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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 5 Documents
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Search results for , issue "Vol. 1 No. 1 (2023): Journal of Data Science Insights" : 5 Documents clear
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.

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