EFESIENSI ENERGI PADA BANGUNAN MENGGUNAKAN MULTIVARIATE RANDOM FOREST
Abstract
Energy is needed by humans. Energy utilization is often carried out in daily activities, such as helping with work, household activities to lighting both at home and on the road. Recently, there has been a lot of research on concerns about the waste of energy and its lasting adverse impact on the environment. Previous research conducted by Tsanas and Xifara in 2012 has carried out energy efficiency in buildings using Statistical Machine Learning. Their research focuses on calculating outcomes one by one, not directly on all outcomes. In this study using the Multivariate Random Forest method. Multivariate Random Forest has similarities compared to Random Forest, while the Multivariate Random Forest method is more used if more than one output is produced. Based on the tests that have been carried out, it can be concluded that the best parameter that gives maximum results is the number of trees as many as 200 with a data division of 60% training data and 40% testing data with RMSE results of 2.602036 and MSE result of 6.770589. Based on the tests that have been carried out, it proves that the more the number of trees does not prove that it can provide maximum results.
Keywords: Energy, Efficiency, Prediction, Multivariate Random Forest
Energi sangat dibutuhkan oleh manusia. Pemanfaatan energi sering dilakukan dalam kegiatan sehari-hari, seperti membantu pekerjaan, kegiatan rumah tangga hingga penerangan baik dalam rumah maupun di jalan. Akhir-akhir ini banyak penelitian tentang kekhawatiran mengenai pemborosan energi dan dampak buruknya yang abadi terhadap lingkungan. Penelitian sebelumnya yang dilakukan oleh Tsanas dan Xifara pada tahun 2012 telah melakukan efesiensi energi pada bangunan menggunakan Statistical Machine Learning. Penelitian mereka berfokus pada perhitungan luaran secara satu persatu, tidak secara langsung semua luaran. Pada penelitian ini menggunakan metode Multivariate Random Forest. Multivariate Random Forest memiliki kesamaan dibandingkan dengan Random Forest, sedangkan metode Multivariate Random Forest lebih digunakan jika luaran yang dihasilkan lebih dari satu. Berdasarkan pengujian yang sudah dilakukan, dapat disimpulkan bahwa parameter terbaik yang memberikan hasil maksimal yaitu pada jumlah pohon sebanyak 200 dengan pembagian data sebanyak 60% data latih dan 40% data uji dengan hasil RMSE sebesar 2.602036 dan MSE sebesar 6.770589. Berdasarkan pengujian yang sudah dilakukan membuktikan semakin banyak jumlah pohon tidak membuktikan bisa memberikan hasil yang maksimal.
Kata kunci: Energi, Efesiensi, Prediksi, Multivariate Random Forest
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DOI: http://dx.doi.org/10.20527/klik.v9i1.421
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