A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru
Data preprocessing plays a crucial role in enhancing the performance of machine learning algorithms for classification tasks. Among the essential preprocessing stages is data normalization, which aims to standardize data into a comparable range of values. This study focuses on normalizing rain...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1916/ http://eprints.intimal.edu.my/1916/1/jods2024_04.pdf |
| _version_ | 1848766870634627072 |
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| author | Rahmad Ramadhan, Laska Anne Mudya, Yolanda |
| author_facet | Rahmad Ramadhan, Laska Anne Mudya, Yolanda |
| author_sort | Rahmad Ramadhan, Laska |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Data preprocessing plays a crucial role in enhancing the performance of machine learning
algorithms for classification tasks. Among the essential preprocessing stages is data normalization,
which aims to standardize data into a comparable range of values. This study focuses on
normalizing rainfall data in Pekanbaru from 2019 to 2023. The objective is to compare various
data normalization techniques, including Min-Max Normalization and Z-Score Normalization.
The comparison of these particular strategies is justified because they are widely applied and have
different approaches. Min-max normalization is an easy-to-implement technique that makes the
data sensitive to outliers by scaling it to a specific range, often from 0 to 1. However, Z-Score
Normalization, sometimes referred to as Standardization, standardizes the data by dividing by the
standard deviation and subtracting the mean, maintaining the shape of the distribution and making
it resistant to outliers. The findings demonstrate that applying normalization techniques effectively
enhances classification performance compared to using unnormalized data. Specifically, the
optimal classification performance is achieved through Z-Score Normalization, yielding accuracy,
sensitivity, and specificity rates of 74.59%, 82.48%, and 63.92%, respectively. |
| first_indexed | 2025-11-14T11:58:01Z |
| format | Article |
| id | intimal-1916 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:01Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-19162024-05-07T01:33:57Z http://eprints.intimal.edu.my/1916/ A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru Rahmad Ramadhan, Laska Anne Mudya, Yolanda Q Science (General) QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software Data preprocessing plays a crucial role in enhancing the performance of machine learning algorithms for classification tasks. Among the essential preprocessing stages is data normalization, which aims to standardize data into a comparable range of values. This study focuses on normalizing rainfall data in Pekanbaru from 2019 to 2023. The objective is to compare various data normalization techniques, including Min-Max Normalization and Z-Score Normalization. The comparison of these particular strategies is justified because they are widely applied and have different approaches. Min-max normalization is an easy-to-implement technique that makes the data sensitive to outliers by scaling it to a specific range, often from 0 to 1. However, Z-Score Normalization, sometimes referred to as Standardization, standardizes the data by dividing by the standard deviation and subtracting the mean, maintaining the shape of the distribution and making it resistant to outliers. The findings demonstrate that applying normalization techniques effectively enhances classification performance compared to using unnormalized data. Specifically, the optimal classification performance is achieved through Z-Score Normalization, yielding accuracy, sensitivity, and specificity rates of 74.59%, 82.48%, and 63.92%, respectively. INTI International University 2024-05-07 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1916/1/jods2024_04.pdf Rahmad Ramadhan, Laska and Anne Mudya, Yolanda (2024) A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru. Journal of Data Science, 2024 (04). pp. 1-8. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | Q Science (General) QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software Rahmad Ramadhan, Laska Anne Mudya, Yolanda A Comparative Study of Z-Score and Min-Max Normalization for Rainfall Classification in Pekanbaru |
| title | A Comparative Study of Z-Score and Min-Max Normalization for Rainfall
Classification in Pekanbaru |
| title_full | A Comparative Study of Z-Score and Min-Max Normalization for Rainfall
Classification in Pekanbaru |
| title_fullStr | A Comparative Study of Z-Score and Min-Max Normalization for Rainfall
Classification in Pekanbaru |
| title_full_unstemmed | A Comparative Study of Z-Score and Min-Max Normalization for Rainfall
Classification in Pekanbaru |
| title_short | A Comparative Study of Z-Score and Min-Max Normalization for Rainfall
Classification in Pekanbaru |
| title_sort | comparative study of z-score and min-max normalization for rainfall
classification in pekanbaru |
| topic | Q Science (General) QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1916/ http://eprints.intimal.edu.my/1916/ http://eprints.intimal.edu.my/1916/1/jods2024_04.pdf |