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All Journal Data Science Insights
Wan Ishak, Wan Hussain
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Computer Science & IT Engineering

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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.
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
Digital Data Collection among Low ICT-Literate Rural Communities: A Case Study using Google Forms via Smartphones Wan Ishak, Wan Hussain; Yamin, Fadhilah; Ismail, Risyawati Mohamed; Mustafar, Mastora; Abu Bakar, Siti Zakiah
Data Science Insights Vol. 3 No. 2 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i2.121

Abstract

This study investigates the use of Google Forms as a digital tool for daily livestock monitoring among rural, low ICT-literate chicken farmers in Malaysia. A total of 198 responses were collected via smartphones through WhatsApp-distributed forms, allowing participants to self-report poultry conditions while reducing the need for frequent site visits. While the approach proved accessible and cost-effective, analysis revealed significant data quality issues, including inconsistent data entry (e.g., mixed numeric and textual values), unstructured categorical responses, duplicate submissions, ambiguous placeholder values, and the absence of unique identifiers. These challenges limited the reliability and usability of the dataset for meaningful analysis. To address these issues, the study recommends implementing structured input fields, validation rules, unique respondent IDs, and user training materials tailored to low digital literacy. This paper highlights both the potential and pitfalls of digital self-reporting tools in underserved rural contexts and provides practical recommendations for improving data quality in similar monitoring efforts. The findings offer valuable guidance for researchers and practitioners designing data collection systems in constrained environments.
Comparative Analysis of Data Visualization Techniques for Rainfall Data Wan Ishak, Wan Hussain; Yamin, Fadhilah; Maidin, Siti Sarah; Husin, Abdullah
Data Science Insights Vol. 3 No. 2 (2025): Journal of Data Science Insights
Publisher : PT Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v3i2.204

Abstract

Rainfall data is essential for applications such as climate monitoring, agricultural planning, flood forecasting, and water resource management. However, the interpretation of this data is often hindered by its high volume, variability, and multi-scale temporal nature. Effective visualization is critical not only for summarizing complex datasets but also for uncovering patterns, detecting anomalies, and facilitating informed decision-making. Despite the availability of numerous visualization techniques, selecting the most suitable method for rainfall data, especially across varying temporal resolutions is a challenging task. This study presents a comparative analysis of widely used data visualization techniques in the context of rainfall data. The methodology was structured into three phases: understanding the nature of rainfall data, reviewing relevant visualization techniques, and conducting a comparative content analysis. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) evaluation was used to assess each technique’s analytical potential, while a temporal suitability comparison was performed across five time granularities: yearly, monthly, weekly, daily, and hourly. Findings show that no single technique is universally effective. Instead, each method demonstrates specific strengths and limitations depending on the temporal scale and analytical objective. Line charts and bar charts are well-suited for lower-frequency data, while heat maps and scatter plots are more effective for high-resolution, time-sensitive patterns. Box plots and histograms provide valuable insights into data distribution and variability, whereas map-based visualizations excel in spatial analysis but require enhancements for temporal exploration. The study concludes that visualization effectiveness depends on aligning method selection with data characteristics and analytical goals. A thoughtful combination of techniques is often necessary to achieve clarity, reduce misinterpretation, and enhance decision support in rainfall data analysis.
Co-Authors Abdullah Abdullah Abu Bakar, Siti Zakiah Fadhilah Mat Yamin Ismail, Risyawati Mohamed Maidin, Siti Sarah Mat Yamin, Fadhilah Mustafar, Mastora Nurul Farhana Ismail Por Eng Choo Yamin, Fadhilah