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Data Science Insights
Published by PT Visi Media Network
ISSN : -     EISSN : 30311268     DOI : https://doi.org/10.63017/jdsi.v3i2
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Data Science Insights, with ISSN 3031-1268 (Online) published by PT Visi Media Network is a journal that publishes Focus & Scope research articles, which include Data Science and Machine Learning; Data Science and AI; Blockchain and Advance Data Science; Cloud computing and Big Data; Business Intelligence and Big Data; Statistical Foundation for Data Science; Probability and Statistics for Data Science; Statistical Inference via Data Science; Big Data and Business Analytics; Statistical Thinking in Business; Data Driven Statistical Methods; Statistical Methods for Spatial Data Analytics; Statistical Techniques for Data Analysis; Data Science in Communication; Information and Communication Technology; Graph Data Management for Social Network Applications; Metadata for Information Management; Information/Data: Organization and Access; Information Science and Electronic Engineering; Big Data and Social Science; Data Communication and Computer Network; ICT & Data Analytics. This journal is published by the PT Visi Media Network, which is published twice a year.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 28 Documents
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Assessing Student ICT Knowledge Through Survey and Hands-On Task Mat Yamin, Fadhilah; Wan Ishak, Wan Hussain; Husin, Abdullah
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.2

Abstract

To date, knowledge on information and communication technology (ICT) is a vital to all students. ICT knowledge is the skills of using appropriate ICT devices and software to accomplish a task. This knowledge is gain through practices and experience when using the ICT. Besides the academic excellence, ICT knowledge is another most important assets for any graduate before entering the job market. This is because ICT has become one of the key components of any organization's operations. This study aims to assess the level of ICT mastery among final year undergraduate students. This study employed two main methods of questionnaire and practical activities. The questionnaire aimed to assess students’ basic knowledge on ICT while the practical activities aimed to assess students’ actual skills. The findings from the questionnaire show that students believe that they have adequate knowledge on ICT. However, practical activities show that students' true mastery is still at a moderate level. Therefore, students need to enhance their ICT skills to enhance their value and capabilities in the job market. Students also should take the advantage of exploring and applying their ICT skills during their learning activities such as preparing, completing, and presenting their assignments. These are crucial exercises that can improve their ICT knowledge and skills.
A Novel Extension of the Fréchet Distribution: Statistical Properties and Application to Groundwater Pollutant Concentrations Suleiman, Ahmad Abubakar; Daud, Hanita; Othman, Mahmod; Sawaran Singh, Narinderjit Singh; Ishaq, Aliyu Ismail; Sokkalingam, Rajalingam; Husin, Abdullah
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.3

Abstract

In this work, we propose and study a novel generalization of the Fréchet distribution called the odd beta prime Fréchet (OBPF) distribution. This distribution was an extension of the Fréchet distribution by applying the odd beta prime generalized family of distributions. The proposed model can be expressed as a linear mixture of Fréchet densities. The shapes of the density function possess great flexibility. It can accommodate various hazard shapes, such as increasing, decreasing, and reversed J. Some important statistical properties of the OBPF are derived, including the ordinary and incomplete moments, order statistics, and quantile function. We have used the maximum likelihood estimation method to estimate the model parameters. The application and flexibility of the new distribution are empirically proven using groundwater pollution data sets compared to other competing distributions. The new model can be used instead of existing lifetime distributions and is suitable to fit data with right-skewed and left-skewed behaviors
Forecasting Model using Fuzzy Time Series for Tourist Arrivals in Langkawi Rahim, Nur Fazliana; Othman, Mahmod; Husin, Abdullah
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.4

Abstract

In several applications, fuzzy time series forecasting was utilized to generate predictions about the future value of variables that were of interest. This study focuses on predicting how many tourists will visit Langkawi since a precise estimate of tourism demand would enable the government to decide whether to raise or lower the money allocated to the sector in the future. To be more precise, this study attempts to choose the best model that may be applied to forecast visitors to Langkawi and assist the public and private sectors in managing tourism-related preparations. The data collection contains monthly data from January 2009 to December 2010 and was directly extracted from the Langkawi Development Authority (LADA) website. When estimating visitor arrivals to Langkawi, the suggested fuzzy time series' accuracy was compared to that of the earlier technique. The experimental findings in this study demonstrated that the Fuzzy Time Series approach can anticipate more accurately. The results of this study could serve as inspiration for the public and private sectors to take action to bring more tourists to Langkawi, make their stay pleasant and pleasurable, and improve the possibility that they would visit again and again in the future.
Forecasting the Southeast Asian Currencies against the British Pound Sterling Using Probability Distributions Suleiman, Ahmad Abubakar; Daud, Hanita; Othman, Mahmod; Husin, Abdullah; Ishaq, Aliyu Ismail; Sokkalingam, Rajalingam; Abdullah, Mohd. Lazim; Khan, Iliyas Karim
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.5

Abstract

The current study aimed to identify the most suitable probability distribution function (pdf) for modeling the exchange rates of three countries. Financial data is essential to many people and to the management of a country. Volatility in financial data influences individual and the country's economic growth. This volatility in the exchange rates between the Malaysian Ringgit (MYR), Singapore Dollar (SGD), and Thailand Thai Baht (THB) against British Pound Sterling (GBP) is found to be very high which make it difficult to model and forecast. This is what has necessitated the development of an accurate and reliable approach for assessing and reducing the risks of trading in any of these currencies.
The need for an enhanced IoT-based malware detection model using Artificial Intelligence (AI) algorithm: A Review Maidin, Siti Sarah; Yahya, Norzariyah
Data Science Insights Vol. 1 No. 1 (2023): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v1i1.6

Abstract

The interconnected world using technology has opened the door for cyberattacks. For example, the utilization of Internet of Things (IoT) devices has increased the exposure to malware attacks. The massive amount of data generated by the IoT devices leads to the possibility of infections in the network. Due to the diverse nature of the IoT devices and the ever-evolving nature of their environment, it can be challenging to devise very comprehensive security measures. Therefore, the application of Artificial Intelligence (AI) in detecting malware has gained attention as a suitable tool for detecting malware due to its strength in malware classification. This research aims to review malware detection in IoT devices using AI and its challenges.
Application of Geolocation Methods in Student Attendance System Design Rizya Pratama , Yoga; Siswanto, Apri
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.10

Abstract

Universitas Islam Riau is one of the universities in Riau province that is of interest to high school graduate students as a place to continue their studies at a higher level. Implementing the student attendance process at the Universitas Islam Riau is still done manually; this causes less efficiency and effectiveness of attendance activities, starting from data collection, processing presence data, and storing and searching processes, which take time. In some cases, fraud may occur, such as falsifying the presence of someone represented by another party. Then, we need a system that can record the attendance of students whose positions are within the scope of the class radius. Geolocation can capture device coordinates by utilizing latitude and longitude, which will be used to measure the distance between classes and students. If the student's position is outside the class radius determined by each lecturer, then the student cannot fill in attendance. If the student's position is within the scope of the class radius that has been determined, students can fill in attendance. In the research, we succeeded in designing a student attendance system based on the geolocation method. Security to overcome fake GPS managed to function properly, and fingerprints to take attendance can work properly. From the results of Black box testing, the system can run well and is free from syntax and functional errors.
Enhancing IoT Security: A Synergy of Machine Learning, Artificial Intelligence, and Blockchain bin Zainuddin, Ahmad Anwar; Sairin, Haziqah; Mazlan, Izzah Atirah; Muslim, Nur Najma Aisyah; Wan Sabarudin, Wan Afiqah Syahmina
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.11

Abstract

With the advancement of technological age, the growth of technology not only affecting the network and security but also the Internet of Things (IoT), this invites the need for robust cybersecurity solutions become increasingly important. To further enhance the existing safety mechanisms used in the IoT industry, the assimilation of Machine Learning (ML) with Artificial Intelligence (AI) along with the technology of blockchain can further offers a high potential solution in order to face the challenges faced in the IoT security. ML and AI algorithms can enhance the detection and prevention by reviewing variety of data, recognizing patterns and predicting the potential vulnerabilities of the cyber threats in IoT. Blockchain, on the other hand, provides a decentralized and tamper-proof platform for secure data storage and transactions. By leveraging these technologies, IoT systems can achieve a sustainable security, ensuring the protection of sensitive and important information as well as protecting the Confidentiality, Availability, and Integrity (CIA) triad of the infrastructure of the network. This research incorporates the variety of machine learning approaches, including the trees of decision, the K-nearest neighbours, the artificial neural networks, convolutional neural networks, the support of vector machines, Bayesian networks, ensemble classifiers, genetic programming, logistic regression as well as the deep learning tactics. Through this, the deriving insights of raw data can then help these technologies to aim and create a modern and dynamically improved security solutions for the evolving landscape of IoT devices. Future research should focus these upcoming issues in order to fully comprehend potential of ML applications in security intelligence of IoT.  
Revolutionizing Healthcare: A Comprehensive Review of Metaverse Integration for Enhanced Patient Outcomes, Medical Practices, The Potential Applications, Challenges and Future Direction bin Zainuddin, Ahmad Anwar; Md Saifuddin , Muhammad Hafiz Faruqi; Wan Azman , Wan Muhammad Arif; Fuadi , Abdul Ghafur; Ahmad Nadzri , Ahmad Adam Nadzeeran; Mohamad Fadil , Muhammad Hussaini; Aisyah Ruzaidi, Nuramiratul
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.12

Abstract

Integrating the Metaverse into the healthcare system can significantly improve patient outcomes, medical education, and healthcare practices. This study examines the various uses of the Metaverse, emphasizing the essential technologies that make it possible, such as Augmented Reality (AR), Virtual Reality (VR), Extended Reality (XR), and digital twins for personalized care. The report highlights the challenges that healthcare professionals may face while suggesting future paths in the healthcare field. Topics examined include telemedicine, medical education, and the potential advancements that can be achieved with AR, VR, XR, and digital twins. Notable uses encompass virtual counseling, medical training and education, digital therapeutics, and telemedicine. While integrating the Metaverse into healthcare can significantly enhance patient care, it may pose privacy and security risks, incur high costs, require legal compliance, and affect trust in patient-technology relationships. However, future trends should address security and legal issues, balance social and virtual life, and enhance medical training and education. By using augmented reality (AR), virtual reality (VR), extended reality (XR), and digital twins, the healthcare industry can improve patient communication, research and decision-making processes, remote healthcare services, precise medical education training, and tailored patient care. These technologies present a unique opportunity to transform healthcare while addressing privacy, security, and sustainability issues. The purpose of this paper is to review the topic comprehensively, provide suggestions for future research and advancement in this domain, and provide insights into the potential of the Metaverse to revolutionize healthcare.
Customer Satisfaction Towards Onsite Restaurant Interactive Self-Service Technology (ORISST) Por Eng Choo; Fadhilah Mat Yamin; Wan Ishak, Wan Hussain
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.15

Abstract

A recent development in the restaurant industry is the use of on-site restaurant interactive self-service technology (ORISST) by some operators who are moving away from traditional service methods. ORISST allows customers to manage dining services independently through interfaces such as self-service kiosks or tabletop tablets. However, the gap in understanding customer satisfaction regarding ORISST is notable as there is a lack of technology-related research in the restaurant industry. The research objectives of this study is to investigate the significant relationship between the four dimensions of SSTQUAL (functionality, design, enjoyment, customization) and customer satisfaction in using ORISST. In this study, quantitative research was conducted. Data was collected via google form from 293 STML students at UUM who had experience using ORISST. The findings of this study show that functionality, design and enjoyment have a significant positive relationship with customer satisfaction in using ORISST, with functionality being the most significant determinant. In contrast, customization has no significant relationship with customer satisfaction in using ORISST. All these findings may provide valuable suggestions to restaurant operators on how to properly implement ORISST to improve their business performance and attract more customers. This study has broadened the understanding of customer satisfaction towards ORISST which has yet to be fully explored.
Database-Specific Keyword Frequency Analysis in Merged Web Log Data: A Preprocessing Method Wan Ishak, Wan Hussain; Nurul Farhana Ismail; Fadhilah Mat Yamin; Husin, Abdullah
Data Science Insights Vol. 2 No. 1 (2024): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v2i1.16

Abstract

This study investigates the complex intricacies of web log data within the Electronic Resources module of the Perpustakaan Sultanah Bahiyah (PSB) website at Universiti Utara Malaysia (UUM). Serving as a cornerstone of academic infrastructure, the Electronic Resources module acts as a vital gateway, seamlessly connecting the UUM academic community to a vast repository of scholarly information. To tackle challenges posed by the size and complexity of web log data, the research employs a meticulous preprocessing method, involving the restructuring of raw data, outlier cleaning, and user session identification, laying the foundation for a comprehensive analysis. The study further explores the identification of search keywords embedded in the log file, employing a systematic process that transforms data into a structured format. The subsequent extraction of databases and keywords yields intriguing findings, prominently highlighting IEEE and Serial Solution databases. The analysis of 19,146 keywords associated with 11 databases offers valuable insights into user behavior, preferences, and the overall effectiveness of the Electronic Resources module. The identification of frequent keywords not only provides analytical insights but also serves to accelerate users' search processes, reducing cognitive load and fostering a more efficient research experience. This research contributes to the optimization of user experiences and the ongoing refinement of digital library services, aligning them with the evolving needs of the academic community

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