and Dynamic Resource Allocation Lwin, July
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11337

Abstract

The scheduling and resource allocation procedure is an essential component of cloud resource management. Effective resource allocation is severely hampered by the task arrival rates' erratic and unclear behavior. To prevent under or overusing resources, an effective scheduling strategy is necessary. To improve scheduling and allocation performance, a multi-objective optimization technique is presented for the best resource allocation and task scheduling inside scientific workflow datasets in a heterogeneous environment. In the first stage, the system calculates four key metrics: Communication Cost, Computation Cost, Earliest Finished Time on a particular VM, and Total Task Length for a specific scientific workflow dataset. These metrics provide a comprehensive understanding of the resource requirements and help make informed scheduling decisions. In the second stage, tasks are clustered using the K-Means clustering algorithm. This clustering groups similar tasks together, making managing and scheduling them easier. In the third stage, the proposed resource allocation algorithm allocates the clustered tasks to the appropriate VMs. This step ensures that the tasks are assigned to the best-suited resources, optimizing the overall system performance and resource utilization. By following this multi-stage process, the system aims to achieve optimal resource allocation and task scheduling, thereby improving the efficiency and effectiveness of cloud resource management. The proposed method significantly outperforms PSO, CSO, and GWO by consistently achieving lower Makespan—under 400 units at 50 tasks—while maintaining high resource utilization rates above 0.75, demonstrating superior efficiency in task execution and resource management.
Comprehensive Review of Security Problems in Mobile Robotic Assistant Systems: Issues, Solutions, and Challenges Dinh, Long Q.; Nguyen, Dung T.; Vu, Thang C.; Nguyen, Tao V.; Nguyen, Minh T.
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11408

Abstract

Nowadays, robots in the modern world are playing an important and increasingly popular role. MRA (Mobile Robotic Assistant) is a type of mobile robot designed to support humans in many different fields, helping to improve efficiency and safety in daily activities, work, or medical treatment. The number of MRAs is increasing and diverse in function, in addition to the ability to collect and process data, MRAs also have the ability to physically interact with users. Therefore, security is one of the important issues to improve the safety and effective operation of MRA. In this paper, through a comprehensive literature review and detailed analysis of the prominent MRA security attacks in recent years (based on criteria such as: attack targets, technologies used, impact level, feasibility, and contribution to addressing overall MRA security issues), a systematic classification by MRA activity fields is conducted. Security attacks, threats, and vulnerabilities are examined from various perspectives, such as hardware attacks or network/system-level attacks, operating systems/application software. Additionally, corresponding security solutions are proposed, compared, and evaluated to enhance MRA security. The paper also addresses challenges and suggests open research directions for the future.
Leveraging Variational Quantum-Classical Algorithms for Enhanced Lung Cancer Prediction Adebayo, Philip Omoniyi; Basaky, Frederick; Osaghae, Edgar
Journal of Computing Theories and Applications Vol. 2 No. 3 (2025): JCTA 2(3) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10424

Abstract

This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.
A Cubical Persistent Homology-Based Technique for Image Denoising with Topological Feature Preservation Al-Imran, Md.; Liza, Mst Zinia Afroz; Shiraj, Md. Morshed Bin; Murshed, Md. Masum; Akhter, Nasima
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11488

Abstract

Image denoising is a fundamental challenge in image processing, where the objective is to remove noise while preserving critical image features. Traditional denoising methods, such as Wavelet, Total Variation (TV) minimization, and Non-Local Means (NLM), often struggle to maintain the topological integrity of image features, leading to the loss of essential structures. This study proposes a Cubical Persistent Homology-Based Technique (CPHBT) that leverages persistence barcodes to identify significant topological features and reduce noise. The method selects filtration levels that preserve important features like loops and connected components. Applied to digit images, our method demonstrates superior performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 46.88 and a Structural Similarity Index Measure (SSIM) of 0.99, outperforming TV (PSNR: 21.52, SSIM: 0.9812) and NLM (PSNR: 22.09, SSIM: 0.9822). These results confirm that cubical persistent homology offers an effective solution for image denoising by balancing noise reduction and preserving critical topological features, thus enhancing overall image quality.
Enhanced Multi-Class Skin Lesion Classification of Dermoscopic Images Using an Ensemble of Deep Learning Models Zaw, Kyi Pyar; Mon, Atar
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11530

Abstract

This study presents an advanced approach to multi-class skin lesion classification by leveraging an ensemble model comprising the Inception-V3, ResNet-50, and VGG16 architectures. The classification task focuses on categorizing skin lesions into distinct classes, including Melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), using the ISIC dataset, a comprehensive collection of dermoscopic images. In order to properly balance the dataset, the oversampling strategy is utilized, as some lesion types are underrepresented due to inherent imbalances in the dataset. By ensuring that the model is trained on a more representative dataset, this balancing improves the algorithm's capacity to categorize all lesion types properly and impartially. By combining the complementary features of ResNet-50, Inception-V3, and VGG16, the ensemble technique improves the overall classification performance. ResNet-50 is chosen for its deep feature extraction capabilities, which help capture fine details in lesion patterns. Inception-V3 is selected for its multi-scale processing, allowing it to effectively analyze lesions at varying resolutions and sizes. VGG16 is included due to its simple yet highly effective architecture for image classification tasks. The ensemble model with data augmentation significantly outperforms individual models in skin lesion classification for both the original and balanced ISIC datasets regarding accuracy, precision, recall, and F1-score. This method offers a robust solution for skin lesion classification, contributing to more accurate and reliable diagnostic tools in dermatology.
Comprehensive Evaluation of LDA, NMF, and BERTopic's Performance on News Headline Topic Modeling Babalola, Olusola; Ojokoh, Bolanle; Boyinbode, Olutayo
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11635

Abstract

Topic modeling is an integral text mining component, employing diverse algorithms to uncover hidden themes within texts. This study examines the comparative performance of prominent topic modeling techniques on news headlines, which is characterized by brevity and specific linguistic style. Given the corpus originates from a non-native English-speaking country, an additional layer of complexity is introduced to the task. Our research explores the feasibility of employing a committee approach for topic modeling, evaluating the efficacy and challenges of various methods in practical settings. We applied three techniques—Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and BERTopic—to create models with a fixed number of topics (n=40). These models were then tested on approximately 150,000 news headlines. To assess topic coherence, we utilized Word2Vec, human evaluators, and two large language models. Statistical tests confirmed the significance and impact of our findings. BERTopic demonstrated superior coherence compared to NMF, though slightly, but consistently outperformed NMF and LDA according to human and LLM evaluations. The notable disparity in LDA's performance relative to BERTopic and NMF underscores the importance of carefully selecting a topic modeling technique, as the choice can significantly influence the outcome of the analysis.
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction Setiadi, De Rosal Ignatius Moses; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Warto, Warto; Gondohanindijo, Jutono; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

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