# Enhancing Wind Tunnel Data Classification Through Effective Data Preprocessing for Machine Learning Algorithm Modeling
> Purwadi P.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_85187550535
Jurnal / Konferensi: Proceedings International Conference on Smart Green Technology in Electrical and Information Systems Icsgteis
Tahun terbit: 2023
DOI: https://doi.org/10.1109/ICSGTEIS60500.2023.10424358
ISSN: 28313992
Citations: 1

## Authors
- Purwadi P.

## Abstract
Wind tunnel experiments provide valuable insights into the aerodynamics behavior of objects, enabling engineers and researchers to optimize designs and enhance performance. In one series of a wind tunnel test can be produced a huge of data. In relation of this number of data, furthermore, a classification have to be built to simplify in recognizing the type of the data resulted from the wind tunnel experiments. In recent years, machine learning techniques have shown great potential in analyzing wind tunnel data and extracting meaningful patterns. However, the preprocessing stage plays a critical role in preparing the data for machine learning modeling, ensuring accurate and reliable classification results. This research focuses on the preprocessing of wind tunnel data for machine learning modeling, specifically targeting classification tasks by doing noise reduction, missing data handling, feature scaling, and outlier detection.

## Keywords
- Computer science
- Data pre-processing
- Preprocessor
- Wind tunnel
- Machine learning
- Algorithm
- Artificial intelligence
- Statistical classification
- Data modeling
- Data mining
- Pattern recognition (psychology)
- Engineering
- Database
- Aerospace engineering

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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