@inproceedings{ae2355e3265e44699e802b66a1256f92,
title = "Hyperspectral and Deep Learning-based Regression Model to Estimate Moisture Content in Sea Cucumbers",
abstract = "The hyperspectral image technology contains information in spectral and spatial forms that produce a huge amount of data. This data becomes an additional load while data is processed. Deep learning is the latest method capable of processing large-scale data with a deep structure of artificial neural network (ANN) and improving the model performance of data analysis. Therefore, this study aims to get a deep learning model into hyperspectral image processing for quantitative measurements of moisture content in dried sea cucumbers study case. The sea cucumber used in this study is the dried sea cucumber (Holothuria scabra), commonly known as Beche-de-mer. This study used the 400-1000 nm wavelength range to measure the moisture content quickly and nondestructively. The proposed model is deep learning which is used to build a predictive model system for moisture content in dried sea cucumbers. The coefficient of determination and the root means square error evaluate the measurement system. The measurement results of moisture content, the coefficient of determination, and the root mean square error values for training data are 0.99 and 0.11%, while testing data are 0.92 and 0.29%. ",
keywords = "Deep learning, Dried sea cucumber, Hyperspectral, Moisture content, Proposed CNN",
author = "Yuwono, {Hendra Angga} and Saputro, {Adhi Harmoko} and Sabar",
note = "Funding Information: ACKNOWLEDGMENT Penelitian Dasar Unggulan Perguruan Tinggi Grant 2021 has supported the research work reported here. The authors gratefully acknowledge the financial support provided by the Ministry of Education, Culture, Research, and Technology, Republic of Indonesia. Publisher Copyright: {\textcopyright} 2021 Institute of Advanced Engineering and Science (IAES).; 8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021 ; Conference date: 20-10-2021 Through 21-10-2021",
year = "2021",
doi = "10.23919/EECSI53397.2021.9624258",
language = "English",
series = "International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "283--287",
editor = "Auzani Jiddin and M Amjad and Subroto, {Imam MI} and Mochammad Facta",
booktitle = "Proceedings - 8th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2021",
address = "United States",
}