Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology
- Yulia Harni Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Iis Afrianty
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Suwanto Sanjaya Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Rahmad Abdillah Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Febi Yanto Indonesia
- Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.
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Copyright (c) 2023 Yulia Harni, Iis Afrianty, Suwanto Sanjaya, Rahmad Abdillah, Febi Yanto, Fadhilah Syafria
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