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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 25 Documents
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Search results for , issue "Vol 5, No 1: JANUARY 2024" : 25 Documents clear
Sentiment Unleashed: Electric Vehicle Incentives Under the Lens of Support Vector Machine and TF-IDF Analysis Johan Reimon Batmetan; Taqwa Hariguna
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.162

Abstract

This research examines public sentiment regarding electric vehicle incentives through sentiment analysis of online comments. These incentives include tax deductions and other financial rewards offered to promote the adoption of electric vehicles. In this study, the researchers collected and analyzed over 1,000 comments from various online platforms to understand the public's perspective on these incentives. The study employs Support Vector Machine (SVM), a powerful machine learning algorithm, as the main method and utilizes Term Frequency-Inverse Document Frequency (TF-IDF) to analyze comment texts. The research findings depict significant variation in public sentiment regarding electric vehicle incentives. Approximately 57.3% of comments express negative sentiment towards these incentives, while 33.2% are positive, and the rest are neutral. There is strong support for these incentives, particularly from a financial standpoint. However, some dissatisfaction is expressed, especially regarding electric vehicle prices and charging infrastructure availability. External factors such as government policies and vehicle prices significantly influence public sentiment. Easy access to charging infrastructure also plays a crucial role in shaping positive sentiment. Environmental issues also contribute to a positive view of electric vehicle incentives. Policy recommendations arising from this research emphasize the need to consider these factors when designing and implementing electric vehicle incentives. Improvement efforts in pricing, infrastructure, and environmental education can help enhance electric vehicle adoption in society. This research provides valuable insights into public sentiment towards electric vehicle incentives and the factors influencing such sentiment. The results can serve as a foundation for better decision-making to support the development of sustainable and environmentally friendly electric vehicles.   
A Comparative Study on Data Collection Methods: Investigating Optimal Datasets for Data Mining Analysis Hendra Jatnika; Ari Waluyo; Abdul Azis
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.148

Abstract

This study is dedicated to evaluating the efficiency of diverse data collection methods in obtaining optimal data for computational data mining. The investigation meticulously compares the questionnaire and web mining methodologies within the framework of SVM and NBC algorithms to discern the flexibility inherent in each data type. The outcomes of this comprehensive analysis demonstrate that questionnaires showcase remarkable flexibility, exhibiting accuracy rates surpassing 80% in both algorithms, along with AUC values exceeding 0.9 when contrasted with data acquired through web mining techniques. These results underscore the paramount importance of the dataset collection method in the realm of computational data mining. The study contributes compelling evidence that advocates for the superiority of the questionnaire data collection method over web mining in the specific context of computational data mining. The questionnaire method not only outperforms in terms of flexibility but also achieves high accuracy, making it a more reliable choice for acquiring data in this domain. Beyond its practical implications, the research highlights a critical aspect of methodology in data collection by emphasizing the necessity of exploring and assessing methods that may have been overlooked in previous research endeavors. This underscores the continuous evolution of research methodologies and the need for ongoing exploration to enhance the robustness and effectiveness of data collection in computational data mining studies.   
Exploring Essential Skills for Sustainable Community Leadership: A Data Analysis Perspective on SDGs Alignment and Model Matee Pigultong
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.174

Abstract

This research explores the essential skills required by Thai Buddhist moral teacher monks as community leaders for sustainable development. These monks, representing Buddhism, play a crucial role in conserving teachings and fostering spiritual well-being. The study, employing mixed methods, identifies a comprehensive set of 17 skills necessary for their leadership roles, ranging from academic coordination to digital media- based Dhamma teaching and environmental management. The findings highlight a direct alignment between these skills and specific United Nations' Sustainable Development Goals (SDGs). The identified skills contribute significantly to achieving inclusive education, sustainable economic growth, resilient infrastructure, responsible consumption, environmental protection, and the promotion of peaceful societies. In conclusion, this research emphasizes the importance of cultivating a diverse skill set among Thai Buddhist moral teacher monks, offering valuable insights for policymakers, educators, and religious institutions seeking to enhance leadership capabilities and align efforts with global sustainability objectives.
Exploring ADR Trends: A Data Mining Approach to Hotel Room Pricing, Cancellations, and EDA Nina Kurnia Hikmawati; Yudi Ramdhani; Wartika Wartika
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.165

Abstract

This study investigates the intricacies of hotel reservation cancellations by analyzing a comprehensive dataset that includes information from both City Hotel and Resort Hotel. Through a thorough examination of various aspects, the research provides detailed insights into cancellation tendencies, daily rates, seasonal trends, and the influence of geographic factors and market segments on cancellation behavior. The overall cancellation and non-cancellation ratios indicate a notable non-cancellation rate of 62.86%, showcasing a high level of guest confidence in their reservations. Conversely, the 37.14% cancellation ratio raises concerns about potential negative repercussions. A comparative analysis between City Hotel and Resort Hotel reveals a significant difference in cancellation rates, emphasizing the need for tailored strategies at City Hotel to enhance booking stability. The study on Average Daily Rate (ADR) for both hotels bring attention to price differences and seasonal trends. Resort Hotel's higher ADR suggests potential advantages in location or amenities. Seasonal trends, particularly the highest ADR during the summer, provide valuable insights for resource planning. The variation in cancellation rates based on countries emphasizes the importance of focused strategies in regions with high cancellation rates, as seen with Portugal having the highest cancellation rate (77.70%). Analysis of hotel customer market segments identifies Online Travel Agencies (OTA) as the segment with the highest cancellation rate (46.97%). These findings present opportunities for tailored marketing and cancellation policies based on the characteristics of each segment. In conclusion, this research offers strategic insights for hotel managers to enhance booking stability, design competitive pricing policies, and understand the impact of geographic factors and market segments on cancellation behavior.
Applying Structural Equation Modeling to Explore the Intention to Use Midi Kriing App Suwandi S. Sangadji; Tanti Handriana; Nugrahini Susantinah Wisnujati; Sarbaini A. Karim
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.157

Abstract

In the rapidly evolving digital landscape, the surge in e-commerce transactions underscores the need for innovative strategies to enhance user satisfaction, trust, and sustainable app usage. This research focuses on the Midi Kriing App, operated by PT Midi Utama Indonesia Tbk, a key player in the e-commerce industry. The study aims to bridge knowledge gaps by investigating factors influencing user intention, specifically e-service quality and e-trust, and their impact on user satisfaction. Employing a quantitative approach with an associative design, data was gathered from 190 Midi Kriing App users in Surabaya, Indonesia. Structural Equation Modeling (SEM), particularly Partial Least Squares (PLS) in SmartPLS, was utilized to explore relationships between variables. Research findings indicate that e-service quality and e-trust significantly impact user satisfaction, with a p-value of 0.00. Similarly, user satisfaction significantly influences the intention to use the Midi Kriing App, with a p-value of 0.00. Among these hypotheses, the statistical t-value of user satisfaction with the intention to use the Midi Kriing App, at 9.871, is higher than the relationship between e-service quality and e-trust with user satisfaction. Nevertheless, these hypothesis tests confirm statistically significant relationships, supporting the reliability and significance of each construct's measurement instruments. In conclusion, this research emphasizes the pivotal role of satisfaction in its relation to e-service quality, e-trust, and the intention to use the Midi Kriing App. Managerial implications stress the importance of enhancing these factors to drive app usage. Improving e-service quality can be achieved through active efforts such as enhancing responsiveness, reliability, and user-friendliness. Similarly, building e-trust involves securing user data and providing a positive user experience.
A Mixed-Methods Data Approach Integrating Importance-Performance Analysis (IPA) and Kaiser-Meyer-Olkin (KMO) in Applied Talent Cultivation Zhang Zhang; Thosporn Sangsawang; Kitipoom Vipahasna; Matee Pigultong
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.170

Abstract

This study endeavors to establish an assessment framework for cultivating undergraduate applied talent, specifically emphasizing data science competencies, in alignment with the development of China's regional economy. A mixed-methods approach, integrating focus group interviews and questionnaire surveys conducted over three rounds of data collection, was employed. The collected data underwent rigorous reliability and validity analyses utilizing SPSS software. An Importance-Performance Analysis (IPA) was executed to construct a performance chart, evaluating the effectiveness of a 24-item framework designed to encompass key aspects of data science education. The initial internal consistency α coefficients for Questionnaire 2 and Questionnaire 3 were found to be .892 and .913, respectively, surpassing the 0.7 threshold, indicating a high level of reliability for all items related to data science competencies. The Kaiser-Meyer-Olkin (KMO) measurements approaching approximately 0.9 affirmed the efficiency of the questionnaire, specifically designed to gauge the relevance and effectiveness of data science-related indicators in the context of applied talent cultivation and regional economic development. Furthermore, the study underscores the significance of indicators such as teamwork, regional market research, and business opportunity identification within the domain of data science. It identifies gaps between key indicators and lower-performing indicators, proposing strategic improvement measures to enhance the alignment of applied talent cultivation objectives with the evolving needs of regional economic development, particularly in the data science landscape. The research findings not only contribute to a foundational understanding of data science competencies in applied talent cultivation but also lay the groundwork for innovative reforms in future talent cultivation models. By clarifying objectives and better aligning them with the dynamic demands of regional economic development, this study sets the stage for transformative advancements in the field of applied talent cultivation, particularly within the realm of data science.
Developing the Readiness and Success Model of Information System Implementation in the Indonesian Equestrian Industry Ajang Sopandi; Nor Adnan Yahaya; Aang Subiyakto
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.145

Abstract

This study reports on the incorporation of technology readiness models in information system (IS) success models in the context of assessing readiness factors and the success of information system integration in the equestrian sports industry in Indonesia. As found in several information systems studies, many IS models are developed by adopting, combining, and adapting previous models. Researchers developed this model based on input-process-output logic and processional and causal models of information system success models. The developed model is structured by involving 12 variables and 62 indicators. The path of influence between variables is described by 30 links. In the research implementation stage, the model is also broken down into more detailed assessment instruments. Although these model development studies may have limitations on the assumptions used and the researchers' understanding, they can make theoretical contributions, particularly in terms of proposed new models. In addition, transparency in model development, proposed models, and data collection instruments may also be a practical consideration for advanced research in the context of readiness and successful implementation of information systems in the equestrian sports industry in Indonesia
Machine Learning Classifier Algorithms for Ransomware Lockbit Prediction Ibrahiem M. M. El Emary; Khalil A. Yaghi
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.161

Abstract

Advanced virus known as ransomware has been spreading quickly in recent years, resulting in considerable financial losses for a variety of victims, including businesses, hospitals, and people. Modern host-based detection techniques need to first infect the host in order to spot abnormalities and find the malware. When the system is infected, it can already be too late because some of the assets have been exfiltrated or encrypted by the malware. On the other hand, as most ransomware families attempt to connect to command-and-control servers before to executing their damaging payloads, network-based methods can be helpful in detecting ransomware attacks. Therefore, one of the most important methods for early identification can be a detailed examination of ransomware network activity. This study presents a thorough behavioral analysis of the ransomware LockBit. In early 2022, ransomware, particularly targeting data on endpoints in Indonesia, was enough to horrify the news online. LockBit ransomware is one of the ransomwares that is particularly worrisome in Indonesia, so study is required to combat the ransomware. Static and dynamic analyses are used to study the ransomware; the former involves deciphering the portable executable (PE) file, while the latter involves actually running the ransomware. These analyses will reveal the impurity and resolve of the LockBit ransomware. Examine the running operations, the resources utilized, the network activities the ransomware performed, and the effect it had on the impacted operating system to try to build a scenario for preventative measures. The real effects of the ransomware-as-a-service (Raas) attacks conducted by the LockBit ransomware are demonstrated in this research. In this work, we describe an attribute selection-based system for identifying and avoiding ransomware that uses a variety of machine learning techniques, such as neural network-based frameworks, to classify the malware's security grade. We used a range of machine learning approaches, such as Decision Tree-DT, Random Forest-RF, Naive Bayes-NB, and Logical Regression-LR based classifiers, on a selected set of attributes for ransomware detection. The results of the study demonstrate that the Random-Forest predictor outperformed different classifiers by achieving the best accuracy, precision, recall, and F1-Score.
Data Envelopment Analysis of Scientific Research Performance for Higher Vocational Colleges Lin Zhou; Sutthiporn Boonsong; Issara Siramaneerat; Thosporn Sangsawang; Pakornkiat Sawetmethikul
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.166

Abstract

This research aims to evaluate the scientific research performance of higher vocational colleges in Sichuan within the evolving landscape of data science. The study pursues two primary objectives: firstly, to assess the scientific research performance of these institutions using advanced methodologies such as Data Envelopment Analysis (DEA) and the Malmquist index models; secondly, to explore the intricate relationship between scientific research inputs and efficiency through the lens of Rough Set theory. The dataset comprises scientific research inputs and outputs from 30 higher vocational colleges, spanning the years 2019 to 2021. The findings underscore an overall positive trend in scientific research performance across the higher vocational colleges under examination. However, a nuanced analysis using DEA and Malmquist index models identified that only five institutions demonstrated robust performance during the specified period. Furthermore, the study delves into the influential factors affecting scientific research efficiency, revealing that internal expenditure on scientific research funds and the presence of senior and above professional teachers play pivotal roles. These insights are gleaned through the application of Rough Set theory, providing a unique perspective within the realm of data science. In conclusion, the research recommends strategic interventions to improve research management and resource allocation, emphasizing their role in enhancing efficiency and mitigating disparities among higher vocational colleges in Sichuan, particularly in the context of data science. The study adopts a holistic approach, employing an integrated model that combines DEA, Malmquist, and Rough Set theory for a comprehensive evaluation of research performance within the evolving landscape of data science.
Comparative Analysis of SVM and RF Algorithms for Tsunami Prediction: A Performance Evaluation Study Husni Teja Sukmana; Yusuf Durachman; Amri Amri; Supardi Supardi
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.159

Abstract

This study explores the use of machine learning algorithms, specifically SVM and RF, for predicting tsunamis, a crucial aspect of disaster management. The research utilized earthquake data from 2001 to 2023, evaluating these models based on accuracy, precision, recall, F1-score, and ROC AUC, emphasizing features like magnitude, depth, and alert levels. The SVM model demonstrated an accuracy of 65.61%, precision of 70.59%, recall of 19.67%, F1-score of 30.77%, and ROC AUC of 62.15%. In comparison, the RF model showed an accuracy of 61.15%, precision of 50.00%, higher recall of 36.07%, F1-score of 41.90%, and ROC AUC of 63.84%. These results highlight the distinct strengths of each model: SVM's precision makes it suitable for minimizing false positives, while RF's higher recall indicates its effectiveness in detecting actual tsunamis. The findings underscore the significance of selecting the appropriate model for tsunami prediction based on specific disaster management needs and the inherent trade-offs in model selection. The research concludes that SVM and RF models provide valuable yet distinct contributions to tsunami prediction. Their application should be customized to disaster management requirements, balancing accuracy and operational efficiency. This study contributes to disaster risk management and opens avenues for further research in enhancing the accuracy and reliability of machine learning in natural disaster prediction and response systems.

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