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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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Traditional-Enhance-Mobile-Ubiquitous-Smart: Model Innovation in Higher Education Learning Style Classification Using Multidimensional and Machine Learning Methods Santiko, Irfan; Soeprobowati, Tri Retnaningsih; Surarso, Bayu; Tahyudin, Imam; Hasibuan, Zainal Arifin; Che Pee, Ahmad Naim
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.598

Abstract

Learning achievement is undoubtedly impacted by each person's unique learning style. The assessment pattern is less focused due to the intricacy of the current components. In fact, general elements like VARK are thought to create complexity that can impair focus when combined with elements like environmental conditions, teacher effectiveness, and stakeholder policies. Although it is only ideal in specific areas, the application of supported information technology has so far yielded positive results. This essay attempts to be creative in evaluating how well students learn in higher education settings. An assessment framework that uses multidimensionality and simplifies features is the innovation that is being offered. Method, Material, and Media (3M) are the three categories into which simplification of aspects is separated. However, the Dimensions are categorized into five groups: Traditional, Enhance, Mobile, Ubiquitous, and Smart (TEMUS). Approximately 1200 respondents consisting of students and lecturers formed into a dataset in 2 types of data, namely test data and training data. The trial was conducted using 4 models, namely Random Forest, SVM, Decision Tree, and K-Nearest. The test results were interpreted in MSE, R-Square, Accuracy, Recall, Precision, and F1-Score. Based on the comparison of test results, it states that Random Forest has the most optimal results with MSE values of 0.46, R Square 0.99, Accuracy 0.86, Recall 0.86, Precision 0.87, F1 Score 0.84. Based on the results obtained, it proves that in addition to being able to carry out the classification process, the TEMUS Dimensional Framework can form a pattern of compatibility with each other, between the learning styles of Lecturers and Students. According to this TEMUS framework, teacher and student performance will be deemed suitable and effective when the 3M components are assessed from both perspectives in the same way. If not, a review will be conducted.
Utilizing Systematic Digital Platforms and Instructional Design in Health Communication: A Data-Driven Approach in China's Curriculum Fu, Ying; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.651

Abstract

This study explores the integration of systematic instructional design and digital platforms in health communication courses in China, with a focus on evaluating the effectiveness of these approaches in enhancing medical interns' knowledge and satisfaction. The research involved 17 experienced physicians and 30 medical interns, utilizing the Delphi Method for expert input and various data collection methods, including in-person surveys, telephone interviews, and email-based questionnaires. The study aimed to assess the impact of digital platforms and instructional design on knowledge acquisition and overall satisfaction. The findings suggest that the integration of systematic instructional design with digital platforms significantly improved medical interns' knowledge and engagement with the health communication curriculum. Additionally, expert consensus supported the effectiveness of this approach in addressing critical gaps in digital literacy and practical health communication skills. The study introduces the Chinese IDSDPS Health Communication Model, a dynamic, culturally relevant framework designed to bridge gaps in digital literacy, communication tactics, data analysis, and interdisciplinary learning. By incorporating locally relevant health content and ensuring alignment with China's public health needs, the model presents a scalable approach to improving health communication education. This research emphasizes the transformative potential of combining instructional design and digital technologies to enhance educational outcomes in health communication, offering valuable insights for addressing broader public health challenges both in China and globally.
Data-Driven Development of an Elderly Training Package Using the GCC Model Cheng, Fan; Sangsawang, Thosporn; Pigultong, Metee; Watkraw, Wasan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.662

Abstract

This study aimed to design and assess the effectiveness of an elderly training package for first-year students at Yibin University, China, based on the GCC Model for geriatric rehabilitation. The goal was to integrate theoretical knowledge with practical skills in geriatric care, using data-driven approaches to evaluate its impact on student learning outcomes. A purposive sample of 17 experts and 30 first-year students enrolled in geriatric rehabilitation courses participated in the study. Data were collected through a combination of in-person surveys, telephone interviews, and email interviews using the Delphi Method. The training package focused on critical aspects of geriatric care, including aging-related health issues, physical rehabilitation, psychological support, and social integration. Additionally, it incorporated technology, practical simulations, case studies, and feedback mechanisms to enhance healthcare professionals’ skills. Data analysis demonstrated a significant improvement in students' knowledge and practical abilities post-intervention, with moderate satisfaction expressed by both experts and students regarding the effectiveness of the package. The study underscores the importance of blending theoretical learning with hands-on experience, utilizing data-driven evaluation methods to assess the impact on educational outcomes. These findings provide valuable insights for the development of effective geriatric care training models that combine data science and educational practices to optimize learning in healthcare education.
Development of Gamification-Based Learning Management System (LMS) with Agile Approach and Personalization of FSLSM Learning Style to Improve Learning Effectiveness Saputra, Jeffri Prayitno Bangkit; Prabowo, Harjanto; Gaol, Ford Lumban; Hertono, Gatot Fatwanto
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.486

Abstract

This research focuses on designing a Learning Management System (LMS) that incorporates gamification elements while addressing student learning styles based on the Felder-Silverman Learning Style Model (FSLSM). Using Agile methodology in the development process, the LMS is designed to deliver a more personalized learning experience, with features tailored to students' unique learning style preferences. The research process began with a comprehensive user needs analysis, followed by iterative design and development in accordance with Agile principles. System evaluation involved user feedback and performance analysis, revealing that the developed LMS increased student engagement by 25% and improved learning motivation by 30% compared to the previous system. Furthermore, 88% of users reported a positive experience with the personalized features, and the system achieved an overall satisfaction score of 85% in usability testing. These results demonstrate that the LMS effectively enhances student motivation and engagement in the learning process while providing a more individualized learning experience. This research contributes to the advancement of adaptive and responsive learning systems that better meet the diverse needs of students.
Using Evolutionary Optimization Techniques to Improve the Efficiency of Transportation Scheduling Shambour, Mohd Khaled Yousef
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.v6i2.581

Abstract

This study addresses the challenge of enhancing transportation efficiency during large-scale events, with a particular focus on the Hajj pilgrimage. Every year, more than two million pilgrims visit Makkah in Saudi Arabia to perform their Hajj rituals. The Haj ritual requires transporting vast numbers of pilgrims within a limited time, compounded by diverse transportation preferences that make timely, optimal scheduling complex. To tackle this, the study employs three optimization algorithms -Harmony Search (HS), Differential Evolution (DE), and Black Widow Optimization (BWO) - to optimize transportation schedules based on individual preferences. A comprehensive mathematical model was developed for this purpose, incorporating both hard and soft constraints that reflect the scheduling requirements and preferences of pilgrims. Experimental results show that the DE algorithm consistently outperforms HS and BWO, achieving the highest mean scores in 100% of scenarios with a population size of 100, 66.7% of scenarios with a population size of 20, and 16.7% of scenarios with a population size of 5. In contrast, BWO struggles to adapt to varying parameter settings, producing consistently lower-quality solutions. DE, in particular, performs exceptionally well with lower crossover probabilities, demonstrating its ability to balance exploration and exploitation effectively. On the other hand, HS yields better results when higher exploration probabilities are used, highlighting its strength in broader search space exploration. In contrast, the performance of BWO remains largely unaffected by variations in exploration and exploitation parameters, leading to consistently inferior solutions. These findings underscore the importance of dynamic parameter tuning for large-scale optimization tasks, suggesting that such approaches are promising for addressing complex scheduling challenges in major events like Hajj.
Decision Support Model for Determining Fuel in Boiler Machines Widyanto, Jeremia; Utama, Ditdit Nugeraha
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.v6i2.520

Abstract

This investigation seeks to formulate a Decision Support Model (DSM) aimed at identifying the most suitable fuel for boiler systems utilized in industrial contexts, encompassing three distinct fuel categories: natural gas, industrial diesel oil, and coal. The assessment is predicated on four fundamental criteria: cost, calorific value, safety, and emissions. Employing a synergistic methodology that combines Analytic Hierarchy Process (AHP) and Fuzzy Logic, AHP allocates weights to each criterion (cost: 0.503, calorific value: 0.273, safety: 0.145, emissions: 0.079). The Fuzzy Logic approach is utilized to effectively address uncertainty and process subjective assessments. The findings indicate that cost constitutes the paramount determinant, exhibiting the highest weight, succeeded by calorific value, safety, and emissions. In accordance with these weighted criteria, the fuels are ordered as follows: coal (0.794), natural gas (0.653), and industrial diesel oil (0.456). These results underscore that cost remains the predominant factor in fuel selection for industrial boilers, whilst safety and environmental ramifications concurrently exert significant influence. The originality of this inquiry is manifested in its implementation of an all-encompassing DSM for fuel selection, marking a pioneering effort within this domain, which integrates both AHP and Fuzzy Logic to furnish a versatile and resilient decision-making framework. The implications of this research are substantial, as it offers a transparent and systematic approach for fuel selection in industrial environments, providing valuable insights into the optimization of energy resources while taking into account economic, environmental, and safety considerations. Subsequent investigations could further examine the incorporation of renewable energy sources and the ramifications of advancing environmental policies on fuel selection.
The CNN Model with YOLO Architecture for Ultrasonography Images in Early Breast Cancer Detection Ayuningtyas, Astika; Wintolo, Hero; Sumari, Arwin Datumaya Wahyudi; Setyaningsih, Emy; Pujiastuti, Asih; Honggowibowo, Anton Setiawan; Nuryatno, Edi Triono; Kusumaningrum, Anggraini
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.v6i2.587

Abstract

The rapid development of object detection technology has opened new opportunities in the healthcare sector, especially in early cancer detection. This paper presents a deep learning-based breast cancer detection system using ultrasound images. The primary goal of this study is to create a model that can effectively differentiate between malignant and benign breast tumors, assisting in early diagnosis. The proposed system employs the Convolutional Neural Network (CNN) algorithm with You Only Look Once version 5 (YOLOv5) architecture, which is renowned for its high speed and accuracy in object detection tasks. A dataset comprising 10,954 ultrasound images was used to train the model, with 70% allocated for training, 20% for validation, and 10% for testing. The study reveals that the model achieves a high accuracy rate of 92.8% for malignant tumor detection and 99.1% for benign tumors, with precision rates of 99.6% for malignant tumors and 97.5% for benign tumors. These results demonstrate the feasibility of the proposed model as a reliable tool for early breast cancer detection. The findings highlight the potential of deep learning in medical image processing, suggesting that this technology could be further developed into an accessible, efficient early detection system for breast cancer in clinical settings. Future research could explore the integration of additional imaging modalities and the application of this model in real-world healthcare environments
Nature-based Hyperparameter Tuning of a Multilayer Perceptron Algorithm in Task Classification: A Case Study on Fear of Failure in Entrepreneurship Saputri, Theresia Ratih Dewi; Kurniawan, Edwin; Lestari, Caecilia Citra; Antonio, Tony
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.v6i2.539

Abstract

Entrepreneurship plays a key role in generating economic growth, encouraging innovation, and creating job opportunities. Understanding which demographic, psychological, and socio-economic factors contribute to fear of failure in entrepreneurship is essential to developing proper standards in entrepreneurship education and policy. However, it remains challenging to accurately classify these factors, especially when balancing model performance with model complexity in a multilayer perceptron algorithm. An effective model requires the correct parameter setting via a hyperparameter tuning process. Adjusting each hyperparameter by hand requires significant effort and knowledge, as there are frequently multiple combinations to consider. Furthermore, manual tuning is prone to human error and may overlook optimal configurations, resulting in inferior model performance and prediction accuracy. This study evaluates nature-inspired optimization techniques, including particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). Several parameters are tuned in the present multilayer perceptron model, including the number of hidden layers and the number of nodes in each hidden layer, learning rate, and activation functions. The used dataset which consists of 39 features from 333 samples captured individual fears, loss score, and computational efficiency as the required amount of time for finding the best parameter combination. Model accuracy performance scores are 45.16%, 53.76%, and 58.61% for GA, PSO, and GWO, respectively. Meanwhile their execution time are 10 minutes, 27 minutes, and 23 minutes, for GA, PSO, and GWO, respectively. Experiment results further reveal that each optimization algorithm has distinct advantages: GA excels at speedy convergence, PSO provides a robust exploration of hyperparameter space, and GWO offers remarkable adaptability to complicated parameter interdependencies. This study provides empirical evidence for the efficacy of nature-inspired hyperparameter modification in improving multilayer perceptron performance for fear of failure categorization tasks.
The Model of Carbon Price Risk Prediction in European Markets Using Long Short-Term Memory- Geometric Brownian Motion Pradana, Yan Aditya; Mukhlash, Imam; Irawan, Mohammad Isa; Putri, Endah Rokhmati Merdika; Iqbal, Mohammad
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.v6i2.536

Abstract

Accurate carbon market price prediction is one of the fundamentals in assessing the risks associated with carbon trading. Related studies on carbon price prediction were mainly focused on two major approaches: mathematical and/or machine learning models. Geometric Brownian Motion (GBM) is one of the mathematical models that can represent carbon price movements but requires modifying the sample size and the number of parameters for compiling the simulation numerically. Moreover, two critical parameters: (μ) mu and (σ) sigma need to be estimated to simulate the carbon price movements. In this study, the parameters μ and σ estimation are based on the average return value and standard deviation. However, if the carbon price movement is very volatile, we need to recognize its trend and characteristics by estimating the parameters precisely until there is no significant change (or stable) patterns. That is very expensive and may be intractable on high-dimensional data with less precise prediction. Therefore, we propose a hybrid model for carbon price prediction based on GBM with the parameter estimation using the Long Short-Term Memory (LSTM) model. The LSTM model was chosen because it has high accuracy in parameter estimation without losing the characteristics of the GBM stochastic model. Furthermore, Value at Risk (VaR) is utilized to measure the risk of carbon price volatility predictions. The simulation results showed the proposed model has higher prediction accuracy with a not-too-significant time difference, and the model is proven reliable in measuring future risks.
An Artificial Ant-Based Approach Using Polynomial Algorithms to Tackle the Text Aspect of Clustering Web Pages Moufok, Souad; Belkadi, Khaled; Lebbah, Fatima Zohra
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.v6i2.546

Abstract

Nowadays, the web clustering problem represents a scalable research area, which is based on deep study and efficient analysis of the user's browsing behavior. Managing huge amounts of unstructured data that are given through web pages is described as a hard and primary task. In this article, we analyze clusters by grouping users based on the similarity of the web pages they have visited. Our work focuses on cleaning, analyzing, and clustering web data to facilitate users’ access to relevant content. Thus, we propose a novel algorithm, called WCLARTANT, to cluster WEB pages, which consists of finding groups of sessions according to the corresponding Web access patterns. We propose a new approach based on the ANTTREE algorithm, inspired from the self-assembling behavior observed in real ants and the binary search tree concept. The combination that we present in our approach is applied for the first time in web usage mining clustering. More precisely, different topologies are built in terms of different similarity measures, such as SBS, Euclidean, Jaccard and Cosine. Afterward, the clusters are extracted from the binary tree, which is built by the prefix depth algorithm. In other words, the proposed algorithms in this manuscript provide the corresponding binary tree to the sessions' matrix, where each node models a WEB session and each branch represents a cluster. In addition, we use the Silhouette index to evaluate and to analyze the clustering performance of WCLARTANT relative to the DBScan algorithm. WClArtAnt combined with the similarity measure SBS provides the best results compared to DBScan. The performance of our algorithm varies between 0.62 and 0.39, which are considered good. The considered log files are coming from NASA and contain all HTTP requests for a month period, from 1st July, 1995, to 31st July, 1995, for a total of 65,194 entries.

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