Antomy David Ronaldo
Unknown Affiliation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

Effective Soil Type Classification Using Convolutional Neural Network Antomy David Ronaldo
International Journal of Informatics and Computation Vol 3 No 1 (2021): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v3i1.33

Abstract

Soil classification is a growing research area in the current era. Various studies have proposed different techniques to deal with the issues, including rule-based, statistical, and traditional learning methods. However, the plans remain drawbacks to producing an accurate classification result. Therefore, we propose a novel technique to address soil classification by implementing a deep learning algorithm to construct an effective model. Based on the experiment result, the proposed model can obtain classification results with an accuracy rate of 97% and a loss of 0.1606. Furthermore, we also received an F1-score of 98%.