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All Journal Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Applied Engineering and Technological Science (JAETS)
Arda Yunianta
King Abdulaziz University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Adaboost-multilayer perceptron to predict the student’s performance in software engineering Ahmad Firdaus Zainal Abidin; Mohd Faaizie Darmawan; Mohd Zamri Osman; Shahid Anwar; Shahreen Kasim; Arda Yunianta; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (496.113 KB) | DOI: 10.11591/eei.v8i4.1432

Abstract

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.
Adaboost-multilayer perceptron to predict the student’s performance in software engineering Ahmad Firdaus Zainal Abidin; Mohd Faaizie Darmawan; Mohd Zamri Osman; Shahid Anwar; Shahreen Kasim; Arda Yunianta; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (496.113 KB) | DOI: 10.11591/eei.v8i4.1432

Abstract

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.
Adaboost-multilayer perceptron to predict the student’s performance in software engineering Ahmad Firdaus Zainal Abidin; Mohd Faaizie Darmawan; Mohd Zamri Osman; Shahid Anwar; Shahreen Kasim; Arda Yunianta; Tole Sutikno
Bulletin of Electrical Engineering and Informatics Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (496.113 KB) | DOI: 10.11591/eei.v8i4.1432

Abstract

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.
The Ontology-Based Methodology Phases To Develop Multi-Agent System (OmMAS) Arda Yunianta; Aina Musdholifah; Nataniel Dengen Haviluddin; Omar Obarukab Norazah Yusof; Herlina Jayadiyanti; Mohd Shahizan Othman
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.289 KB) | DOI: 10.11591/eecsi.v4.1027

Abstract

Semantic aspect on methodology phase is a significant  issue  to  develop  multi-agent  system in  the  current days.   There are a lot of methodologies to develop multi-agent system, but the current problem is how to choose the best methodology phase to develop current multi-agent system. The development of multi-agent system currently is to be more complex and difficult. Many aspects that contains on multi-agent system, the one of the famous issue now is about semantic aspect on multi-agent system. The old methodology phases are not suitable to develop current multi-agent system. Nowadays, many researchers start to improve and customize the obsolete methodology to adjust with the current needed. There are two research steps contains in this paper, the first step is to review and criticize previous methodologies especially about MOMA (Methodology for Developing Ontology-Based Multi-Agent System) was introduced in 2013. The second step is the main contribution of this paper is to improve previous methodology phases with the new methodology phases named OmMas (The Ontology-Based Methodology phases to Develop Multi-Agent System), and using semantic aspect as the  main focus of this methodology. The result of this research is improved ontology- based methodology phases as a representation of semantic aspect on the ontology development process. 
Analysis and Identification of Non-Impact Factors on Smart City Readiness Using Technology Acceptance Analysis: A Case Study in Kampar District, Indonesia M. Khairul Anam; Arda Yunianta; Hasan J. Alyamani; Erlin Erlin; Ahmad Zamsuri; Muhammad Bambang Firdaus
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.2401

Abstract

Most countries start to implement Smart Cities as an innovation for urban strategy. However, not all Smart Cities implementations worked and were implemented well, because the community still not ready for the implementation of Smart City. The aim of this research is to investigate community readiness and finding low impact factors for implementing smart cities based on 5 factors, namely AU, PEOU, ATU, BIU, and PU. This research was using a qualitative study with the Technology Acceptance Model approach (TAM) to investigate the relationship between 5 factors. Based on the results of data distribution, there are 2 clusters, namely people who know about public service applications and people who are not aware of any public service applications. Furthermore, there are 3 tests conducted in this research namely T-test, F-test and Coefficient Determination Test to determine the impact and influence of the relationship between each factor. However, from the results of the t-test it was found that there were 2 relationships that had no impact because the t-count was negative and the 2 relationships between these factors were between PU - AU and AU - PU.
Co-Authors Ahmad Firdaus Zainal Abidin Ahmad Zamsuri Aina Musdholifah Anam, M Khairul Erlin Hasan J. Alyamani Haviluddin Haviluddin Herlina Jayadiyanti Mohd Faaizie Darmawan Mohd Shahizan Othman Mohd Zamri Osman Muhammad Bambang Firdaus Omar Obarukab Norazah Yusof Shahid Anwar Shahreen Kasim Tole Sutikno