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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
Fuzzy SAW Based Decision Model for Determining the Priority Scale of ICT Handling in Public Sector Organizations Yulanda, Rissa; Utama, Ditdit Nugeraha
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.v5i4.419

Abstract

Determining the priority of handling information and communication technology (ICT) infrastructure in public sector organizations can help them take the right actions in maximizing limited budgets, to handle technical maintenance, improve human resource (HR) capabilities and governance of ICT infrastructure. The purpose of this research is to develop a decision-making model that is able to determine the priority of handling ICT, especially in public sector organizations. Decision support modeling (DSM) with Fuzzy Simple Additive Weighting (Fuzzy SAW) method is used to build a computer model that supports decision making in this case. The study consists of four stages, which are an integral part of the Fuzzy SAW-based DSM process. These stages include analyzing the case, determining parameters, collecting data and building the model. This study produces a Fuzzy SAW-based DSM consisting of 14 parameters, namely governance, number of internet users, number of ICT managers, work experience of ICT managers, bandwidth service capacity, router device age, educational background of ICT managers, network firewalls, network maintenance, server room availability, Network Attached Storage (NAS) storage devices, neatly organized cable devices, adequate electrical resources and internet connection backup networks, to determine the priority ranking of 34 existing alternatives. The final result of this research is a Fuzzy SAW-based DSM that is able to provide a priority score for handling ICT infrastructure in Public Sector Organizations. The findings in this model show that the parameter weights affect the final score of the model. Thus, the conclusion of this research is that the model has been successfully implemented, making a significant contribution in providing guidance on determining accurate ICT infrastructure handling for public sector organizations.
ARP Spoofing Attack Detection Model in IoT Network using Machine Learning: Complexity vs. Accuracy Alsaaidah, Adeeb; Almomani, Omar; Abu-Shareha, Ahmad Adel; Abualhaj, Mosleh M; Achuthan, Anusha
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.v5i4.374

Abstract

Spoofing attacks targeting the address resolution protocol, or the so-called ARP, are common cyber-attacks in IoT environments. In such an attack, the attacker sends a fake message over a local area network to spoof the users and interfere with the communication transferred from and into these users. As such, to detect such attacks, there is a need to check the network gateways and routers continuously to capture and analyze the transmitted traffic. However, there are three major problems with such traffic data: 1) there are substantial irrelevant data to the ARP attacks, 2) there are massive patterns in the way by which the spoof can be implemented, and 3) there is a need for fast processing of such data to reduce any delay resulting from the processing stage. Accordingly, this paper proposes a detection approach using supervised machine learning algorithms. The focus of this paper is to show the tradeoff between speed and accuracy to offer various solutions based on the demanded quality. Various algorithms were tested to find a solution that balanced time requirements and accuracy. As such, the results using all features and with various feature selection techniques were reported. Besides, the results using simple classifiers and ensemble learning algorithms were also reported. The proposed approach is evaluated on an IoT network intrusion dataset (IoTID20) collected from different IoT devices. The results showed that the highest accuracy is obtained using the RF classifier with a subset of features produced by the wrapper technique. In such a case, the accuracy obtained was 99.74%, with running time equal to 305 milliseconds. However, If time is more critical for a given application, then DT can be used with the whole feature set. In such a case, the accuracy was 99.41%, with running time equal to 11  milliseconds.
The Efficacy of Online Gamification in Improving Basic English Skills for Fourth-Grade Students Pasawano, Tiamyod; Sangsawang, Thosporn
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.v5i4.410

Abstract

The study aimed to achieve three main objectives: 1) to develop an online gamification system using digital learning platforms for teaching English to Grade 4 students, following the E1/E2 = 80/80 efficiency criterion, 2) to compare students' achievement in Basic English through online gamification, and 3) to assess students' satisfaction with the use of online gamification in learning Basic English. The sample comprised 30 Grade 4 students from Settabutr Upathum School in the academic year 2022, selected through purposive random sampling. Research instruments included online Zoom classes, lesson plans, and interactive learning platforms. The study employed mean, standard deviation, and t-tests for dependent samples for data analysis. The results revealed an efficiency value of E1/E2 as 70.00/69.00, falling short of the 80/80 criteria. Several factors, such as the comprehensive nature of testing macro skills using digital media beyond cognitive abilities, may have contributed to not meeting the set criterion. Furthermore, a significant improvement in learning achievements in Basic English was observed among Grade 4 students who used online gamification compared to traditional methods, with higher scores in achievement tests at a significance level of 0.05. Finally, students expressed a good level of satisfaction with the online gamification approach in learning Basic English.
Analyzing the Impact of Publicity and e-WOM on Indonesian Tourists’ Visit Intention to Seoul through Destination Awareness and Preference: A Structural Equation Modeling Approach Herstanti, Ghassani; Suhud, Usep; Handaru, Agung Wahyu
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.v5i4.531

Abstract

This research explores the role of digital publicity and electronic word of mouth (E-WOM) in shaping Indonesian tourists' intentions to visit Seoul. By integrating destination awareness and preference as mediating variables, the study provides a holistic view of how publicity and EWOM interact to influence visit intentions. Digital publicity, including news coverage and promotional content on social media, raises initial awareness of Seoul by highlighting its attractions, culture, and experiences. E-WOM, expressed through online reviews, travel blogs, and social media shares, further enhances the perception of Seoul by providing authentic, peer-driven narratives. These user-generated insights are particularly impactful, as they foster trust and add an emotional dimension to tourists’ perception of the destination. Using a structural equation modeling approach, the study analyzes survey responses from Indonesian tourists to validate six core hypotheses, examining the direct and indirect effects of publicity and E-WOM on destination awareness, preference, and visit intention. Results indicate that both digital publicity and E-WOM significantly contribute to tourists' awareness and preference for Seoul, with preference being a particularly strong predictor of visit intention. The findings underscore the importance of aligning digital publicity efforts with targeted E-WOM strategies, enabling tourism marketers to build both cognitive awareness and emotional appeal, which ultimately drive visit intention. These insights are valuable for tourism stakeholders aiming to enhance destination marketing strategies, as they suggest that a combined approach—leveraging both structured publicity and organic E-WOM—can effectively increase a destination’s appeal. By focusing on creating authentic, accessible content and fostering positive online word of mouth, tourism authorities can better attract tourists and establish Seoul as a top choice for Indonesian travelers.
Aspect-Based Sentiment Analysis of Healthcare Reviews from Indonesian Hospitals based on Weighted Average Ensemble Setiawan, Esther Irawati; Tjendika, Patrick; Santoso, Joan; Ferdinandus, FX; Gunawan, Gunawan; Fujisawa, Kimiya
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.v5i4.328

Abstract

Public assessments are essential for evaluating hospital quality and meeting patient demand for superior medical treatment. This study offers a novel approach to aspect-based sentiment analysis (ABSA), which consists of aspect extraction, emotion categorization, and aspect classification. The goal is to examine patient reviews (6,711 reviews) from Google assessments of 20 Indonesian hospitals, broken down by categories including cost, doctor, nurse, and other categories. For example, there are 469 good, 66 negative, and 7 neutral ratings for cleanliness and 93 positive, 125 negative, and 19 neutral reviews for pricing in the sample, which covers a range of attitudes. Using the Conditional Random Field (CRF) approach, aspect phrase extraction was refined and word characteristics and positional tags were adjusted, resulting in an improvement in the F1-score from 0.9447 to 0.9578. The Support Vector Machine (SVM) model had the greatest F1-score of 0.8424 out of two strategies used for aspect categorization. With the addition of sentiment words, sentiment classification improved and led by SVM to an ideal F1-score of 0.7913. For aspect and sentiment classification, a Weighted Average Ensemble approach incorporating SVM, Naïve Bayes, and K-Nearest Neighbors was employed, yielding F1-scores of 0.7881 and 0.8413, respectively. The use of an ensemble technique for sentiment and aspect classification and the incorporation of hyperparameter optimization in CRF for aspect term extraction, which led to notable performance gains, are the innovative aspects of this work.
Leveraging Data Analytics for Student Grade Prediction: A Comparative Study of Data Features Misinem, Misinem; Kurniawan, Tri Basuki; Dewi, Deshinta Arrova; Zakaria, Mohd Zaki; Nazmi, Che Mohd Alif
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.v5i4.442

Abstract

In educational settings, a persistent challenge lies in accurately identifying and supporting students at risk of underperformance or grade retention. Traditional approaches often fall short by applying generalized interventions that fail to address specific academic needs, leading to ineffective outcomes and increased grade repetition. This study advocates for integrating machine learning algorithms into educational assessment practices to address these limitations. By leveraging historical and current performance data, machine learning models can help identify students needing additional support early in their academic journey, allowing for precise and timely interventions. This research examines the effectiveness of three machine learning algorithms: Naive Bayes, Deep Learning, and Decision Trees. Naive Bayes, known for its simplicity and efficiency, is well-suited for initial data screening. Deep Learning excels at uncovering complex patterns in large datasets, making it ideal for nuanced predictions. Decision Trees, with their interpretable and actionable outputs, provide clear decision paths, making them particularly advantageous for educational applications. Among the models tested, the Decision Tree algorithm demonstrated the highest performance, achieving an accuracy rate of 86.68%. This high precision underscores its suitability for educational contexts where decisions need to be based on reliable, interpretable data. The results strongly support the broader application of Decision Tree analysis in educational practices. By implementing this model, educational administrators can better identify at-risk students, tailor interventions to meet individual needs, and ultimately improve student success rates. This study suggests that Decision Trees could become a vital tool in data-driven strategies to enhance student retention and optimize academic outcomes.
An Improved Prediction of Transparent Conductor Formation Energy using PyCaret: An Open-Source Machine Learning Library Olanipekun, Ayorinde Tayo; Mashao, Daniel
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.v5i4.202

Abstract

Designing innovative materials is necessary to solve vital energy, health, environmental, social, and economic challenges. Transparent conductors are compounds that combine low absorption visible range and good electrical conductivity, which are essential properties for conductors. Technological devices such as photovoltaic cells, transistors, photovoltaic cells and sensors majorly rely on combining the two properties due to their relevancy in an optoelectronic application. Meanwhile, fewer compounds exhibit both outstanding conductivity and transparency suitable for their application in transparent conducting materials. Kaggle hosted an open big-data competition organized by novel material discovery (NOMAD) to address the importance of finding new material with the ideal functionality. The competition was organized to identify the best machine learning (ML) to predict formation enthalpy (indicating stability) for 3000  (AlxGaylnz)2NO3Ncompounds datasets; where x, y, and z can vary from the constraints x+y+z=1. Here we present a prediction using an open-source machine learning library in Python called PyCaret to summarise top-ranked ML algorithms. The gradient boosting regressor (GBR) model performed best with MAE 0.0281, MSE 0.0018 and R2 0.84. The research shows that Machine learning can significantly accelerate the discovery and optimization of materials while reducing cost of computation and required time. Low code tools like PyCaret were used to enhance the machine learning applications in materials science, paving way for more efficient materials discovery processes.
Convolutional Neural Network for Battery System Monitoring and SOC Estimation for Ev Applications to Achieve Sustainability S, Priya; P, Vinoth Kumar; V, Sridevi; Batumalay, M; D, Gunapriya
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.v5i4.237

Abstract

The necessity to develop an alternative energy source to handle the looming energy crisis has arisen due to the recent rise in energy consumption. This is most likely to happen with grid-synchronized electric vehicles (EVs), since vehicle-to-grid (V2G) technology is one of the smart grid's technological advancements that permits energy exchange between EVs and the grid. The merging of EVs with the grid influences the whole electricity system and is susceptible to imbalances in supply and demand, frequency, and voltage. The proposed work focuses on effective and smart control of the Single Ended Primary Inductance Converter (SEPIC) converter with efficient control techniques employed for battery management systems for electric vehicle charging. PI oversees controlling the converter. The battery's calculated state of charge (SOC) is used to make a paradigm-shifting sequence for the converter with workable optimization strategies to lower imbalance issues when EVs are connected to the grid. This leads to the achievement of sustainable development goals (SDGs). Purpose. When the SEPIC converter is connected to a photovoltaic source, it needs to be analyzed in terms of how it switches operations. The source also needs to be used efficiently when it is connected to a battery. Monitoring and SOC estimation of the battery need to be efficiently performed with a quicker response for EV applications. Methods. Convolutional neural networks (CNN) were used to solve the issue; these networks considerably enhance response times and boost system reliability overall. Results. The system operates on the principle that when the battery level is less than 60%, the battery is charged through buck operation, and it is discharged through the boost mode when the SOC exceeds 60%. When linked to the grid, the PI controller regulates both power and practical value. The proposed system demonstrates how battery management-based CNN and SEPIC can switch at high speeds. The system's research directions were established for the results' later application to experimental samples for energy efficiency and process innovation.
Artificial Intelligence Techniques for Early Autism Detection in Toddlers: A Comparative Analysis Shambour, Qusai; Qandeel, Nazem; Alrabanah, Yousef; Abumariam, Anan; Shambour, Mohd Khaled
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.v5i4.353

Abstract

Individuals with autism spectrum disorder are often characterized by complications in social interaction and communication, which can be attributed to disruptions in brain development affecting their perception and interaction with others. While ASD is not a treatable condition, early detection and diagnosis can significantly mitigate its effects. Recent advancements in artificial intelligence have enabled the development of novel diagnostic tools, which can detect ASD at an earlier stage than traditional methods. This study attempts to enhance and automate the diagnostic process by employing a variety of machine learning techniques to identify the most critical characteristics of ASD. To this end, we employed six state-of-the-art machine learning classification models, including Support Vector Machine, Random Forest, k-Nearest Neighbors, Logistic Regression, Decision Tree, and Naive Bayes classifiers, to analyze and predict ASD in toddlers using a non-clinical ASD dataset. Our evaluation focused on a range of performance metrics, including accuracy, precision, recall, and F1-score, to assess the efficacy of each model. Notably, the Logistic Regression model demonstrated the highest accuracy in diagnosing ASD in toddlers, achieving a perfect score of 100% across all metrics.
Developing and Evaluating a Rhythm Reading Practice Kit: A Study on Learning Outcomes and Music Learners Satisfaction in Music Education Using Quantitative Analysis Chantanasut, Thaworada
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.v5i4.471

Abstract

This study presents the development and evaluation of a rhythm reading practice kit aimed at improving rhythm comprehension and overall student satisfaction in music education. The kit was specifically designed for students in the Western Music and Vocal Education Studies Program at Rajamangala University of Technology Thanyaburi and Nawaminthrachinuthit School, Horwang, Nonthaburi. It includes progressive exercises across multiple time signatures, enabling both guided classroom uses and self-directed practice. A quantitative research approach was adopted, utilizing pre-test and post-test assessments to measure academic improvement in rhythm reading skills, coupled with a satisfaction survey to gauge student perceptions. Findings revealed a statistically significant increase in students' post-test scores, with university students achieving a mean improvement of 18.35 points and high school students 15 points. The paired samples t-test results indicated strong significance at the .05 level, underscoring the kit's positive impact on rhythm reading proficiency. Furthermore, the student satisfaction survey highlighted high levels of approval, particularly in areas such as instructional clarity, content alignment, and ease of use for independent learning. These results suggest that the rhythm reading practice kit not only enhances students' rhythm skills but also supports their engagement and enjoyment of the learning process. The study concludes with recommendations for future research, suggesting potential digital integrations to increase accessibility and considering adaptations to address a broader range of musical skills beyond rhythm. The findings contribute to the field of music education by providing evidence of the kit's effectiveness and promoting its use as a tool to foster comprehensive rhythm education in diverse educational settings.

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