An Ensemble Convolutional Neural Network Approach for Image Classification of Indonesian Endemic Fruits

Widiasri, Monica and Siswantoro, Joko and Duanto, Alexander Kenrick (2026) An Ensemble Convolutional Neural Network Approach for Image Classification of Indonesian Endemic Fruits. In: The 2nd International Conference on Applied Sciences and Smart Technologies (InCASST 2025): E3S Web Conf. Volume 687, 2026, 15 Oktober 2025, Yogyakarta.

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Abstract

Indonesia has a diverse range of endemic fruits that grow in its various regions. These fruits have their own distinctive characteristics, which can sometimes lead to confusion in the sorting process. Classification can be used as a solution to this problem. Several similar studies have classified fruits; however, there has been no research specifically using deep learning methods for Indonesia's endemic fruits. The designed system is expected to classify fruits accurately based on their unique characteristics. The classification models used consist of three CNN architecture models: AlexNet, ResNet-50, and InceptionV3, which are then combined with an ensemble method. Each model is compared by evaluating the use of transfer learning and without it. The three models with the most optimal results are implemented in an ensemble application. The best results were obtained from the AlexNet model, with an accuracy of 99.67%, the InceptionV3 model, with an accuracy of 99.81%, and the ResNet-50 model, with an accuracy of 100%. All three models are implemented in an ensemble using the majority voting method. The results of the ensemble implementation yield an accuracy of 100% on the test dataset.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering > Department of Informatic
Depositing User:	Monica Widiasri 61151
Date Deposited:	23 Jan 2026 08:16
Last Modified:	23 Jan 2026 08:16
URI:	http://repository.ubaya.ac.id/id/eprint/50182

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