Technological advances and developments in the Internet of Things (IoT) have made many people and companies aware of using artificial intelligence as a tool to speed up work processes. Deep learning which is a part of artificial intelligence is an important application in the application of Convolutional Neural Network (CNN) for image classification and detection. Convolutional Neural Network (CNN) is an innovation in the development of Multilayer Perceptron (MLP) in image processing. This research aims to conduct a preliminary study on the application of the Convolutional Neural Network (CNN) to obtain a corrosion classification based on the severity of the area on the ship's structure and the appropriate Convolutional Neural Network (CNN) architecture to detect and classify corrosion based on the detection error value. The results of the preliminary study of the Convolutional Neural Network (CNN) application on the ship structure, from 127 images obtained the highest number of labels is pitting corrosion, then general corrosion and the least is edge corrosion. The program design at the preliminary study stage is already able to detect corrosion with 3 categories but still has a low accuracy value. Where the test evaluation has an average accuracy of 0.3 and an average recall of 0.5. This is due to the low amount of data used as input for training and testing. Therefore, in the next stage, it is necessary to increase the number of data samples as input in the Convolutional Neural Network (CNN) process.

Original languageEnglish
Title of host publication3rd International Conference on Engineering, Technology and Innovative Researches
EditorsYogiek Indra Kurniawan, Ari Fadli, Dani Nugroho Saputro, Probo Hardini, Maulana Rizkia Aditama, Amanda Sofiana, Ayu Anggraeni Sibarani
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735442986
Publication statusPublished - 21 Feb 2023
Event3rd International Conference on Engineering, Technology and Innovative Researches, ICETIR 2021 - Purbalingga, Virtual, Indonesia
Duration: 1 Sept 2021 → …

Publication series

NameAIP Conference Proceedings
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616


Conference3rd International Conference on Engineering, Technology and Innovative Researches, ICETIR 2021
CityPurbalingga, Virtual
Period1/09/21 → …


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