Metode Komparasi Artificial Neural Network Pada Prediksi Curah Hujan - Literature Review

Jayadianti, Herlina; Cahyadi, Tedy Agung; Amri, Nur Ali; Pitayandanu, Muhammad Fathurrahman

doi:10.36787/jti.v14i2.150

Cited by 13 publications

(10 citation statements)

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“…Penggunaan algoritma Long Short Term Memory (LSTM) pada penelitian ini sangatlah penting dalam letak deret waktu, dengan penggunaan LSTM ini model mengekstrak fitur abstrak dari urutan nilai sensor dari data dari pada teknik secara manual dan hal ini sangatlah membantu pada proses prediksi [12]. Proses prediksi dilakukan setelah proses pre-processing dengan menggunakan algoritma perhitungan Long Short Term Memory (LSTM), dalam algoritma LSTM terdapat 3 gate yaitu input gate, forget gate, dan output gate seperti pada rumus berikut.…”

Section: Prediksi Dengan Lstmunclassified

Prediksi Curah Hujan Menggunakan Long Short Term Memory

Badriyah

Fariza

Harsono

2022

mib

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The importance of predicting rainfall in fields that require rainfall information such as in agriculture, transportation and industry. Prediction of rainfall with statistics is done to solve the problems of this paper, thus this paper proposes prediction of rainfall using Long Short Term Memory in the case study: Surabaya City. The data used is rainfall data at two Surabaya stations, namely the Perak Meteorological Station I and the Tanjung Perak Maritime Meteorology Station from 2015 to 2020. The prediction test was carried out using the Long Short Term Memory algorithm with accuracy measurement results MSE 0.489, MAE 0.537 and R2 0.497. from these results prove that the Long Short Term Memory algorithm is better than previous studies.

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Section: Prediksi Dengan Lstmunclassified

Prediksi Curah Hujan Menggunakan Long Short Term Memory

Badriyah

Fariza

Harsono

2022

mib

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“…Similarly, according to [8] research on a review of the methods used in rainfall research, which was carried out by comparing the results of studies using 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 state that the Artificial Neural Network model is superior to other methods in recognizing patterns well and is easier to develop according to existing problems and parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Based on these studies, in this study, the effect of using the number of factors and data on the weather prediction process using the Artificial Neural Network classification method will be studied. This method was chosen because based on [6], [7] research, ANN is a recommended method for studies related to wind speed and rainfall and produces a high level of accuracy [7], [8]. In this study, there is an additional weather factor, namely rainfall in the previous month.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Shofura

Suryani

Salma

et al. 2021

ijoict

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Current weather-related research only focuses on weather prediction based on raw data and the factors used are generally 4 factors: average temperature, solar radiation, air pressure, and wind. In this research, monthly weather prediction is done using 5 factors where the additional factor used is rainfall in the previous time. In contrast to previous prediction research, the prediction process carried out in this study emphasizes the modeling of training data according to the desired prediction model.. These two things distinguish this research from previous studies. The prediction model used in this study is a classification-based prediction model that is the Artificial Neural Network (ANN) method combined with the backpropagation algorithm for calculating the weight of the ANN network. The data used are meteorological data from 2010 to 2018 in the Bogor area, where data from 2010 to 2016 are used as training data, and data from 2017 to 2018 are used as test data. The results of this study indicate that the design of the model with the use of data for 6 years with feature data of 5 factors has an accuracy rate of 83.33%.

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“…Tahapan PenelitianBerdasarkan Gambar 1. Mengenai tahapan penelitian di atas, dijelaskan bahwa alur penelitian dibagi menjadi 3 tahapan[13][14][15], yaitu:…”

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Prediksi Hasil Belajar Hybrid Menggunakan Artificial Neural Network Dengan Multilayer Perceptron

Anwar

Kurnia²,

Faqih³

et al. 2022

Jur. Ris. Kom.

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The implementation of online distance learning affects the psychosocial aspects of students so that a combination of offline and face-to-face learning is carried out using Hybrid Learning. The problem in this research is the analysis of the competencies that will be generated, student characteristics, face-to-face interactions, online learning strategies, and lecturers by calculating the data from the Hybrid Learning evaluation to get the best level of accuracy using the Machine Learning technique method. The purpose of this study is to evaluate the results of Hybrid learning using the Artificial Neural Network (ANN) method which is expected to have the right level of accuracy and prediction and tolerate errors so that it can produce good predictions and can be used to model good relationships in finding patterns in the data. . Prediction of Hybrid Learning learning outcomes during the Covid 19 pandemic using Machine Learning techniques consisting of 12 attributes with a total of 1,231 datasets of Hybrid Learning learning outcomes in 2022. The Artificial Neural Network algorithm model uses Retrive operators, Set Roles, optimization parameters, Cross Validation, Apply Models, Performance and Logs. Accuracy results show 99.35% meaning that the results of the learning evaluation using Hybrid Learning with predictions that match and turn out to be appropriate are 1039, then those that do not match are 8. The predictions are very suitable and turn out to be very suitable in 104. Artificial Neural Network with Multilayer Perceptron, with two hidden layer and the format of the first hidden layer is 2 nodes, then the second hidden layer is 5 nodes with an output of 3 nodes

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Metode Komparasi Artificial Neural Network Pada Prediksi Curah Hujan - Literature Review

Cited by 13 publications

References 0 publications

Prediksi Curah Hujan Menggunakan Long Short Term Memory

Prediksi Curah Hujan Menggunakan Long Short Term Memory

The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Prediksi Hasil Belajar Hybrid Menggunakan Artificial Neural Network Dengan Multilayer Perceptron

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