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Submitted
Jul 24, 2025
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Sep 2, 2025
Published
Sep 2, 2025
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Abstract

This study aims to classify two types of Sumba horses, namely the Sandelwood horse and the Peranakan Luar horse, using the Convolutional Neural Network (CNN) method. The classification process was carried out by implementing two CNN architectures, namely MobileNetV2 and ResNet50, which were tested to compare their performance. The dataset used consisted of 600 images, which went through preprocessing stages including cropping, normalization, and augmentation to improve data quality and model generalization capabilities. The test results showed that MobileNetV2 provided the best performance with 94% accuracy, 100% precision for the Peranakan class, and 100% recall for the Sandelwood class. In contrast, ResNet50 only achieved 65% accuracy, with low training stability. Analysis of accuracy and loss graphs indicated overfitting in MobileNetV2 due to limited data amount. Based on these findings, MobileNetV2 is recommended as a more efficient architecture for limited datasets. For further development, the use of additional data augmentation techniques, regularization, and early stopping is recommended to improve the model's generalization capabilities.


Keywords: CNN, MobileNetV2, ResNet50, Image Classification, Sumba Horse

Keywords

Kuda Sumba;Convolutional Neural Network;Klasifikasi Citra;Deep Learning;MobileNetV2; Identifikasi Hewan

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to Cite
bura, audyelumbu, Pahlevi Putra, R. ., & Rahman, A. Y. (2025). CLASSIFICATION OF SUMBA HORSE TYPES USING THE CONVULATION NEURAL NETWORK (CNN) METHOD. Edutran Computer Science and Information Technology, 2(1), 30-41. https://doi.org/10.59805/ecsit.v2i1.143

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