Improving the Accuracy of Student Problem Identification Using Rule-Based Machine Learning

DOI

10.2991/assehr.k.201010.018How to use a DOI?

Keywords

student problems, information systems, rule-based classifier, machine learning

Abstract

Adolescence is a period of development that is prone to problems and often makes adolescents unable to control emotions. No exception to adolescents who are in high school education. Problems that do not need to be resolved immediately and will arise even greater problems later on. Many methods of solving students’ problems are carried out in conventional ways that require relatively takes time and costly. Therefore teacher career guidance and policy in schools use the problem list method provided for students. One thing that promises to improve accuracy with a short time to identify students’ problems by creating information systems using intelligent technology such as machine learning. Machine learning offers sophisticated techniques in creating automated schemes that can be used by students and guidance counseling teachers in technical issues is on the rise. This article discusses issues related to learners but also offers knowledge-based users (rules) that can be used by counseling guidance teachers to replace those who are behind information systems. The results of this study indicate that the information system developed which is based on rule-based machine learning offers a classification that is more accurate, faster, can be done anytime, anywhere and requires no cost compared to existing conventional methods.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Budi Sulistiyo
AU  - Bayu Surarso
AU  - Wahyul Amien Syafei
PY  - 2020
DA  - 2020/10/11
TI  - Improving the Accuracy of Student Problem Identification Using Rule-Based Machine Learning
BT  - Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019)
PB  - Atlantis Press
SP  - 116
EP  - 120
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201010.018
DO  - 10.2991/assehr.k.201010.018
ID  - Sulistiyo2020
ER  -