Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression
Temperature is a crucial element affecting various aspects, from agriculture to natural disasters. Temperature data imputation is also important because, in some cases, temperature data is not always complete. This study aims to predict missing temperature data in the East Nusa Tenggara (NTT) regio...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Islam Negeri Sunan Kalijaga Yogyakarta
2025-05-01
|
| Series: | JISKA (Jurnal Informatika Sunan Kalijaga) |
| Subjects: | |
| Online Access: | https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4497 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849735741035249664 |
|---|---|
| author | Isa Kholifatus Sukhna Brina Miftahurrohmah Catur Wulandari Putri Amelia |
| author_facet | Isa Kholifatus Sukhna Brina Miftahurrohmah Catur Wulandari Putri Amelia |
| author_sort | Isa Kholifatus Sukhna |
| collection | DOAJ |
| description |
Temperature is a crucial element affecting various aspects, from agriculture to natural disasters. Temperature data imputation is also important because, in some cases, temperature data is not always complete. This study aims to predict missing temperature data in the East Nusa Tenggara (NTT) region using the Support Vector Regression (SVR) method. The data used comes from six BMKG observation stations in NTT and ERA-5 Reanalysis data. The choice of the SVR method is based on its ability to handle data with complex structures. Modeling is conducted separately for each station using the Radial Basis Function (RBF) kernel. Model evaluation employs the metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), presenting the evaluation results with low error. The results show that among several parameter tests, the parameter ranges [C = 1, 5, 10, 15], [ε = 0,1, 0,3, 0,6, 0,9], and [γ = 1, 5, 10, 15] in the SVR method are the best parameter ranges across all stations. The prediction graphs display different temperature fluctuation patterns at each station. This study contributes to enhancing the availability of accurate climate data to support sustainable decision-making in the NTT region.
|
| format | Article |
| id | doaj-art-4d17478b34914f098a6878a4b58524d4 |
| institution | DOAJ |
| issn | 2527-5836 2528-0074 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Universitas Islam Negeri Sunan Kalijaga Yogyakarta |
| record_format | Article |
| series | JISKA (Jurnal Informatika Sunan Kalijaga) |
| spelling | doaj-art-4d17478b34914f098a6878a4b58524d42025-08-20T03:07:28ZengUniversitas Islam Negeri Sunan Kalijaga YogyakartaJISKA (Jurnal Informatika Sunan Kalijaga)2527-58362528-00742025-05-01102Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector RegressionIsa Kholifatus Sukhna0Brina Miftahurrohmah1Catur Wulandari2Putri Amelia3Universitas Internasional Semen IndonesiaUniversitas Internasional Semen IndonesiaUniversitas Internasional Semen IndonesiaUniversitas Internasional Semen Indonesia Temperature is a crucial element affecting various aspects, from agriculture to natural disasters. Temperature data imputation is also important because, in some cases, temperature data is not always complete. This study aims to predict missing temperature data in the East Nusa Tenggara (NTT) region using the Support Vector Regression (SVR) method. The data used comes from six BMKG observation stations in NTT and ERA-5 Reanalysis data. The choice of the SVR method is based on its ability to handle data with complex structures. Modeling is conducted separately for each station using the Radial Basis Function (RBF) kernel. Model evaluation employs the metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), presenting the evaluation results with low error. The results show that among several parameter tests, the parameter ranges [C = 1, 5, 10, 15], [ε = 0,1, 0,3, 0,6, 0,9], and [γ = 1, 5, 10, 15] in the SVR method are the best parameter ranges across all stations. The prediction graphs display different temperature fluctuation patterns at each station. This study contributes to enhancing the availability of accurate climate data to support sustainable decision-making in the NTT region. https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4497TemperatureSVRNTTReanalysis ERA-5RBF |
| spellingShingle | Isa Kholifatus Sukhna Brina Miftahurrohmah Catur Wulandari Putri Amelia Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression JISKA (Jurnal Informatika Sunan Kalijaga) Temperature SVR NTT Reanalysis ERA-5 RBF |
| title | Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression |
| title_full | Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression |
| title_fullStr | Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression |
| title_full_unstemmed | Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression |
| title_short | Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression |
| title_sort | imputasi data temperatur maksimum menggunakan metode support vector regression |
| topic | Temperature SVR NTT Reanalysis ERA-5 RBF |
| url | https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/4497 |
| work_keys_str_mv | AT isakholifatussukhna imputasidatatemperaturmaksimummenggunakanmetodesupportvectorregression AT brinamiftahurrohmah imputasidatatemperaturmaksimummenggunakanmetodesupportvectorregression AT caturwulandari imputasidatatemperaturmaksimummenggunakanmetodesupportvectorregression AT putriamelia imputasidatatemperaturmaksimummenggunakanmetodesupportvectorregression |