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Authors: Heru Mardiansyah ; Saib Suwilo ; Erna Nababan and Syahril Efendi

Affiliation: Universitas Sumatera Utara, Medan, Indonesia

Keyword(s): Knowledge Graph, Graph Convolutional Networks, Myers-Briggs Type Indicator.

Abstract: The order of vertices in a graph is very important because the graph is oriented. Or the vertices are not important because they are not oriented. The graph of data is a heterogeneous polydigraph, called a directed vertex set, with many edges between any two vertices. Information is created by establishing real-world relationships between graphics and objects. This study was conducted to improve machine learning performance by proposing a theoretical model for the data used and creating a graph convolutional network (GCN) model for training the data. Data are created from a low-dimensional (latent) space, but can only be observed in a high-dimensional (observation) space. The results of these studies may not always yield the same results because they were not measured on the same person, were unreliable, or the results obtained did not provide consistent results. MBTI tests may change at any time. It is obtained according to the result of a person’s mood. This MBTI method is often co nsidered weak and unscientific, so it must be tested with 200 iterations on the GCN. The resulting GCN scores are 89.8% accuracy and 2.78 Test Loss. (More)

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Paper citation in several formats:
Mardiansyah, H.; Suwilo, S.; Nababan, E. and Efendi, S. (2024). Graph Convolutional Networks with Knowledge Graph for Myers-Briggs Type Indicator. In Proceedings of the 3rd International Conference on Advanced Information Scientific Development - ICAISD; ISBN 978-989-758-678-1, SciTePress, pages 285-288. DOI: 10.5220/0012448500003848

@conference{icaisd24,
author={Heru Mardiansyah. and Saib Suwilo. and Erna Nababan. and Syahril Efendi.},
title={Graph Convolutional Networks with Knowledge Graph for Myers-Briggs Type Indicator},
booktitle={Proceedings of the 3rd International Conference on Advanced Information Scientific Development - ICAISD},
year={2024},
pages={285-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012448500003848},
isbn={978-989-758-678-1},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Information Scientific Development - ICAISD
TI - Graph Convolutional Networks with Knowledge Graph for Myers-Briggs Type Indicator
SN - 978-989-758-678-1
AU - Mardiansyah, H.
AU - Suwilo, S.
AU - Nababan, E.
AU - Efendi, S.
PY - 2024
SP - 285
EP - 288
DO - 10.5220/0012448500003848
PB - SciTePress

- Science and Technology Publications, Lda.
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