Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier
Weather conditions are one of the crucial factors that need attention. Changes in weather conditions significantly impact various activities. Weather condition changes are determined by numerous factors, often occurring within a relatively short period in the atmosphere, such as pressure, wind spee...
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| Format: | Article |
| Language: | Indonesian |
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Indonesian Society of Applied Science (ISAS)
2024-06-01
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| Series: | Journal of Applied Computer Science and Technology |
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| Online Access: | https://journal.isas.or.id/index.php/JACOST/article/view/741 |
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| author | Steven Joses Donata Yulvida Siti Rochimah |
| author_facet | Steven Joses Donata Yulvida Siti Rochimah |
| author_sort | Steven Joses |
| collection | DOAJ |
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Weather conditions are one of the crucial factors that need attention. Changes in weather conditions significantly impact various activities. Weather condition changes are determined by numerous factors, often occurring within a relatively short period in the atmosphere, such as pressure, wind speed, rainfall, temperature, and other atmospheric phenomena. Issues in weather forecasting arise due to several factors, namely the fluctuating atmospheric conditions. This research proposes the development of a weather forecasting model using the ensemble learning method approach. The weather data used consist of 33746 records with attributes used after preprocessing, namely Temperature, Dew Point, Humidity, Wind Speed, Wind Gust, Pressure, Precipitation, and Condition. Testing in this research employs several single-machine learning methods such as K-Nearest Neighbor (KNN), Logistic Regression, Random Forest, Naive Bayes, and Multi-Layer Perceptron. The Naive Bayes method using default parameters achieves a high accuracy of 99.00%. In the ensemble method, combinations of three methods exhibit excellent accuracy for all combinations. The best combination methods are found in the Soft Voting Classifier method (Random Forest, MLP, Naive Bayes), Soft Voting Classifier (Logistic Regression, MLP, Naive Bayes), and Soft Voting Classifier (Random Forest, KNN, Naive Bayes) with an accuracy of 99.03%.
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| format | Article |
| id | doaj-art-8eba3650b9f4482c8a67ec707f2945d7 |
| institution | Kabale University |
| issn | 2723-1453 |
| language | Indonesian |
| publishDate | 2024-06-01 |
| publisher | Indonesian Society of Applied Science (ISAS) |
| record_format | Article |
| series | Journal of Applied Computer Science and Technology |
| spelling | doaj-art-8eba3650b9f4482c8a67ec707f2945d72025-08-20T03:45:07ZindIndonesian Society of Applied Science (ISAS)Journal of Applied Computer Science and Technology2723-14532024-06-015110.52158/jacost.v5i1.741741Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting ClassifierSteven Joses0Donata Yulvida1Siti Rochimah2Universitas Widya Dharma PontianakInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh Nopember Weather conditions are one of the crucial factors that need attention. Changes in weather conditions significantly impact various activities. Weather condition changes are determined by numerous factors, often occurring within a relatively short period in the atmosphere, such as pressure, wind speed, rainfall, temperature, and other atmospheric phenomena. Issues in weather forecasting arise due to several factors, namely the fluctuating atmospheric conditions. This research proposes the development of a weather forecasting model using the ensemble learning method approach. The weather data used consist of 33746 records with attributes used after preprocessing, namely Temperature, Dew Point, Humidity, Wind Speed, Wind Gust, Pressure, Precipitation, and Condition. Testing in this research employs several single-machine learning methods such as K-Nearest Neighbor (KNN), Logistic Regression, Random Forest, Naive Bayes, and Multi-Layer Perceptron. The Naive Bayes method using default parameters achieves a high accuracy of 99.00%. In the ensemble method, combinations of three methods exhibit excellent accuracy for all combinations. The best combination methods are found in the Soft Voting Classifier method (Random Forest, MLP, Naive Bayes), Soft Voting Classifier (Logistic Regression, MLP, Naive Bayes), and Soft Voting Classifier (Random Forest, KNN, Naive Bayes) with an accuracy of 99.03%. https://journal.isas.or.id/index.php/JACOST/article/view/741weather predictionmachine learningensemble methodsoft voting classifier |
| spellingShingle | Steven Joses Donata Yulvida Siti Rochimah Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier Journal of Applied Computer Science and Technology weather prediction machine learning ensemble method soft voting classifier |
| title | Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier |
| title_full | Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier |
| title_fullStr | Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier |
| title_full_unstemmed | Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier |
| title_short | Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier |
| title_sort | pendekatan metode ensemble learning untuk prakiraan cuaca menggunakan soft voting classifier |
| topic | weather prediction machine learning ensemble method soft voting classifier |
| url | https://journal.isas.or.id/index.php/JACOST/article/view/741 |
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