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All Journal International Journal of Electrical and Computer Engineering Prosiding International Conference on Sustainable Innovation (ICoSI)
Syed Ahmad, Sharifah Sakinah
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Tiny datablock in saving Hadoop distributed file system wasted memory Al-Masadeh, Mohammad Bahjat; Azmi, Mohad Sanusi; Syed Ahmad, Sharifah Sakinah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1757-1772

Abstract

Hadoop distributed file system (HDFS) is the file system whereby Hadoop is use it to store all the upcoming data inside it. Since it been declared, HDFS is consuming a huge memory amount in order to serve a normal dataset. Nonetheless, the current file saving mechanism in HDFS save only one file in one datablock. Thus, a file with just 5 Mb in size will take up the whole datablock capacity causing the rest of the memory unavailable for other upcoming files, and this is considered a huge waste of memory in serving a normal size dataset. This paper proposed a method called tiny datablock-HDFS (TD-HDFS) to increase the usability of HDFS memory and increase the file hosting capabilities by reducing the datablock size to the minimum capacity, and then merging all the related datablocks into one master datablock. This master datablock consists of tiny virtual datablocks that contain the related small files together; will exploit the full memory of the master datablock. The result of this study is a running HDFS with a minimum amount of wasted memory with the same read/write data performance. The results were examined through a comparison between the standard HDFS file hosting and the proposed solution of this study.
How University Students Regulate AI Prompting Behavior Across Varying Levels of Learning Difficulty Purbohadi, Dwijoko; Syed Ahmad, Sharifah Sakinah
Proceedings International Conference on Sustainable Innovation (ICoSI) Vol. 6 No. 1 (2026)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/icosi.v6i1.154

Abstract

The integration of generative artificial intelligence into higher education has significantly impacted how students engage in learning activities, especially in programming courses. This study investigates how students regulate their AI interaction behavior across different levels of learning difficulty. We collected data from 183 undergraduate students enrolled in the Java Programming course, distributed across six classes. This study analyzes the frequency of prompting, behavioral change during the difficult and easy learning phases, and the primary purpose of chatbot use. The findings show that most students actively adapt their interactions with generative AI based on their perception of the level of learning difficulty. 78 percent of students increase the frequency of prompting when facing challenging tasks, while 69 percent reduce interaction after reaching conceptual understanding. Code generation is identified as the primary purpose of using chatbots, followed by debugging and conceptual clarification. This pattern shows that generative AI serves as a flexible cognitive support tool that students strategically use to manage learning challenges. This research indicates that adaptive prompting behavior represents a new form of self regulated learning in AI supported education environments. Instead of relying on chatbots continuously, students use them selectively to support problem solving and conceptual understanding. This study contributes to the growing body of research on artificial intelligence in education by highlighting behavioral adaptations in students’ interactions with generative AI systems and their implications for programming education in higher education
Co-Authors Al-Masadeh, Mohammad Bahjat Azmi, Mohad Sanusi Dwijoko Purbohadi