Faisal Syafar
He is an associate Professor of Information Technology (IT). He is currently working as a full time lecturer for Department of Electronics and Information Technology, and is the director of Computing and Information Systems (CIS) Research Centre. Faisal is a member of the global Cisco CTO Forum, IEEE and many others international professional community members within IT field. He is Cisco’s most senior technology leadership board, representing Asia-Pacific (APAC). He is also currently active on external commercial and government technology advisory boards. Before joining Cisco, Faisal held senior positions with other telecommunications equipment manufacturers, systems integrators and service providers including Lucent Telecommunications and Telstra. He has also been working as a professional trainer/consultant for many Australian, Germany and Indonesian leading institutions including cyber security, ERP, smart city initiatives, corporate data quality and telecommunication. Faisal holds a Master and Ph.D. in IT from University of South Australia. His research interests focus on the use of Mobile technologies and leveraging data and information to generate quality of healthcare, engineering assets as well as teaching and learning domains. He has published in many academic journals, book chapters and conferences proceedings in various topics covers mobile collaboration technologies and IT healthcare.
visits
UPCOMING EVENTS
17. International Conference on Smart Communication Technologies and Mobile Technologies
03-04 April 2023, Athens, Greece
The Thirteenth International Conference on Mobile Services, Resources, and Users
26-30 June 2023, Nice, Saint-Laurent-du-Var, France
My Latest Research
Mobile learning is characterized as a powerful element of learning and education for facilitating the learning experiences. With enhanced and rapid advancements in technologies of ICTs (Information and Communication Technologies) and mobile, numerous innovative services and applications are being developed. Therefore, it becomes significant to investigate the factors influencing the intentions of mobile learning to be used among the students of higher education institution. This study examines the ‘‘Technology Acceptance Model’’ (TAM), ‘‘Theory of Reasoned Action’’ (TRA) and ‘‘Unified Theory of Acceptance and Use of Technology’’ (UTAUT). The study is based on a survey being conducted across diverse groups of stu- dents, belonging to different communities and universities. The survey questionnaire was utilized for collecting the relevant data from 300 respondents. The results analyzed yields the impact that the proposed model of mobile learning is comprehensive to study in Indonesia institutions of higher education.
The research does not intend to merely be a review of data and information quality software, but rather aims to identify limitations of such software solutions when used in conjunction with higher education information system applications. Despite its unique nature and role, physical asset in Indonesian higher education (HE) institutions is not considered as a core business activity by many HE institutions, which therefore depend on traditional institutional information sources to manage their assets. This paper places a special focus on data and information quality issues and on how software can assist in dealing with those various data and information quality issues. This study began with an analysis of the need to improve the asset management process mode. A close examination was conducted on each of the four selected data and information quality enhancing software packages. It evaluated how each package supports the individual data and information quality requirements in each asset management process.
This paper can be valuable to practitioners, researchers and software developers who are specializing in, studying, developing or adopting a computerized software solution for data and information quality in higher education institutions. Four main functions incorporated in the majority of data and information quality solutions were evaluated systematically including data profiling, data cleansing and matching, data enhancing, and data monitoring functions.