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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 53 Documents
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Search results for , issue "Vol 5, No 4: DECEMBER 2024" : 53 Documents clear
AI Prediction Model to Investigate the GovTech Maturity Index (GTMI) Indicators for Assessing Governments’ Readiness for Digital Transformation AlMurtadha, Yahya
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.373

Abstract

Digital transformation helps governments improve their efforts to provide services to beneficiaries around the clock. However, governments must consider the potential disadvantages of unplanned digital transformation such as lack of attention to cybersecurity standards, which could put citizens' data at risk, or resistance to change and adoption of new technologies by government employees. The goal is for governments to take a comprehensive, well-planned approach to digital transformation that addresses people, processes and technology. Hence, governments should utilize digital maturity models to assess their current state and develop a plan for successful digital transformation. Governments especially are seeking for a smart transition to a mature digital transformed state. Therefore, this study proposes using the digital transformation maturity index as a systematic framework for governments to assess their digital transformation and plan a successful digital transformation. This study suggests using AI prediction algorithms to chart a path for a mature digital transformation.  Hence, this study builds a model that predicts government maturity level to one of four maturity classes (A, B, C, and D) using several AI prediction techniques on the World Bank GovTech dataset, which contains 48 important indicators used to measure the GovTech maturity index.  The results show that decision tree algorithm outperforms other approaches in terms of prediction accuracy. Government’s experts may thus utilize decision trees to determine the digital transformation maturity index success route starting at the root and working their way up to the leaf.  The results also highlight the need for a government to examine three essential indicators for a successful digital transformation with higher maturity class: universally accessible citizen-centric public services, a national strategy to connect all departments under one goal, and transparency. The study concludes that governments should embrace holistic and well-planned digital transformation while considering factors such as cultural and behavioral changes, future disruptions and emerging technologies.
Novel Predictive Framework for Student Learning Styles Based on Felder-Silverman and Machine Learning Model Maulana Baihaqi, Wiga; Eko Saputro, Rujianto; Setyo Utomo, Fandy; Sarmini, Sarmini
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.408

Abstract

This study analyzes data from the Open University Learning Analytics Dataset to evaluate how students' interactions with Virtual Learning Environment (VLE) materials influence their final outcomes. This research aims to formulate and build a novel predictive framework based on the Felder-Silverman and Machine Learning Model for student learning styles. Based on these objectives, this research provides novelty and contributions since it enhances student data analysis, uses a learning model using Felder-Silverman Learning Style Model (FSLSM) to give a more comprehensive understanding of students' learning styles, and improves prediction accuracy by introducing Artificial Neural Network (ANN) and feature selection using Random Forest. The data used includes 3 main files: vle.csv, which contains information about the materials and activities in the VLE; studentVle.csv, which records students' interactions with the materials; and studentInfo.csv, which provides demographic information of students and their final outcomes. The analysis process involved data merging and processing, including handling of missing values, data type conversion, as well as mapping activity types to learning style features based on the FSLSM. We use the Random Forest feature selection method, as well as data imbalance handling techniques such as oversampling, to improve model performance. The applied classification models include Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and ANN. The analysis results showed that after tuning, the Random Forest model achieved 97% accuracy, while SVM achieved 97% accuracy as well, with better performance than previous studies. This research highlights the importance of comprehensive data integration and appropriate processing techniques in improving the accuracy of student learning style prediction. Based on the increase in accuracy results, it can be beneficial for more effective personalized learning and improve our understanding of students' learning style preferences. The research advances knowledge and provides practical applications for educators to tailor their teaching strategies.
Quantitative Evaluation of Watercolor Brush Performance: A Comparative Study of User Satisfaction and Task Efficiency using 24 Innovative Brush Designs Chantanasut, Suraphan
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.462

Abstract

This study investigates the performance, user satisfaction, and durability of innovative watercolor brushes compared to traditional brushes, with a focus on quantifiable improvements. The innovative brushes, designed in collaboration with professional watercolorists, feature both roundhandled and flat-handled versions aimed at enhancing painting comfort, precision, and control. The researcher created an innovative watercolor brush with a total of 24 types, divided into 12 round-handled brushes and 12 flat-handled brushes. A sample of 24 artists, including both professionals and amateurs, completed three distinct painting tasks—still-life, large-area washes, and detailed line work. Quantitative data on task completion times, paint usage, and durability were collected, alongside user satisfaction ratings for comfort, ease of use, and stroke control. Statistical analysis revealed that the innovative brushes significantly outperformed traditional brushes across all metrics. On average, the innovative brushes reduced task completion times by 13-15%, with a mean of 13.88 minutes compared to 15.89 minutes for traditional brushes on the still-life task. Paint usage was also lower, with innovative brushes using approximately 2.44 grams on average for the still-life task, compared to 2.97 grams for traditional brushes, reflecting a 17.8% improvement in paint efficiency. User satisfaction ratings were consistently higher for the innovative brushes, scoring an average of 4.5 out of 5 for comfort, ease of use, and stroke control, in contrast to 3.5 for traditional brushes. Durability assessments further showed that innovative brushes maintained an average bristle condition rating of 4.6 versus 3.5 for traditional brushes after extended use, confirming superior longevity. These results highlight the impact of ergonomic handle design and advanced synthetic materials on brush performance. Recommendations for future brush designs include further refinement of handle shapes and enhanced bristle technologies to support the technical and artistic needs of watercolorists. While limitations such as the subjective nature of user ratings and sample size should be noted, this study lays the groundwork for continued research on performance metrics for art tools across various creative disciplines.
Performance Comparison of Whale and Harris Hawks Optimizers with Network Intrusion Prevention Systems Abualhaj, Mosleh M.; Al-Khatib, Sumaya N; Alsharaiah, Mohammad A; Hiari, Mohammad O
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.323

Abstract

Digital technology has permeated every aspect of our daily lives. Processing and evaluating information are highly demanding in all fields, including cybersecurity. Cybersecurity engineers widely use the Network Intrusion Prevention System (NIPS) to safeguard against cyberattacks. To avoid cyberattacks, the NIPS must deal with a large amount of data, which degrades its performance. This paper uses the whale optimization algorithm (WOA) and the Harris Hawks optimization method (HHO) to diminish the large amount of data that the NIPS needs to deal with. Subsequently, the Gradient Boosting Machine (GBM) is employed to determine the accuracy achieved when employing WOA and HHO. The GBM classifier is widely regarded as a sophisticated and straightforward classifier in data mining. Regardless of the premise of feature independence, it outperforms all other classification algorithms by delivering excellent performance. When using GBM, the findings indicate that the accuracy achieved with HHO is 89.81%, but the accuracy attained with WOA is 94.3%.
Empirical Study of the Correlation between Social Media Content and Health Issues among College Students Using Machine Learning Hemalatha, M.; Maidin, Siti Sarah; Sun, Jing
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.365

Abstract

This study analyzes the effect of social media content on college student addiction using data science techniques. It aims to examine the correlation between different types of social media content and addictive behavior in college students. The research methodology used is non-probability sampling with a sample size of 587 college students in Tamil Nadu, India. The study uses statistical tools such as correlation analysis, regression analysis, one-way ANOVA, and Friedman ranking test to analyze the data collected. The findings suggest that the factors influencing social media addiction are positively correlated with the health issues faced by college students. The study indicates that demographic variables such as age, gender, year in college, and place of living may play a role in shaping an individual's perception of social media addiction. The results of the study can inform the development of interventions and prevention strategies to reduce social media addiction among college students. The study recommends a multi-pronged approach to address the root causes of addiction and provide students with the tools and resources they need to manage their social media use and promote their physical and mental health.
Osteoporosis Detection Using a Combination of Recursive Feature Elimination and Naive Bayes Classifier with Rule-Based Chatbot Testing Sela, Enny Itje; Rianto, Rianto; Anggara, Afwan; Utami, Wahyu Sri
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.409

Abstract

Osteoporosis is a condition characterized by reduced bone mass and density, increasing the risk of fractures. Early detection relies on patient awareness and proactive health management. Despite advances in technology, patient independence and awareness remain critical for early diagnosis. A rule-based chatbot tool can assist by helping patients screen their bone health. The chatbot provides automated recommendations, offering an alternative to traditional hospital visits. This study presents a rule-based chatbot designed to detect osteoporosis, using Recursive Feature Elimination (RFE) combined with the Naïve Bayes Classifier (NBC). Machine learning is integrated to enhance the chatbot's ability to identify early signs of osteoporosis. The model’s performance is compared to other feature selection methods, such as Principal Component Analysis (PCA), and machine learning algorithms like Deep Learning, Support Vector Machine (SVM), and Logistic Regression. The dataset used includes public data sets for training and validation, as well as data from the Yogyakarta Health Office for predictions. Research phases include normalization, data encoding, feature selection, training, validation, and prediction. The chatbot implements text preprocessing techniques, such as tokenization, stop word removal, and feature extraction, alongside normalization and encoding of numeric data. The prediction stage determines if the patient has a positive or negative osteoporosis status. Validation results show the RFE-NBC model is particularly effective for osteoporosis detection, offering a balanced performance in identifying both positive and negative cases. Additionally, this model served as the foundation for creating a rule-based chatbot designed to detect osteoporosis. Based on the set of testing metrics using chatbot, the model demonstrates strong overall performance, with a good balance between identifying positive and negative instances.
Developing a Parallel Network Slack-Based Measure Model in the Occurrence of Hybrid Integer-Valued Data and Uncontrollable Factors Dzulkarnain, Syarifah Nurfuaduz Zakiah Habib; Nawawi, Mohd Kamal Mohd; Kashim, Rosmaini
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.407

Abstract

This study develops an alternative approach to the parallel network Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model, offering a more accurate and informative assessment of performance within a network system. Traditional DEA models solely focus on the input utilization and the outputs produced when assessing efficiency, disregarding the operation of internal processes within a network system. In addition, these approaches do not assess the concurrent requirement of hybrid integer-valued data and uncontrollable factors on efficiency measures. To address these gaps, we propose a novel approach to parallel network SBM DEA model that integrates hybrid integer-valued data with uncontrollable factors, aiming for a more precise evaluation. Both requirements were initially integrating into the existing method. Subsequently, the optimal solution for the proposed method was achieved by converting its fractional form into a linear one. Therefore, the measures of the proposed approach can now deal directly with controllable hybrid integer- valued input and output slacks. We applied this model to a dataset of 26 faculties in a Malaysian public university, followed by a comparative analysis with existing models. Empirical findings indicate that four (4) faculties are found to be overall effective, as all of their internal processes are effective, while the other faculties are ineffective since not all of their internal processes are effective. The results from our model enable decision-makers to identify ineffectiveness within network processes, thereby facilitating targeted improvements in system performance. By concentrating on the appropriate processes, management can enhance their overall effectiveness and internal effectiveness.
Applied Density-Based Clustering Techniques for Classifying High-Risk Customers: A Case Study of Commercial Banks in Vietnam Nhat, Nguyen Minh
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.344

Abstract

Understanding and effectively engaging with customers is paramount in today's rapidly evolving business landscape. With rapid technological advances, banks have unprecedented opportunities to improve their approach to customer segmentation. This change is driven by integrating resource planning systems and digital tools, enabling a more comprehensive and data-driven understanding of customer behavior. Therefore, the study aims to evaluate the performance of various density-based clustering algorithms in classifying customers at risk of default. The algorithms analyzed include K-Means, DBSCAN, HDBSCAN, and Birch, each offering unique strengths in handling diverse data structures. Using a dataset of 77,272 customers from Vietnamese commercial banks spanning 2010 to 2022, the study rigorously assesses these models based on seven critical metrics: Davies-Bouldin Index, Silhouette Score, Adjusted Rand Index, Homogeneity, Completeness, V-Measure, and Accuracy. The results indicate that density-based methods, particularly DBSCAN and HDBSCAN, excel in identifying high-risk clusters despite challenges in cluster separation and alignment with accurate data distributions. Birch demonstrates superior cluster separation and compactness but requires further refinement for optimal accuracy. The findings underscore the potential of integrating clustering methods into credit risk management frameworks, enhancing financial institutions' predictive accuracy and operational efficiency. This research contributes to the ongoing discourse on practical credit risk assessment tools, providing valuable insights for practitioners in the banking sector. Finally, once segments are identified, banks can tailor marketing messages, product offerings, and customer experiences to better suit each group. This can lead to reduced risk, improved customer satisfaction, higher conversion rates, and ultimately increased revenue and customer segmentation in the context of technology trends is becoming an indispensable part of modern business strategy
Implementation of Stacking Technique Combining Machine Learning and Deep Learning Algorithms Using SMOTE to Improve Stock Market Prediction Accuracy Munthe, Ibnu Rasyid; Rambe, Bhakti Helvi; Hanum, Fauziah; Amanda, Ade Trya; Hutagaol, Anita Sri Rejeki; andrianto, Richi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.421

Abstract

This study introduces a stacking technique that integrates machine learning (ML) and deep learning (DL) algorithms to enhance the accuracy of stock market trend predictions. The stacking model utilizes XGBoost and Random Forest as base models from the ML domain, while Logistic Regression and LSTM (Long Short-Term Memory) function as meta models to optimize predictive accuracy. A significant challenge in stock market data is class imbalance, where certain trends, such as stock price drops, are underrepresented. To mitigate this, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic data for the minority class. This approach helps the model better capture patterns from the underrepresented data while preserving essential information from the majority class. The implementation of SMOTE, coupled with the stacking technique, yielded a substantial improvement in prediction accuracy. The results showed that the Random Forest algorithm achieved an accuracy of 85% with precision, recall, and F1-score all at 85%, while XGBoost and Logistic Regression achieved accuracies of 82% and 81% respectively. For the deep learning models, LSTM reached an accuracy of 83%, while the Stacking Meta Model with LSTM achieved an accuracy of 83% with slightly better precision and recall at 84%. The stacking model, with Logistic Regression as the meta model, ultimately achieved the highest accuracy of 86%, outperforming individual models such as SVM (Support Vector Machine), LSTM, Random Forest, and Logistic Regression (LR). These findings demonstrate the efficacy of combining SMOTE with stacking to address data imbalance and improve stock market predictions. The novelty of this study lies in the integration of advanced ML and DL models within a stacking framework to handle class imbalance in financial datasets. Future research will explore the deployment of this model in a real-time web-based application to support investor decision-making in stock market trend analysis.
Applied Data Science and Artificial Intelligence for Tourism and Hospitality Industry in Society 5.0: A Review Hartatik, Hartatik; Isnanto, R. Rizal; Warsito, Budi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.300

Abstract

The primary purpose of this research is to delve into the emerging trends of artificial intelligence and data science with a specific focus on the tourism and hospitality sectors. A comprehensive methodology used to conduct this research includes collecting article data, conducting analysis and then conducting a review study on data science and artificial intelligence trends. These articles were selected based on metadata sourced from web of science and Scopus metadata. In particular, the research scrutinized and assessed the evolving trends in data science and artificial intelligence   within the hotel and tourism category. This analysis drew data from two prominent databases, Web of Science and Scopus, obtained a total of 4155 articles identified using the software and generated 124 terms in the articles with at least ten co-occurrence relationships. The findings of this study explain the huge potential, namely the trend of data application of science and artificial intelligence   in the tourism sector which is categorized in five distinct areas: forecasting tourist demand, implementing customized service recommender systems for the tourism industry, classifying tourist behavior patterns in automation, analyzing and understanding tourist behavior, developing tourist destinations, and planning itineraries. Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, contributions of this research it offers valuable insights into the future directions of apllied data science and artificial intelligent research are represents the pioneering effort to analyze of applying machine learning to advance artificial intelligence and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.Additionally, the research anticipates a heavy emphasis on future studies on predicting travel demand. Looking ahead, this research extends the foundations laid by previous review studies primarily focusing on knowledge and forecasting methodologies in the tourism sector. The conclusions drawn in this research are well-supported by the evolving landscape of knowledge in this field. Furthermore, it offers valuable insights into the future directions of sentiment analysis research. Notably, this paper represents the pioneering effort to comprehensively analyze the methodology of applying machine learning to advance AI and big data within the hotel and travel industries. The authors propose several avenues for future research in this domain based on the data unearthed.

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