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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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Price Prediction of Aglaonema Ornamental Plants Using the Long Short-Term Memory (LSTM) Algorithm Sugiarti, Yuni; Suroso, Arif Imam; Hermadi, Irman; Sunarti, Euis; Yamin, Fadhilah Bt Mat
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.640

Abstract

The Aglaonema ornamental plant is a horticultural commodity with high economic value and promising prospects. It is well known for its attractive leaf variations, earning it the nickname "Queen of Leaves." However, unpredictable price fluctuations make investing in Aglaonema speculative and high-risk. This research aims to predict the price of Aglaonema over the next five years using the Long Short-Term Memory (LSTM) algorithm. LSTM is considered superior to other algorithms in handling time series data. The model's performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) on a weekly Aglaonema price dataset covering the period from January 2012 to December 2023. The results demonstrate that the LSTM algorithm can predict Aglaonema prices with high accuracy, as indicated by the following metrics: MSE: 0.005 – Represents the average squared difference between predicted and actual prices. A lower MSE indicates higher model accuracy. RMSE: 0.07-RMSE provides a more interpretable error measurement as it retains the same units as the original data. A low RMSE signifies that the model's predictions closely align with actual values. MAE: 0.04 – Measures the absolute average difference between predicted and actual prices. A lower MAE value reflects a smaller prediction error. Thus, this research makes a significant contribution to the development of a machine learning-based price prediction system for the ornamental plant industry.
Dynamic Replica Management Strategy Based-on Data Accessing Popularity for Load Balancing and Optimizing Network Performance in Cloud Storage Thavamani, S.; Maidin, Siti Sarah; Varun, S. T.
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.674

Abstract

Network performance plays a vital role in organizational efficiency, where large volumes of data, fast transmission, and low latency significantly enhance productivity and reduce downtime. Cloud storage offers a service model that enables remote data management and efficient content distribution. In such systems, data replication is widely used to improve availability, reliability, fault tolerance, and throughput. However, static replication policies often allocate replicas during system initialization, failing to adapt to the dynamic and heterogeneous nature of cloud environments. These environments are susceptible to challenges such as data loss, node failures, and fluctuating demand, which can degrade service quality. To address this, we propose a dynamic replica management strategy that considers data popularity, active peer participation, and peer capacity. Virtual peers are grouped into strong, medium, and weak clusters based on their weight values, which are derived from bandwidth, CPU speed, memory size, and access delay. Content is categorized into Class I, II, and III based on access frequency. Highly popular data (Class I) is replicated in strong clusters, while less frequently accessed data is placed in medium and weak clusters. A hierarchical routing mechanism ensures that queries are directed to the appropriate cluster. The proposed system was implemented and evaluated through simulations. Results show up to 25% improvement in throughput, 20% reduction in packet drops, 97% query efficiency, and decreased bandwidth utilization under high load. By maintaining optimal replica counts without compromising availability, the system supports cloud SLA compliance while minimizing overhead. This solution is aligned with the ninth UN Sustainable Development Goal: Industry, Innovation, and Infrastructure.
Development of a Self-Identity Construction Model for Private Vocational College Students Using Data Science Techniques Chen, Mei; Sangsawang, Thosporn
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.v6i3.731

Abstract

This study aimed to synthesize theories of self-identity learning to develop a self-identity development model for private vocational college students in Yunnan Province, China, identify key influencing factors, and evaluate the model's effectiveness. Using purposive sampling, the study involved 17 experts and 1,004 first-year students. Data were collected through a semi-structured questionnaire via Delphi Technique, supported by consultations via email, WeChat video, and in-person interviews. The model’s validity was assessed based on satisfaction levels from students, teachers, and stakeholders. Statistical analyses included weight calculations, means, standard deviations, coefficients of variation, and path analysis. The results showed strong expert consensus, with an average score of M = 4.5008 and CV = 0.1181, forming a model of 27 first-level and 21 second-level indicators. The "career development expectation evaluation" held the highest weight at 26.86% in the initial assessment, while "dynamic feedback loop development" recorded the highest importance at 0.442 in the practical development phase. Practical testing demonstrated significant effectiveness, with satisfaction means ranging from M = 4.059 to 4.341. Regression analysis confirmed significant mutual influences among the model's five modules. Overall, the model effectively addresses the urgent need for personalized development strategies for private vocational college students in Yunnan Province.
Integrating Convolutional Neural Networks into Mobile Health: A Study on Lung Disease Detection Hasibuan, Muhammad Said; Isnanto, R Rizal; Dewi, Deshinta Arrova; Triloka, Joko; Aziz, RZ Abdul; Kurniawan, Tri Basuki; Maizary, Ary; Wibaselppa, Anggawidia
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.v6i3.660

Abstract

This study presents the development and evaluation of a Convolutional Neural Network (CNN) model for lung disease detection from chest X-ray images, complemented by a mobile application for real-time diagnosis. The CNN model was trained on a diverse dataset comprising images labeled as "NORMAL" and "PNEUMONIA," achieving an overall accuracy of 96%. Compared to traditional machine learning methods such as Support Vector Machine (SVM) and Random Forest, which typically achieve accuracies ranging from 85% to 92%, the proposed CNN model demonstrates superior performance in classifying lung conditions. The model achieved high precision (0.98) and recall (0.96) for pneumonia detection, as well as precision (0.89) and recall (0.95) for normal cases, ensuring both sensitivity and specificity in diagnostic performance. These results indicate that the model minimizes false positives and false negatives, which is crucial for reducing misdiagnoses and improving patient outcomes in clinical settings. To enhance accessibility, an Android-based application was developed, allowing users to upload chest X-ray images and receive instant diagnostic results. The application successfully integrated the trained CNN model, offering a user-friendly interface suitable for healthcare professionals and patients alike. User testing demonstrated reliable performance, facilitating timely and accurate lung disease detection, particularly in areas with limited access to radiologists. These findings highlight the potential of CNNs in medical imaging and the critical role of mobile technology in expanding healthcare accessibility. This innovative approach not only improves diagnostic accuracy but also enables real-time disease detection, ultimately supporting clinical decision-making. Future research will focus on expanding the dataset, incorporating additional lung conditions, and optimizing the model for enhanced robustness in diverse clinical scenarios.
Data Science Approaches to Analyzing Aesthetic Strategies in Contemporary Presidential Campaigns Isnawijaya, Isnawijaya; Lexianingrum, Siti Rahayu Pratami; Taqwa, Dwi Muhammad; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.609

Abstract

In today’s digital political landscape, social media platforms play a critical role in shaping voter engagement, especially among youth. This study investigates how aesthetic political strategies were applied in Prabowo Subianto’s 2024 presidential campaign on TikTok and Instagram. It focuses on decoding voter sentiment, optimizing content delivery, and identifying visual elements that resonate with the public. Using machine learning models tailored to various data types, the research analyses over 50,000 comments and 30 million engagements. A BERT-based sentiment analysis model achieved 88% accuracy, revealing 60% positive, 25% neutral, and 15% negative sentiment, reflecting broad public approval. Meanwhile, a Gradient Boosting engagement prediction model reached 85% accuracy in forecasting post performance based on content format, timing, and hashtag use. Posts with videos and trending hashtags had a 78% chance of high engagement, while static images without hashtags scored only 45%. Evening posts performed best, with a 25% higher likelihood of engagement. The findings highlight the value of AI-driven insights in political communication, emphasizing that emotionally and visually rich content—particularly patriotic and relatable themes—enhances audience connection. This study offers a practical framework for political actors to develop adaptive, data-informed strategies that align with voter preferences in an increasingly fragmented and fast-paced digital media environment.
Evaluating the Effectiveness of Augmented Reality in Mixed Reality Exhibition Spaces: A Multidisciplinary Perspective Widjaja, Yunus; Junaidi, Junaidi; Solihatin, Etin
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.v6i3.690

Abstract

In MR exhibition environments, the integration of AR provides a dynamic approach to increasing visitor engagement, encouraging interactive learning, and enhancing knowledge retention. However, the impact of AR on user experience and educational outcomes in these settings remains underexplored. This study investigates the role of AR in MR exhibitions, aiming to identify both its benefits and challenges. Using a mixed-methods research framework, this study combines qualitative and quantitative approaches to assess AR’s influence on visitor interaction and cognitive processing. Qualitative data is collected through ethnographic observations and structured user interviews, utilizing thematic analysis to identify engagement patterns and usability concerns. Quantitative data is gathered through eye-tracking metrics, user interaction logs, and pre- and post-exhibition knowledge assessments to measure cognitive load, retention rates, and engagement levels. Findings show that AR increases engagement by seamlessly integrating physical and digital elements to create immersive and intuitive experiences. Interactive storytelling and spatial learning mechanisms enhance knowledge retention. However, challenges such as technical limitations, accessibility barriers, and content development complexities restrict widespread adoption. This research contributes to the growing discourse on AR in cultural and educational contexts by offering evidence-based recommendations for optimizing its implementation. By focusing on user-centered design and interdisciplinary collaboration, the study identifies strategies for addressing challenges and maximizing AR’s potential in MR exhibitions. The findings are valuable for curators, designers, and educators seeking to create more effective and engaging exhibition experiences through AR.
Robust Digital Image Watermarking Scheme in the DCT Domain Employing Möbius Transformation Alrammahi, Atheer Hadi; Sajedi, Hedieh; Radif, Mustafa
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.v6i3.705

Abstract

This study introduces a novel digital image watermarking method that integrates Möbius transformations with the Discrete Cosine Transform (DCT) to enhance both resilience and imperceptibility. The primary objective is to address the challenges of watermark embedding in digital images, ensuring robustness against geometric distortions, noise, and compression while maintaining high visual quality. The method employs a genetic algorithm to optimize the Möbius transformation parameters for effective watermark embedding in the DCT domain. Experimental results demonstrate the robustness of the proposed technique, with peak signal-to-noise ratio (PSNR) values consistently above 40 dB, ensuring minimal perceptual distortion. The bit error rate (BER) is significantly lower than that of traditional methods, demonstrating the technique's resilience against a wide range of attacks, including rotation, scaling, Gaussian noise, JPEG compression, and cropping. Compared to existing watermarking schemes, this approach consistently outperforms them in visual quality and resistance to tampering, with the PSNR reaching 60.94 dB for Lena images and achieving an SSIM value close to 1, indicating superior imperceptibility. The novelty of this approach lies in its combination of Möbius transformations with the DCT domain, offering a robust, efficient, and scalable solution for digital rights management and secure media transmission. This technique’s efficiency in terms of computational complexity and potential scalability for broader applications like video and audio watermarking highlights its practical advantages.
Data-Driven Knowledge Management Frameworks for Effective Risk and Crisis Management: A Cross-Industry Approach Qhal, Eissa Mohammed Ali
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.780

Abstract

In today’s interconnected world, organizations across industries face a wide range of risks, from cyber threats to economic crises, which demand agile, data-driven crisis management strategies. Knowledge Management (KM) systems have become essential in managing these challenges by enabling real-time decision-making through data-driven insights. This study examines the role of KM frameworks integrated with advanced data science techniques, such as sentiment analysis and big data analytics, in improving crisis management across various sectors. Additionally, emerging technologies, including Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and cloud computing, have been incorporated into KM frameworks to enhance risk mitigation, communication, and organizational resilience during crises. A cross-industry comparison reveals that while the finance sector has successfully integrated these technologies into its KM systems, other sectors, such as manufacturing, struggle with knowledge retention and data security. The study highlights the value of sentiment analysis in understanding stakeholder perceptions, which refines decision-making in crisis scenarios. The results indicate that KM practices contribute to a 60% reduction in risk, a 65% improvement in crisis resolution speed, and a 62% increase in organizational resilience. Furthermore, the integration of advanced technologies within KM frameworks reduces crisis response times by 82%. Despite these benefits, sectors like healthcare and manufacturing continue to face challenges in knowledge sharing and data security. The study emphasizes the importance of addressing these barriers and incorporating advanced technologies into KM frameworks to optimize crisis management effectiveness. These findings underscore the critical role of KM systems in strengthening organizational resilience, supporting proactive risk management, and enabling quick responses to future crises.
Applied Data Science for Exploring Human Resource Management Affecting the Competitiveness of Commercial Banks in Vietnam Hien, Lam Thanh; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.710

Abstract

Human resource management is crucial in banking operations and is fundamental for maintaining sustainable development and enhancing the competitiveness of commercial banks. The current challenges in human resource management within banking operations provide notable limits that adversely impact competitiveness. This study seeks to identify the elements impacting human resource management in commercial banks in Vietnam and assess their degree of impact. Considering these issues and their influence on human resource management, the authors suggested strategies to enhance human resource management and bolster the competitiveness of banks. This research employs a blend of qualitative and quantitative methodologies. The study included qualitative approaches, utilizing expert techniques and in-depth interviews with 10 bank directors, supplemented by primary data gathered via 350 questionnaires distributed to staff across 10 commercial banks in 10 regions in Vietnam. SPSS 20.0 and Amos software were utilized to measure the extent of influence of the components. The research found six factors: leadership support, training and development of human resources, workplace environment, employee perks and policies, incentive strategies, monitoring, and assessment. The study finds that control and evaluation have the strongest impact on human resource management. Ultimately, the tool assists banks in maintaining adequate human resource management and enhancing competitive positioning. The novelty of this study showed that human resource management affects the competitiveness of commercial banks with a significance of 0.05. Finally, commercial banks should improve digital human resource development software to provide comprehensive management solutions such as AI-driven recruitment platforms, automated performance evaluations, and human resource analytics tools to enhance efficiency, reducing costs and time to perform tasks. Especially helping bank leaders quickly make the right decisions about human resources. Furthermore, human resources management also contributes to the crystallization of corporate cultural values, building and preserving the bank's brand and identity, and is the driving force and goal for the bank's sustainable development.
Understanding Culinary Tourism Preferences: A Study of Local Food Preferences in Indonesia Tunjungsari, Hetty Karunia; Buana, Salsabilla Ayundha Martsha; Hoo, Wong Chee; Wolor, Christian Wiradendi
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.v6i3.664

Abstract

To encourage the contribution of tourism to grow from time to time, it is necessary to have a driving factor for the success of tourism. The government also relies on the number of visits from domestic tourists in increasing tourism power so it is very important for the government to pay more attention to domestic tourists. This study explores the factors influencing tourist satisfaction in local food culinary tourism sector, identifying key drivers and challenging existing assumptions. There were 242 tourists participated freely to fill online questionnaire in the study. The research highlights the importance of Perceived Quality (PQ), Costs and Risks (CR), and Gastronomy Tourist Market Assessment (GTME) as primary determinants of satisfaction. PQ emphasizes the significance of service quality and authenticity, while GTME underscores the role of market positioning in enhancing the tourist experience. Unexpectedly, CR was found to positively influence satisfaction, suggesting that moderate costs and perceived risks may enhance the appeal of culinary tourism. In contrast, Local Food Satisfaction (LFD), Destination Image (DI), Perceived Value (PV), and Tourist Expectations (TE) exhibited statistical significance but had a less pronounced practical impact on overall satisfaction. These findings contribute to the tourism satisfaction theory by emphasizing the need for context-specific models and offering actionable insights for tourism stakeholders. The study suggests that improving service quality, strategic market positioning, and finding a balance between affordability and perceived adventure can enhance the culinary tourism experience. This study introduces a novel application of tourism satisfaction theory by demonstrating the importance of contextualizing satisfaction models specifically for culinary tourism—an area often overlooked in broader tourism satisfaction research.

Page 38 of 52 | Total Record : 518

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