Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-Debit
Fundamental weaknesses of the application of Tank Models is on so many parameters whose values should be set first before the model is simultaneously applied. This condition causes the Tank Models is considered inefficient to solve practical problems. This study is an attempt to improve the performa...
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| Format: | Article |
| Language: | English |
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Jurusan Teknik Industri, Fakultas Teknik Universitas Muhammadiyah Malang
2012-03-01
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| Series: | Jurnal Teknik Industri |
| Subjects: | |
| Online Access: | http://ejournal.umm.ac.id/index.php/industri/article/view/644 |
| _version_ | 1811294580324171776 |
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| author | Sulianto Sulianto Ernawan Setiono |
| author_facet | Sulianto Sulianto Ernawan Setiono |
| author_sort | Sulianto Sulianto |
| collection | DOAJ |
| description | Fundamental weaknesses of the application of Tank Models is on so many parameters whose values should be set first before the model is simultaneously applied. This condition causes the Tank Models is considered inefficient to solve practical problems. This study is an attempt to improve the performance of Tank Models can be applied to more practical and effective for the analysis of the data transformation of rainfall into river flow data. The discussion in this study focused on efforts to solve systems of equations Tank Models Series Composition, Parallel Composition and Combined Composition with the use of genetic algorithms in the optimization process parameters, so that the resulting system of equations to determine the optimal model parameter values are automatically in the studied watersheds. The results showed that the Wonorejo Watershed, Genetic Algorithm to solve the optimization process Tank Models parameter values as well. In the generation-150 showed the three models can achieve convergence with similar fitness values . Testing optimal parameter values by using the testing data sets show that the Tank Models Combined composition with Genetic Algorithm-based tend to be more consistent than the other two types of Tank Models. |
| first_indexed | 2024-04-13T05:19:40Z |
| format | Article |
| id | doaj.art-dc14109854dd4049a3ff8c005f1aa629 |
| institution | Directory Open Access Journal |
| issn | 1978-1431 2527-4112 |
| language | English |
| last_indexed | 2024-04-13T05:19:40Z |
| publishDate | 2012-03-01 |
| publisher | Jurusan Teknik Industri, Fakultas Teknik Universitas Muhammadiyah Malang |
| record_format | Article |
| series | Jurnal Teknik Industri |
| spelling | doaj.art-dc14109854dd4049a3ff8c005f1aa6292022-12-22T03:00:48ZengJurusan Teknik Industri, Fakultas Teknik Universitas Muhammadiyah MalangJurnal Teknik Industri1978-14312527-41122012-03-01131859210.22219/JTIUMM.Vol13.No1.85-92662Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-DebitSulianto SuliantoErnawan SetionoFundamental weaknesses of the application of Tank Models is on so many parameters whose values should be set first before the model is simultaneously applied. This condition causes the Tank Models is considered inefficient to solve practical problems. This study is an attempt to improve the performance of Tank Models can be applied to more practical and effective for the analysis of the data transformation of rainfall into river flow data. The discussion in this study focused on efforts to solve systems of equations Tank Models Series Composition, Parallel Composition and Combined Composition with the use of genetic algorithms in the optimization process parameters, so that the resulting system of equations to determine the optimal model parameter values are automatically in the studied watersheds. The results showed that the Wonorejo Watershed, Genetic Algorithm to solve the optimization process Tank Models parameter values as well. In the generation-150 showed the three models can achieve convergence with similar fitness values . Testing optimal parameter values by using the testing data sets show that the Tank Models Combined composition with Genetic Algorithm-based tend to be more consistent than the other two types of Tank Models.http://ejournal.umm.ac.id/index.php/industri/article/view/644genetic algorithms, combined, parallel, series, tank model |
| spellingShingle | Sulianto Sulianto Ernawan Setiono Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-Debit Jurnal Teknik Industri genetic algorithms, combined, parallel, series, tank model |
| title | Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-Debit |
| title_full | Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-Debit |
| title_fullStr | Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-Debit |
| title_full_unstemmed | Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-Debit |
| title_short | Algoritma Genetik Untuk Optimasi Parameter Model Tangki Pada Analisis Transformasi Data Hujan-Debit |
| title_sort | algoritma genetik untuk optimasi parameter model tangki pada analisis transformasi data hujan debit |
| topic | genetic algorithms, combined, parallel, series, tank model |
| url | http://ejournal.umm.ac.id/index.php/industri/article/view/644 |
| work_keys_str_mv | AT suliantosulianto algoritmagenetikuntukoptimasiparametermodeltangkipadaanalisistransformasidatahujandebit AT ernawansetiono algoritmagenetikuntukoptimasiparametermodeltangkipadaanalisistransformasidatahujandebit |