Penerapan Metode CNN-LSTM Dalam Memprediksi Hujan Pada Wilayah Medan

 (*)Mhd. Alfandi Mail (Universitas Budi Darma, Medan, Indonesia)
 Pristiwanto Pristiwanto (Universitas Budi Darma, Medan, Indonesia)
 A. M. Hatuaon Sihite (Universitas Budi Darma, Medan, Indonesia)

(*) Corresponding Author

Abstract

The causative factor of rain can occur due to the air temperature in an area or also due to the volume of water carried by the clouds. Tomorrow's weather conditions are needed to draw up various plans. The people of medan city with the work of the majority of employees and traders need information about rainfall. For the past, rainfall forecasts depended heavily on the month, there was a dry season and a rainy season. But nowadays, rainfall is increasingly difficult to predict, so a model or system is needed that can accurately predict rainfall. In this study, it was explained about rainfall prediction using one of the ANN models to predict future rainfall called CNN-LSTM. CNN-LSTM is an artificial neural network system specifically designed to handle long-term time series data such as rainfall. In its architecture, the CNN-LSTM model uses 2 LSTM Hidden Layers consisting of 108 LSTM neurons in each layer. The activation function used is Tanh. The loss function used is Mean Square Error. The result obtained is a model that can better predict rainfall if the input data given to the model is getting longer which is marked by a smaller Root Mean Square Error value.

Keywords


Rain; Predictions; CNN, LSTM; CNN-LSTM

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References

M. Febriantoro, “Prediksi Curah Hujan Menggunakan Recurrent Neural Network-Long Short Term Memory: Studi Kasus Di Stasiun Bmkg Karangploso, Malang.” Universitas Brawijaya, 2018.

F. Novadiwanti, A. Buono, And A. Faqih, “Prediksi Awal Musim Hujan Di Kabupaten Pacitan Menggunakan Optimasi Cascade Neural Network (Cnn) Dengan Genetic Algorithm (Ga) Berdasarkan Data Gcm,” J. Tanah Dan Iklim, Vol. 41, No. 1, Pp. 69–77, 2017.

R. S. Budi, R. Patmasari, And S. Saidah, “Klasifikasi Cuaca Menggunakan Metode Convolutional Neural Network,” Eproceedings Eng., Vol. 8, No. 5, 2021.

N. Ritha, M. Bettiza, And A. Dufan, “Prediksi Curah Hujan Dengan Menggunakan Algoritma Levenberg-Marquardt Dan Backpropagation,” J. Sustain. J. Has. Penelit. Dan Ind. Terap., Vol. 5, No. 2, Pp. 11–16, 2016.

A. I. Hrp, J. R. Lubis, I. Ferianto, S. Djasmayena, S. Dewi, And R. Aritonang, “Analisis Penerapan Jaringan Saraf Tiruan Untuk Mendeteksi Gangguan Psikologi Pada Manusia Menggunakan Metode Backpropagation,” J. Susi, Vol. 1, No. 02, Pp. 1–4, 2021.

P. N. U. Rahayu, “Peramalan Curah Hujan Di Kota Semarang Dengan Metode Hybrid Seasonal Autoregressive Integrated Moving Average (Sarima) Adaptive Neuro Fuzzy Inference System (Anfis).” Muhammadiyah University, Semarang, 2020.

M. Yanto, “Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Pada Pola Penentuan Nilai Status Kelulusan Sidang Skripsi,” J. Teknoif Itp, Vol. 5, No. 2, Pp. 79–87, 2017.

Hernadewita, Y. K. Hadi, M. J. Syaputra, And D. Setiawan, “Peramalan Penjualan Obat Generik Melalui Time Series Forecasting Model Pada Perusahaan Farmasi Di Tangerang: Studi Kasus,” J. Ind. Eng. Manag. Res. ( Jiemar), Vol. 1, No. 2, Pp. 35–49, 2020.

A. Setiawan, A. Wibowo, And S. Wijaya, “Aplikasi Peramalan Penjualan Kosmetik Dengan Metode Arima Alexander,” Konf. Nas. Sist. Inf., Pp. 1–10, 2017.

W. Seok, Y. Kim, And C. Park, “Pattern Recognition Of Human Arm Movement Using Deep Reinforcement Learning Intelligent Information System And Embedded Software Engineering, Kwangwoon University,” Pp. 917–919, 2018.

N. A. Batubara, R. M. Awangga, And S. F. Pane, Perbandingan Faster R-Cnn Dengan Ssd Mobilenet Untuk Mendeteksi Plat Nomor, Vol. 1. Kreatif, 2020.

L. Zaman, S. Sumpeno, And M. Hariadi, “Analisis Kinerja Lstm Dan Gru Sebagai Model Generatif Untuk Tari Remo,” J. Nas. Tek. Elektro Dan Teknol. Inf., Vol. 8, No. 2, Pp. 142–150, 2019.

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Copyright (c) 2022 Mhd. Alfandi, Pristiwanto Pristiwanto, A. M. Hatuaon Sihite

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