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Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection

Satellites are critical components of modern infrastructure, supporting countless applications in communication, navigation, and observation. However, ensuring their functionality and safety within complex space environments can be challenging. The satellite experiences the highest loss in the s...

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Main Authors: Mutholib, Abdul, Abdul Rahim, Nadirah, Gunawan, Teddy Surya, Ahmarofi, Ahmad Afif
Format: Proceeding Paper
Language:English
Published: IEEE 2024
Subjects:
BPC175 Islam and engineering. Sustainable engineering. Sustainable building
T Technology (General)
T55.4 Industrial engineering.Management engineering.
TK Electrical engineering. Electronics Nuclear engineering
TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:http://irep.iium.edu.my/114532/7/114532_Performance%20comparison%20of%20data.pdf
http://irep.iium.edu.my/114532/
https://ieeexplore.ieee.org/document/10675571
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spelling my.iium.irep.1145322024-09-20T03:47:16Z http://irep.iium.edu.my/114532/ Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection Mutholib, Abdul Abdul Rahim, Nadirah Gunawan, Teddy Surya Ahmarofi, Ahmad Afif BPC175 Islam and engineering. Sustainable engineering. Sustainable building T Technology (General) T55.4 Industrial engineering.Management engineering. TK Electrical engineering. Electronics Nuclear engineering TK5101 Telecommunication. Including telegraphy, radio, radar, television Satellites are critical components of modern infrastructure, supporting countless applications in communication, navigation, and observation. However, ensuring their functionality and safety within complex space environments can be challenging. The satellite experiences the highest loss in the space industry caused by anomalies. Hence, it needs early detection so that the loss can be avoided immediately. With the advancement of technology, satellite anomalies diagnosis and detection can be done with trade-space exploration (TSE) and Artificial Intelligence (AI) models based on satellite data. The problem is that in satellite data preprocessing step, the data can be too large and sometimes there are some missing values encountered which leads to outliers. To mitigate these problems, efficient data preprocessing is needed so that the accuracy can be leveraged and requires only minimal computation resources. This paper presents the examination of the data preprocessing performance from the combination of both data cleansing and data normalization methods. Elimination, Imputation, Feature of Missing and Imperative Imputation methods are involved in data cleansing. While for the data normalization presented, Min Max, Z-Score using Standard Scalar, Robust Scaling, Vector Normalization and Power Transformation methods are used. As for the AI model classification, it is using Support Vector Machines (SVMs). The test was conducted using data from Satellite Database and Space Market Analysis (Seradata) consisting of approximately 4,455 data. The result shows that the accuracy of the Elimination and the Power Transformation normalization is the highest in training accuracy with 60%. While the Elimination and the Min Max or the Z-Score methods are the top in the testing accuracy with 60%. IEEE 2024-09-18 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/114532/7/114532_Performance%20comparison%20of%20data.pdf Mutholib, Abdul and Abdul Rahim, Nadirah and Gunawan, Teddy Surya and Ahmarofi, Ahmad Afif (2024) Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection. In: 2024 IEEE 10th International Conference on Smart Instrumentation, Measurement and Applications ( ICSIMA), 30-31 July 2024, Bandung, Indonesia. https://ieeexplore.ieee.org/document/10675571
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic BPC175 Islam and engineering. Sustainable engineering. Sustainable building
T Technology (General)
T55.4 Industrial engineering.Management engineering.
TK Electrical engineering. Electronics Nuclear engineering
TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle BPC175 Islam and engineering. Sustainable engineering. Sustainable building
T Technology (General)
T55.4 Industrial engineering.Management engineering.
TK Electrical engineering. Electronics Nuclear engineering
TK5101 Telecommunication. Including telegraphy, radio, radar, television
Mutholib, Abdul
Abdul Rahim, Nadirah
Gunawan, Teddy Surya
Ahmarofi, Ahmad Afif
Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection
description Satellites are critical components of modern infrastructure, supporting countless applications in communication, navigation, and observation. However, ensuring their functionality and safety within complex space environments can be challenging. The satellite experiences the highest loss in the space industry caused by anomalies. Hence, it needs early detection so that the loss can be avoided immediately. With the advancement of technology, satellite anomalies diagnosis and detection can be done with trade-space exploration (TSE) and Artificial Intelligence (AI) models based on satellite data. The problem is that in satellite data preprocessing step, the data can be too large and sometimes there are some missing values encountered which leads to outliers. To mitigate these problems, efficient data preprocessing is needed so that the accuracy can be leveraged and requires only minimal computation resources. This paper presents the examination of the data preprocessing performance from the combination of both data cleansing and data normalization methods. Elimination, Imputation, Feature of Missing and Imperative Imputation methods are involved in data cleansing. While for the data normalization presented, Min Max, Z-Score using Standard Scalar, Robust Scaling, Vector Normalization and Power Transformation methods are used. As for the AI model classification, it is using Support Vector Machines (SVMs). The test was conducted using data from Satellite Database and Space Market Analysis (Seradata) consisting of approximately 4,455 data. The result shows that the accuracy of the Elimination and the Power Transformation normalization is the highest in training accuracy with 60%. While the Elimination and the Min Max or the Z-Score methods are the top in the testing accuracy with 60%.
format Proceeding Paper
author Mutholib, Abdul
Abdul Rahim, Nadirah
Gunawan, Teddy Surya
Ahmarofi, Ahmad Afif
author_facet Mutholib, Abdul
Abdul Rahim, Nadirah
Gunawan, Teddy Surya
Ahmarofi, Ahmad Afif
author_sort Mutholib, Abdul
title Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection
title_short Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection
title_full Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection
title_fullStr Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection
title_full_unstemmed Performance comparison of data preprocessing methods for trade-space exploration with AI model: case study of satellite anomalies detection
title_sort performance comparison of data preprocessing methods for trade-space exploration with ai model: case study of satellite anomalies detection
publisher IEEE
publishDate 2024
url http://irep.iium.edu.my/114532/7/114532_Performance%20comparison%20of%20data.pdf
http://irep.iium.edu.my/114532/
https://ieeexplore.ieee.org/document/10675571
_version_ 1811679654298255360
score 13.211869

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