TY - GEN
T1 - Categorization of Learning Materials Using Multilabel Classification
AU - Alfiani, Fadilla Sukma
AU - Imamah,
AU - Yuhana, Umi Laili
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/14
Y1 - 2021/9/14
N2 - Adaptive learning can adjust learning materials based on students' individual abilities. To facilitate the selection of appropriate materials, the categorization of learning materials can be done first. This study aims to categorize learning materials based on topics and subtopics with multilabel classification. Multilabel problem is handled by problem transformation approach. The problem transformation methods used are Binary Relevance, Label Powerset, and Classifier Chain. While the classification algorithms are Naive Bayes, SVM, and Random Forest. The dataset used in this study is 448 learning materials which are science subject materials that include biology, physics, and chemistry for junior high school students. The evaluation results show that the best combination is achieved by Binary Relevance method and SVM algorithm with accuracy value of 0.966 for topics and 0.699 for subtopics.
AB - Adaptive learning can adjust learning materials based on students' individual abilities. To facilitate the selection of appropriate materials, the categorization of learning materials can be done first. This study aims to categorize learning materials based on topics and subtopics with multilabel classification. Multilabel problem is handled by problem transformation approach. The problem transformation methods used are Binary Relevance, Label Powerset, and Classifier Chain. While the classification algorithms are Naive Bayes, SVM, and Random Forest. The dataset used in this study is 448 learning materials which are science subject materials that include biology, physics, and chemistry for junior high school students. The evaluation results show that the best combination is achieved by Binary Relevance method and SVM algorithm with accuracy value of 0.966 for topics and 0.699 for subtopics.
KW - learning materials
KW - multilabel classification
KW - text processing
UR - http://www.scopus.com/inward/record.url?scp=85119101196&partnerID=8YFLogxK
U2 - 10.1109/IEIT53149.2021.9587387
DO - 10.1109/IEIT53149.2021.9587387
M3 - Conference contribution
AN - SCOPUS:85119101196
T3 - Proceedings - IEIT 2021: 1st International Conference on Electrical and Information Technology
SP - 167
EP - 171
BT - Proceedings - IEIT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Electrical and Information Technology, IEIT 2021
Y2 - 14 September 2021 through 15 September 2021
ER -