Jayadianti, Herlina and Cahyadi, Tedy Agung and Amri, Nur Ali and Pitayandanu, Muhammad Fathurrahman (2020) METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW. JurnalTechno Intensif, 4 (2). pp. 48-53. ISSN : 1907-4964
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Abstract
Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak
dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang.
Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian
sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di
antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN),
Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network
(AEEMD-ANN), E-SVR-Artificial Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation
(BPNN), Adaptive Splines Threshold (ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel,
Autoregressive Integrated Moving Average (ARIMA), Feed Forward Neural Network (FFNN), Support Vector
Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), dan Artificial Neural Network-Fuzzy (ANNFuzzy). Hasil dari review menyimpulkan bahwa model Artificial Neural Network memiliki beberapa kelebihan
dibandingkan dengan metode yang lain, yakni ANN mampu memberikan hasil yang dapat mengenali pola-pola
dengan baik dan mudah dikembangkan menjadi bermacam-macam variasi sesuai dengan permasalahan maupun
parameter yang ada, sehingga ANN direkomendasikan untuk perhitungan prediksi hujan.
Kata kunci : machine learning, prediksi, Artificial Neural Network, curah hujan, akurasi
Abstract - Various kinds of research have been carried out to find accurate models to predict rainfall in various
fields, so the research that has been done previously was reviewed again to help the drainage process in mining
companies. The review is done by comparing the results of each model that has been conducted in several previous
studies. This research used quantitative methods. Models compared in this study include the Fuzzy model, Fast
Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN),
Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN), E-SVR -Artificial
Neural Network (E-SVR-ANN), Artificial Neural Network Backpropagation (BPNN), Adaptive Splines Threshold
(ASTAR), Seasonal First-Order Autoregressive (SAR), Gumbel, Autoregressive Integrated Moving Average
(ARIMA), Feed Forward Neural Network (FFNN), Support Vector Machine (SVM), Adaptive Neuro-Fuzzy
Inference System (ANFIS), and Artificial Neural Network-Fuzzy (ANN-Fuzzy). The results of the review concluded
that the Artificial Neural Network model has several advantages compared to other methods, namely ANN is able
to provide results that can recognize patterns well and easily developed into a variety of variations in accordance
with existing problems and parameters, so ANN is recommended for rain prediction calculation.
Keywords : machine learning, prediction, Artificial Neural Network, rainfall, accuracy
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | machine learning, prediction, Artificial Neural Network, rainfall, accuracy, machine learning, prediksi, Artificial Neural Network, curah hujan, akurasi |
| Subjek: | T Technology > TN Mining engineering. Metallurgy |
| Divisions: | x. Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
| Depositing User: | Dr. ST. MT Nur Ali Amri |
| Date Deposited: | 17 Nov 2021 15:30 |
| Last Modified: | 17 Nov 2021 15:30 |
| URI: | http://eprints.upnyk.ac.id/id/eprint/27094 |
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