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Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 518 Documents
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Firefly Algorithm-Optimized Deep Learning Model for Cyber Intrusion Detection in Wireless Sensor Networks Using SMOTE-Tomek Hamad, Noor Abdulkaadhim; Jasem, Oras Nasif
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.717

Abstract

Wireless Sensor Networks are increasingly vulnerable to sophisticated cyber threats, necessitating effective and intelligent intrusion detection strategies. This paper presents a deep learning-based intrusion detection model that enhances cybersecurity performance through intelligent hyperparameter optimization and advanced data balancing. The main objective of the study is to improve classification accuracy and generalization in intrusion detection systems by employing a dynamic and adaptive optimization framework. A Firefly Algorithm that mimics nature is part of the suggested model. It changes the number of neurons, learning rate, and dropout rate while evaluating the performance of every arrangement with just a little training. It uses the strategy of swarms to find solutions effectively and in an adaptable way. A hybrid method called SMOTE-Tomek is employed to deal with the issues caused by an unequal number of classes in the dataset. The network is built with different dense layers that are enhanced with dropout and batch normalization, and adaptive learning rate adjustment. In preprocessing the data, we encoded categorical variables, made the values consistent with normalization, and balanced the classes by producing artificial data when needed. The model was trained using GPU software for ten epochs and checked for performance using accuracy measurements, confusion matrices, and classification reports. The optimized model obtained an accuracy of 97.82% in classifications, higher than what baseline models and previous machine learning methods could do. It is able to spot and classify various kinds of attacks, completely handles Flooding cases and greatly lowers the chances of mistakes when identifying Blackhole and Grayhole. The study underlines the fact that using swarm intelligence with hybrid resampling enhances the real-time protection of networks against cyber attacks. A deep learning framework is developed at the end of the study that can work well and effectively in cybersecurity tasks.
Formalization of Morphological Rules for Kazakh Nouns in the New Latin Alphabet Zhetkenbay, Lena; Sharipbay, Altynbek; Razakhova, Bibigul; Bekmanova, Gulmira; Barlybayev, Alibek; Nazyrova, Aizhan; Yergesh, Banu
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.820

Abstract

This study presents a hybrid computational model for formalizing and predicting morphological inflections of Kazakh nouns written in the new Latin alphabet. The motivation stems from limitations in previous systems based on Cyrillic orthography, which often misrepresented key phonological features such as vowel harmony and consonant assimilation. The main objective is to develop a linguistically informed and computationally efficient system to support Natural Language Processing (NLP) for Kazakh during its transition to Latin script. The methodology combines rule-based grammar formalization with a machine learning approach, specifically a Bayesian Regulation Backpropagation Neural Network (BR-BPNN). A manually curated dataset of 1,000 Latin-script Kazakh nouns was annotated for various morphological forms. Each word was encoded at the character level using a custom dictionary (kazlat_dict), capturing the final four letters as feature vectors. Formal logic and regular expressions were used to model morphological rules such as pluralization and case endings, incorporating vowel harmony, consonant softness, and sonority. These rules provided the training labels for the BR-BPNN model. The trained model achieved 91.5% accuracy, 89.4% precision, and a correlation coefficient (R) above 0.98, confirming the effectiveness of the hybrid system. A user interface prototype was developed to demonstrate practical utility, enabling users to input root nouns and receive suffix predictions with confidence scores and linguistic explanations. The novelty of this work lies in integrating linguistic theory with machine learning for a low-resource Turkic language. It offers a foundation for intelligent Kazakh language tools including spell checkers, grammar correctors, and educational platforms. Future work will extend the system to other parts of speech and explore contextual modeling to improve handling of ambiguous or irregular forms.
The Use of Artificial Intelligence in Accounting Classes: Behavioral Insights from Students Fachrurrozie, Fachrurrozie; Nurkhin, Ahmad; Santoso, Jarot Tri Bowo; Asrori, Asrori; Harsono, Harsono
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.781

Abstract

Accounting learning has entered a new transformation with the intensive use of artificial intelligence. This study analyzes student behavior in using artificial intelligence in accounting lectures. The Unified Theory of Acceptance and Use of Technology (UTAUT) framework is used to understand student behavior by adding the construct of experience and competence of information and communication technology. This study contributes to the extended application of UTAUT. This survey took a sample of accounting and accounting education students at the Faculty of Economics and Business, Universitas Negeri Semarang, totaling 124 students. The questionnaire distributed via Google Forms was used as a data collection technique. The data analysis technique used was SEM-PLS. The results of the analysis show moderate student behavior in using artificial intelligence in accounting lectures, there is 2.48 of an average score. ChatGPT and Canva are the types of AI most frequently used by students in accounting courses. SEM-PLS analysis indicates that students' intentions to use artificial intelligence in their accounting lectures are more determined by performance and effort expectancy. The coefficients are 0.519 and 0.382 at a P-value of 0.000. Social influence does not have a significant effect on student intentions, with a P-value of 0.104. Student intentions, ICT experience, and ICT competence significantly influence student behavior to use AI. The coefficients are 0.382, 0.241, and 0.214 at a P-value less than 0.05. Facilitating conditions do not have a substantial effect on actual behavior, with a P-value of 0.210. The practical implication of this study is the importance of highlighting students' ICT experiences and competencies that determine the use of AI for lecture purposes.
Enhancing SMOTE Using Euclidean Weighting for Imbalanced Classification Dataset Ramadhan, Nur Ghaniaviyanto; Maharani, Warih; Gozali, Alfian Akbar; Adiwijaya, Adiwijaya
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.798

Abstract

Class imbalance is a significant challenge in machine learning classification tasks because it often causes models to be biased toward the majority class, resulting in poor detection of minority classes. This study proposes a novel enhancement to the Synthetic Minority Over-sampling Technique (SMOTE) by incorporating Euclidean distance-based feature weighting, called Weighted SMOTE. The key idea is to improve the quality of synthetic minority samples by calculating feature importance using a Random Forest model and assigning higher weights to the most relevant features. The objective of this research is to generate more representative synthetic data, reduce model bias, and increase predictive accuracy on highly imbalanced datasets. Experiments were conducted on four benchmark datasets from the KEEL Repository with imbalance ratios ranging from 0.013 to 0.081. The proposed Weighted SMOTE combined with an ensemble voting classifier (Random Forest, AdaBoost, and XGBoost) demonstrated significant improvements compared to standard SMOTE and models without resampling. For example, on the Zoo-3 dataset, the Balanced Accuracy Score (BAS) increased from 75% to 90%, while the F1-score improved from 48% to 94%. On the Cleveland-0_vs_4 dataset, precision improved from 83% to 91% and recall remained high at 99%. Statistical testing using the Wilcoxon signed-rank test confirmed these improvements with p-values 0.05 for key metrics. The findings show that the proposed method effectively balances sensitivity and precision, generates more meaningful synthetic samples, and reduces the risk of overfitting compared to conventional oversampling. The novelty of this work lies in integrating Euclidean-based feature weighting into the SMOTE process and validating its performance on multiple domains with varying feature types and imbalance ratios. These results indicate that the proposed Weighted SMOTE approach contributes a practical solution for improving classification performance and model stability on severely imbalanced data.
Data-Driven Optimization of UPQC Performance for Solar PV Systems in Weak Grids Using Simulation and Predictive Modeling Rajasree, R.; Lakshmi, D.; Batumalay, M.
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.742

Abstract

The integration of solar photovoltaic (PV) systems into weak power grids presents significant challenges due to low short circuit ratios (SCR), resulting in voltage instability, high harmonic distortion, and diminished fault tolerance. This study proposes a data-driven framework to enhance grid stability and power quality by employing a Unified Power Quality Conditioner (UPQC) integrated with Proportional-Integral (PI) controllers. A comprehensive simulation model was developed using MATLAB/Simulink and validated through hardware-in-the-loop (HIL) experiments. Key electrical performance metrics—such as voltage profiles, total harmonic distortion (THD), and reactive power—were collected and analyzed. To enhance system insight, the dataset was further processed using statistical analysis and predictive modeling techniques to evaluate control response under varying solar irradiance and load conditions. The results demonstrate that the UPQC system maintains stable voltage, reduces THD to within IEEE-519 standards, and improves power factor to 0.98. This research highlights the potential of combining power electronics control with data-centric evaluation to ensure reliable renewable energy integration in weak grid environments. The proposed system contributes toward developing intelligent grid-support solutions for sustainable energy transitions and process innovation.
A Hybrid CNN-Transformer Model with Quantum-Inspired Fourier Transform for Accurate Skin Disease Classification S, Aasha Nandhini; Manoj, R. Karthick; Batumalay, M.
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.782

Abstract

Skin disease classification is a complex task that requires robust feature extraction, efficient classification, and interpretability. Artificial intelligence-based technologies offer effective solutions for developing a framework for skin disease classification while ensuring explainability for healthcare professionals. This study proposes a novel Hybrid Transformer model comprising of Convolutional Neural Network (CNN) architecture infused with a Quantum-Inspired Fourier Transform (QIFT) to enhance classification accuracy. QIFT is incorporated to emphasize frequency-domain information alongside the spatial features captured by CNNs, potentially improving feature representation and model generalization. For demonstration, a dataset containing four different classes of dermatological images is used. Data augmentation techniques and adaptive learning rate scheduling are employed to optimize the dataset. A weighted cross-entropy loss function is used to address class imbalances in the dataset. In this research, explainability is implemented using a standard attribution technique like Integrated Gradients providing insights into model decision-making, and enhancing trust in medical applications. Performance evaluation involves validating the proposed framework using metrics such as confusion matrix analysis, classification reports, and training-validation curves. Experimental results demonstrate a high classification accuracy of 92.5% across skin disease categories. The findings indicate that integrating QIFT and CNN-based feature extraction with transformer-driven attention mechanisms enhances skin disease classification performance while ensuring interpretability as process innovation.
A Study of Unified Framework for Extremism Classification, Ideology Detection, Propaganda Analysis, and Flagged Data Detection Using Transformers Balajia, R S Lakshmi; Thiruvenkataswamy, C S; Batumalay, Malathy; Duraimutharasan, N.; Devadas, Amar Dev Thirukulam; Yingthawornsuk, Thaweesak
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.702

Abstract

The rise of extremism and its rapid dissemination through propaganda channels have become pressing global challenges, threatening peace, security, and social cohesion. This study aligns with the United Nations Sustainable Development Goal 16 by proposing a unified framework leveraging advanced machine learning and large language models to combat extremism through extremism classification, ideology detection, propaganda analysis, and flagged word recognition. This framework introduces process innovation by integrating state-of-the-art transformer models such as BERT, RoBERTa, DistilBERT and XLNet to streamline the analysis process and overcome traditional limitations in extremism detection with exceptional performance: 90.00% accuracy for extremism classification, 98.82% accuracy for ideology detection, and 99.71% accuracy for flagged word recognition. While the proposed approach demonstrates high precision and recall, it faces challenges such as potential data bias, ethical concerns in dataset usage and the risk of false positives, which could lead to misclassification of benign content. The inclusion of multilingual capabilities broadens the applicability of the framework but variations in linguistic structures and cultural contexts introduce complexities in model generalization. Additionally, ethical considerations in handling extremist content, especially in social media data collection, necessitate stringent privacy safeguards to prevent unintended harm. By providing actionable insights, this research contributes to counter-extremism efforts in areas such as online content moderation, law enforcement and intelligence analysis, laying a foundation for future advancements in safeguarding global security which enhance the process innovation.
Human Shoulder Posture Anthropometry System for Detecting Scoliosis Using Mediapipe Library Hustinawaty, Hustinawaty; Rumambi, Tavipia; Hermita, Matrissya
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.870

Abstract

The system proposed in this research is a posture detection system using real-time computer vision technology with system limitations aimed at detecting shoulder posture as part of anthropometric measurements, because if the shoulder posture is unbalanced and has a very significant height difference, it is called an indication of scoliosis. This research aims to facilitate the detection of scoliosis, especially in one of its symptoms, namely shoulder asymmetry with anthropometric measurements of the ‘Elbow-to-Elbow breadth’ position using the scoliomter method. In addition, common screening methods that can be used for scoliosis, especially in adolescents, include the Adams forward bend test, Cobb angle measurement, and Moire measurement. The anthropometric shoulder posture detection system includes the stages of preparation for detection using a webcam with T-position calibration, then MediaPipe Library processes 33 keypoints, OpenCV and Python to analyze body movements in real time, then this asymmetry is calculated using standard algorithms for pose prediction, vector projection and atan2 to obtain asymmetry angle information. The results of testing the shoulder detection system in the form of shoulder posture according to landmarks on one test subject and keypoint extraction on the user interface display in real time and provide information on the angle of asymmetry of the shoulder and hip in the front and rear facing positions. From testing 16 respondents, the shoulder tilt angle is obtained in the range of 7.42-19.84 degrees which will have a TRUE value if the angle is greater than 15 degrees. Information on the angle of more than 15 degrees can be used as a reference for scoliosis symptoms and further diagnosis by medical practitioners and through this detection system it will be easy to get information related to the results of shoulder posture detection accurately and in real time compared to using only a scoliometer.
Factor Analysis on Teaching Quality Management for Art Design Students Using Data Driven Approach Junru, Chen; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.746

Abstract

This study aimed to improve teaching quality management for Art Design students using a data-driven approach through three objectives: (1) synthesizing key factors influencing instructional quality, (2) analyzing those factors using expert consensus, and (3) evaluating student satisfaction after applying the data-driven methodology. The Delphi Method was used to gather insights from 17 education experts, while 30 purposively selected Art Design students participated in satisfaction assessments. Data collection involved questionnaires and interviews, with analysis techniques including mean, standard deviation, Coefficient of Variation (CV), and t-tests. Cronbach’s α was 0.98, indicating high internal reliability. Results showed expert consensus on relevant teaching quality factors (M = 3.92, SD = 0.33, CV = 19.96, p = .002). Key aspects identified included instructional design, digital integration, feedback mechanisms, and curriculum alignment. Post-intervention analysis revealed significant student improvement, with average skill levels increasing from 16.12 (SD = 0.89) to 20.34 (SD = 0.566, p = .002). Student satisfaction reached 78.59%, with a mean of 3.90 (SD = 0.72, CV = 18.78). All statistical terms were properly defined and contextualized. The findings underscore the role of structured data analysis and expert-informed models in enhancing instructional strategies, aligning teaching with professional expectations, and promoting continuous improvement in Art and Design education.
Designing a Data-Driven, Innovative Practical Model for Minority Dance Courses in Higher Education Institutions Zhou, Dan; Sangsawang, Thosporn; Vipahasna, Kitipoom; Prammanee, Noppadol; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.768

Abstract

This study aimed to design and evaluate a data-driven, innovative practical teaching model for minority dance courses in higher education by integrating constructivist learning theory, multicultural education, and experiential learning. The objectives were threefold: (1) to develop a systematic instructional design framework, (2) to measure students' knowledge improvement before and after applying the model, and (3) to assess student satisfaction with the model, particularly regarding cultural identity, learning experience, and engagement. A total of 17 expert instructors from Chinese universities and Kunming University were selected through purposive sampling to contribute to the design process using the Delphi Method. Additionally, 402 first-year dance students participated in evaluating the model’s effectiveness. Quantitative analysis was conducted using means, standard deviations, coefficients of variation, and t-tests. The experts' evaluation of the teaching model yielded a mean of 4.63 (SD = 0.31, CV = 17.84, p = .002), indicating moderate agreement. Student performance significantly improved after intervention, with average skill scores rising from 16.11 (SD = 0.884) to 20.33 (SD = 0.564), p = .002. Student satisfaction reached 78.58% (mean = 3.90, SD = 0.72, CV = 18.78). The hybrid teaching model—blending traditional methods with interactive digital tools and interdisciplinary content (effectively enhanced students' dance proficiency, cultural awareness, and engagement). These findings support the use of blended learning and data-informed instructional strategies to drive innovation and improve outcomes in minority dance education.

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