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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 55 Documents
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Search results for , issue "Vol 6, No 3: September 2025" : 55 Documents clear
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.
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.
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.
Exploring Machine Learning to Support Software Managers' Insights on Software Developer Productivity Suwarno, Suwarno; Christian, Yefta; Yoputra, Keaton; Estrada, Yuki
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.v6i3.661

Abstract

Software developer productivity is a complex issue with no single, universally accepted definition or measurement. Emerging technologies like machine learning offer a promising opportunity for more accurate productivity measurement. Semi-structured interviews were conducted to gain qualitative insights into software managers’ perception of developer productivity to identify issues and inform the development of applied machine learning solutions. It was discovered that digital distractions significantly hinder developer productivity and conventional methods to monitor developer activity were often inefficient. Therefore, machine learning models were developed to monitor developer activity by classifying screenshots captured during activity, along with the URL and text content scraped from accessed URLs. Train and test data were obtained from a cooperating software house, supplemented with online sources. For screenshot classification, transfer learning using EfficientNetV2B0 outperformed InceptionV3, Resnet50V2, and VGG16, reaching 99.6% accuracy. This was achieved without fine-tuning, which resulted in the fastest training and lowest resource consumption. For content classification, SVC hyperparameter-tuned using grid search outperformed six other classifiers, reaching 88.5% accuracy. The design concept for a web application that utilizes the developed models to help managers measure developer productivity was well-received by the managers interviewed.
Modeling Female Contraceptive Recommendation Using Hybrid Analytical Hierarchy Process and Profile Matching Zaidiah, Ati; Astriratma, Ria; Isnainiyah, Ika Nurlaili
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.v6i1.613

Abstract

Multi-criteria decision-making (MCDM) methodologies have been extensively employed across various domains within healthcare. They can be utilized for disease prediagnosis, aiding clinical decision-making (e.g., surgery), conducting health technology assessments, as well as establishing healthcare priorities. This research presents the outcomes of a hybrid MCDM strategy, integrating the AHP and Profile Matching to facilitate clinical recommendations related to female contraception. Many cases in Indonesia show acceptors' inappropriateness in using available contraceptives, causing side effects resulting in negative effects. The challenges in the Keluarga Berencana or Planned Parenthood program in Indonesia are increasing, based on the decline in the number of new acceptors and the high unmet needs for contraception. Failure to meet the need for contraception has the potential to increase birth rates and maternal mortality rates, which requires serious attention and the development of appropriate strategies. Based on the problem, this study aims to create a decision support model in selecting suitable contraceptives for acceptors. The criteria used in this study consisted of age, medical history, weight (BMI), breastfeeding or not, history of childbirth, period of use, and income. The seven criteria are implemented in AHP with a consistency test result value of 2.2%. Based on the target value of contraceptives obtained from the results of Profile Matching, compatibility was determined with a sample of three acceptor profiles. The results that have been achieved indicate a sample recommendation model for acceptors of IUD-type contraception that can assist midwives or medical personnel in providing recommendations for selecting appropriate contraceptive methods. Future studies can integrate the results of recommendations for health service providers (e.g., hospitals, Public Health Center or Puskesmas) in procuring contraceptives.
Analyzing the Impact of Social Media Advertising on Consumer Interactions, Trust, and Purchase Intentions in the Cosmetics and Personal Care Sector Yee, Loo Shu; Hoo, Wong Chee; Wolor, Christian Wiradendi; Bakar, Syarifah Mastura Syed Abu; Nurkhin, Ahmad
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.v6i3.669

Abstract

This study analyses the influence of social media advertisements on consumer purchasing intentions within Malaysia's cosmetics and personal care sector, highlighting the significance of interactivity, informativeness, and trust. Considering the growing prevalence of social media in marketing, comprehending how these elements affect consumer choices is crucial for enhancing digital advertising strategies.  This research utilises a quantitative approach to evaluate consumer behaviour. Data were gathered from 384 Malaysian consumers aged 18 and older who actively interact with cosmetic and personal care brands on social media using online survey. The research employed descriptive analysis, reliability and validity assessments, and hypothesis testing to investigate the relationships among principal variables.  Research demonstrates that informativeness and trust in social media advertisements substantially increase purchase intentions, with trust acting as a vital mediator. Although interactivity enhances trust, it does not directly affect purchase intention, indicating that its influence may be contingent upon contextual variables. These findings underscore the imperative for brands to deliver transparent, reliable, and informative content to enhance consumer trust and stimulate purchasing behaviour. The study's originality is rooted in its examination of the Malaysian cosmetics and personal care market, providing practical insights for marketers to enhance social media advertising strategies. The research underscores trust as a pivotal factor, offering essential insights for brands aiming to improve their digital engagement. This study enhances the existing literature on digital marketing in emerging markets, providing practical insights for businesses seeking to improve their social media efficacy in Malaysia.
Intelligent Solar Panel Monitoring Using Machine Learning and Cloud-Based Predictive Analytics Dhilipan, J.; Vijayalakshmi, N.; Shanmugam, D.B.; Maidin, Siti Sarah; Shing, Wong Ling
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.v6i3.720

Abstract

The increasing global energy demand necessitates reliable and sustainable solutions, with solar photovoltaic (PV) technology emerging as a key carbon-neutral option. However, optimizing solar energy systems requires advanced monitoring and predictive analytics to enhance efficiency and ensure long-term performance. This study introduces an Internet of Things (IoT)-based solar energy monitoring system, integrating machine learning algorithms and cloud computing to enhance real-time performance assessment. The proposed system employs K-Means Clustering for condition classification, Support Vector Machine (SVM) for fault detection, Long Short-Term Memory (LSTM) for energy forecasting, Prophet for time-series predictions, and Isolation Forest for anomaly detection. The system was validated using a 125-watt photovoltaic module, monitoring temperature, solar radiation, voltage, and current. A Wi-Fi-enabled microcontroller collects data, which is processed through a cloud-based platform and visualized via the Blynk application. Experimental results demonstrate 94.2% energy prediction accuracy using LSTM, 89.7% fault classification accuracy with SVM, and 88.5% anomaly detection accuracy with Isolation Forest, confirming high reliability. The system's wireless tracking mechanism minimizes resource consumption, ensuring scalability and adaptability for commercial and industrial applications. The integration of IoT, machine learning, and cloud analytics provides a cost-effective and scalable approach for solar PV optimization. Future enhancements include deep learning models and reinforcement learning algorithms to improve energy forecasting, fault detection, and adaptive optimization, ensuring greater efficiency, resilience, and sustainability in solar energy management.
Assimilate Grid Search and ANOVA Algorithms into KNN to Enhance Network Intrusion Detection Systems Alsharaiah, Mohammad A.; Almaiah, Mohammed Amin; Shehab, Rami; Alkhdour, Tayseer; AlAli, Rommel; Alsmadi, Fares
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.v6i3.604

Abstract

The recent progress of operational network intrusion detection systems (NIDS) has become increasingly essential. Herein, a fruitful attempt to introduce an innovative NIDS methodology that integrates the grid search optimization algorithm and ANOVA techniques with the K nearest neighbor (KNN) algorithm to analyze both spatial and temporal characteristics of data for network traffic. We employ the UNSW-NB15 benchmark dataset, which presents various patterns and a notable imbalance between the training and testing data, with 257674 samples. Therefore, the Synthetic Minority Oversampling Technique has been used since this method is effective in handling imbalanced datasets. Further, to handle the overfitting issue the K folds cross-validation method has been applied. The feature sets within the dataset are meticulously selected using ANOVA mechanisms. Subsequently, the KNN classifier is fine-tuned through hyperparameter tuning using the grid search algorithm. This tuning process includes adjusting the number of K neighbors and evaluating various distance metrics such as 'euclidean', 'manhattan', and 'minkowski'. Herein, all attack types in the dataset were labeled as either 1 for abnormal instances or 0 for normal instances. Our model excels in binary classification by harnessing the strengths of these integrated techniques. By conducting extensive experiments and benchmarking against cutting-edge machine learning and deep learning models, the effectiveness and advantages of our proposed approach are thoroughly demonstrated. Achieving an impressive performance of 99.1%. Also, several performance metrics have been applied to assess the proposed model's efficiency.
Applying Structural Equation Modelling to Examine the Impact of Environmental Management Accounting on Financial Nguyen, Nga Thi Hang
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.v6i3.728

Abstract

Environmental management accounting has garnered significant attention from various stakeholders. Enterprises that effectively implement environmental management accounting not only contribute to sustainable development but also enhance organizational performance. This study aims to examine the relationship between environmental management accounting, green innovation, and the financial performance of small and medium-sized enterprises in the context of a developing country like Vietnam. A quantitative research approach was employed to analyze data collected through a structured survey. The dataset comprises responses from 151 small and medium-sized enterprises, with financial managers and management accountants serving as key informants. Data analysis was conducted using the Smart Partial Least Squares software. The findings reveal that environmental management accounting has a direct positive impact on financial performance and an indirect impact through the mediation of green process innovation. While green product innovation exerts a direct impact on financial performance, environmental management accounting appears to have no significant influence on green product innovation. Consequently, green product innovation does not function as a mediating variable in the relationship between environmental management accounting and financial performance. The results underscore the greater significance of green process innovation over green product innovation in driving improvements in the financial performance of small and medium-sized enterprises. These results significantly contribute to the relatively unexplored theoretical relationship between environmental management accounting, green innovation, and the financial performance of small and medium-sized enterprises. Furthermore, the study provides a practical foundation for managers to boost their organizations’ financial performance by practicing environmental accounting and integrating green innovation into business operations.

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