Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil
Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors e...
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| Format: | Article |
| Language: | English |
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Universitas Syiah Kuala
2022-09-01
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| Series: | Jurnal Rekayasa Elektrika |
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| Online Access: | https://jurnal.unsyiah.ac.id/JRE/article/view/25903 |
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| author | Ricky Afiful Maula Agus Indra Gunawan Bima Sena Bayu Dewantara M. Udin Harun Al Rasyid Setiawardhana Setiawardhana Ferry Astika Saputra Junaedi Ispianto |
| author_facet | Ricky Afiful Maula Agus Indra Gunawan Bima Sena Bayu Dewantara M. Udin Harun Al Rasyid Setiawardhana Setiawardhana Ferry Astika Saputra Junaedi Ispianto |
| author_sort | Ricky Afiful Maula |
| collection | DOAJ |
| description | Shrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734. |
| format | Article |
| id | doaj-art-6ae8883daa8a4ca0bbdb05e5e4e09893 |
| institution | OA Journals |
| issn | 1412-4785 2252-620X |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Universitas Syiah Kuala |
| record_format | Article |
| series | Jurnal Rekayasa Elektrika |
| spelling | doaj-art-6ae8883daa8a4ca0bbdb05e5e4e098932025-08-20T02:18:50ZengUniversitas Syiah KualaJurnal Rekayasa Elektrika1412-47852252-620X2022-09-0118310.17529/jre.v18i3.2590314547Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran KecilRicky Afiful Maula0Agus Indra Gunawan1Bima Sena Bayu Dewantara2M. Udin Harun Al Rasyid3Setiawardhana Setiawardhana4Ferry Astika Saputra5Junaedi Ispianto6Politeknik Elektronika Negeri SurabayaPoliteknik Elektronika Negeri SurabayaPoliteknik Elektronika Negeri SurabayaPoliteknik Elektronika Negeri SurabayaPoliteknik Elektronika Negeri SurabayaPoliteknik Elektronika Negeri SurabayaAsosiasi Tambak IntensifShrimp cultivation is strongly influenced by pond water quality conditions. Farmers must know the appropriate action in regulating water quality that is suitable for shrimp survival. The state of water quality can be understood by measuring pond parameters using various sensors. Installing sensors equipped with artificial intelligence modules to inform water quality conditions is the right action. However, the sensor cannot be separated from errors, so it results in not being able to get data or missing data. In this case, the approach of 5 parameters of pond water quality from 13 available parameters is carried out. This paper proposes a technique to obtain lost data caused by sensor error and looks for the best model. A simple approach can be taken, such as the Handling Missing Value (HMV), which is commonly used, namely the mean, with the K-Nearest Neighbors (KNN) classifier optimized using a grid search. However, the accuracy of this technique is still low, reaching 0.739 at 20-fold cross-validation. Calculations were carried out with other methods to further improve the prediction accuracy. It was found that Linear Regression (LR) can increase accuracy up to 0.757, which outperforms different approaches such as the statistical approach to mean 0.739, mode 0.716, median 0.734, and regression approach KNN 0.742, Lasso 0.751, Passive Aggressive Regressor (PAR) 0.737, Support Vector Regression (SVR) 0.739, Kernel Ridge (KR) 0.731, and Stochastic Gradient Descent (SGD) 0.734.https://jurnal.unsyiah.ac.id/JRE/article/view/25903handling missing valueiterative imputationalgoritma regresiakuakultur |
| spellingShingle | Ricky Afiful Maula Agus Indra Gunawan Bima Sena Bayu Dewantara M. Udin Harun Al Rasyid Setiawardhana Setiawardhana Ferry Astika Saputra Junaedi Ispianto Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil Jurnal Rekayasa Elektrika handling missing value iterative imputation algoritma regresi akuakultur |
| title | Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil |
| title_full | Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil |
| title_fullStr | Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil |
| title_full_unstemmed | Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil |
| title_short | Handling Missing Value dengan Pendekatan Regresi pada Dataset Akuakultur Berukuran Kecil |
| title_sort | handling missing value dengan pendekatan regresi pada dataset akuakultur berukuran kecil |
| topic | handling missing value iterative imputation algoritma regresi akuakultur |
| url | https://jurnal.unsyiah.ac.id/JRE/article/view/25903 |
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