COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA
The performance of machine learning in analyzing time series data is being widely discussed. A new ensemble method Double Random Forest (DRF), which considers supervised learning currently developed. This method has been claimed to be able to improve the performance of Random Forest (RF) if the data...
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Universitas Pattimura
2023-06-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7738 |
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| author | Andika Putri Ratnasari Budi Susetyo Khairil Anwar Notodiputro |
| author_facet | Andika Putri Ratnasari Budi Susetyo Khairil Anwar Notodiputro |
| author_sort | Andika Putri Ratnasari |
| collection | DOAJ |
| description | The performance of machine learning in analyzing time series data is being widely discussed. A new ensemble method Double Random Forest (DRF), which considers supervised learning currently developed. This method has been claimed to be able to improve the performance of Random Forest (RF) if the data is under-fitting. Another machine learning method, Long Short-Term Memory Networks (LSTMs) have capability to analyze nonlinear data. Since the study compare both methods has not been existed in literature, it is interesting to compare the performance of both methods using Indonesian data, especially economic indicator data which have been found to be under-fitting, non-underfitting, and nonlinear data. The indicators used in this study are Export, Import, Official Reserves Asset, and Exchange Rate data. The results showed that overall, the LSTMs method outperforms DRF method in analyzing the data. |
| format | Article |
| id | doaj-art-163a90831bf54570b4ca807ae4fcf409 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-163a90831bf54570b4ca807ae4fcf4092025-08-20T03:37:33ZengUniversitas PattimuraBarekeng1978-72272615-30172023-06-011720757076610.30598/barekengvol17iss2pp0757-07667738COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATAAndika Putri Ratnasari0Budi Susetyo1Khairil Anwar Notodiputro2Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, IndonesiaThe performance of machine learning in analyzing time series data is being widely discussed. A new ensemble method Double Random Forest (DRF), which considers supervised learning currently developed. This method has been claimed to be able to improve the performance of Random Forest (RF) if the data is under-fitting. Another machine learning method, Long Short-Term Memory Networks (LSTMs) have capability to analyze nonlinear data. Since the study compare both methods has not been existed in literature, it is interesting to compare the performance of both methods using Indonesian data, especially economic indicator data which have been found to be under-fitting, non-underfitting, and nonlinear data. The indicators used in this study are Export, Import, Official Reserves Asset, and Exchange Rate data. The results showed that overall, the LSTMs method outperforms DRF method in analyzing the data.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7738drflstmsunder-fittingnon-underfittingnonlinear |
| spellingShingle | Andika Putri Ratnasari Budi Susetyo Khairil Anwar Notodiputro COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA Barekeng drf lstms under-fitting non-underfitting nonlinear |
| title | COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA |
| title_full | COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA |
| title_fullStr | COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA |
| title_full_unstemmed | COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA |
| title_short | COMPARISON OF DOUBLE RANDOM FOREST AND LONG SHORT-TERM MEMORY METHODS FOR ANALYZING ECONOMIC INDICATOR DATA |
| title_sort | comparison of double random forest and long short term memory methods for analyzing economic indicator data |
| topic | drf lstms under-fitting non-underfitting nonlinear |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7738 |
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