Article Per Year (5 Year)
p-Index From 2019 - 2024
0.517
P-Index
This Author published in this journals
All Journal Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics
Budi Laksono Putro
Bandung Institute of Technology
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Development of online learning groups based on MBTI learning style and fuzzy algorithm Budi Laksono Putro; Yusep Rosmansyah; Suhardi Suhardi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.14922

Abstract

Group development is an initial step and an important influence on learning collaborative problem solving (CPS) based on the digital learning environment (DLE). Group development based on the Myers-Briggs types indicators (MBTI) rule proved successful for the educational and industrial environment. The MBTI ideal group rules are reached when a group leader has the highest level of leadership and compatibility between group members. The level of leadership and suitability of group members is determined based on the MBTI learning style (LS). Problems arise when the population of MBTI LS with the highest level of leadership is over. This will lead to dual leadership problems and have an impact on group disharmony. This study proposes an intelligent agent software for the development of the ideal group of MBTI, using the Fuzzy algorithm. The intelligent agent was developed on the SKACI platform. SKACI is a DLE for CPS learning. Fuzzy algorithm for solving dual leadership problems in a group. Fuzzy algorithm is used to increase the population of MBTI LS to 3 levels, namely low, medium and high. Increasing the population of MBTI LS can increase the probability of forming an ideal group of MBTI. Intelligent agents are tested based on a quantitative analysis between experimental classes (applying intelligent agents), and control classes (without intelligent agents). Experiment results show an increase in performance and productivity is better in the experimental class than in the control class. It was concluded that the development of intelligent agents had a positive impact on group development based on the MBTI LS.
An intelligent agent model for learning group development in the digital learning environment: A systematic literature review Budi Laksono Putro; Yusep Rosmansyah; Suhardi Suhardi
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (375.522 KB) | DOI: 10.11591/eei.v9i3.2009

Abstract

Group development is the first and most important step for the success of collaborative problem solving (CPS) learning in the digital learning environment (DLE). A literacy study is needed for studies in the intelligent agent domain for group development of collaborative learning in DLE. This paper is a systematic literature review (SLR) of intelligent agents for group formation from 2001 to 2019. This paper aims to find answers to 4 (four) research questions, namely: 1) What components to develop intelligent agents for group development; 2) What is the intelligent agent model for group development; 3) How are the metrics for measuring intelligent agent performance; and 4) How is the Framework for developing intelligent agent. The components of the intelligent agent model consist of: member attributes, group attributes (group constraints), and intelligent techniques. This research refers to Srba and Bielikova's group development model. The stages of the model are formation, performing and closing. An intelligent agent model at the formation stage. A performance metric for the intelligent agent at the performance stage. The framework for developing an intelligent agent is a reference to the stages of development, component selection techniques, and performance measurement of an intelligent agent.
Co-Authors Suhardi Suhardi Yusep Rosmansyah