Predicting Rainfall Intensity using Naïve Bayes and Information Gain Methods (Case Study: Sleman Regency)

Sena, I. G. W.; Dillak, J. W.; Leunupun, Pieter; Santoso, Alb. Joko

doi:10.1088/1742-6596/1577/1/012011

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…In flood prediction in Bangladesh using the K-NN algorithm, the study shows that the K-NN algorithm yields an average accuracy value of 94.91%, an average precision value of 92%, and an average recall value of 91% [7]. In the Sleman regency's rainfall intensity prediction using the NB algorithm, the study shows that the most influential parameter on rainfall intensity is the average temperature with an entropy value of 0.047811028 [8]. Using these references, we studied how machine learning solves flood prediction and classification problems based on the water level.…”

Section: Imentioning

confidence: 98%

Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

Batubara¹,

Zarlis²,

Rosnelly³

2023

ICoSTEC

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In the era of technology 4.0, a system is needed to support the development of a company, both in industry, education, and others to help solve problems. In this study, the authors used the Backpropagation Neural Network Algorithm in recognizing hyperthyroid disease patterns. In this study, in the recognition of hyperthyroid disease patterns. The author uses 11 data variables that will be trained using the back-propagation algorithm where the weighting is done randomly and the second data is trained using the backpropagation algorithm. In this study using Matlab application for processing. From the results of testing data derived from kaggle, namely hyperthyroid disease data above, we can see in the 2-2-1 architecture which shows that the target is reduced by the jst output that the SSE is 0.06571 which indicates that there is an increase in hyperthyroid disease activity in humans. From the data obtained, that the performance of artificial neural network calculations with the Backpropagation Algorithm is 86%. Can be seen by comparing the desired target with the prediction target.

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Section: Imentioning

confidence: 98%

Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

Batubara¹,

Zarlis²,

Rosnelly³

2023

ICoSTEC

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“…Sebagai negara tropis dengan musim hujan dan kemarau, Indonesia juga merasakan dampak dari perubahan iklim yang terjadi (Sena, Dillak, Leunupun, & Santoso, 2020), maka dari itu sebagai upaya untuk mengantisipasi kerugian terhadap pertumbuhan tanaman di masa mendatang, Stasiun Klimatologi Jawa Timur yang terletak pada Lintang -7.90080, bujur 112.59790 memperoleh data laporan harian iklim yang dapat peneliti ambil dari website BMKG dengan rentang waktu mulai dari Januari 2020 hingga September 2022, format dari data adalah excel, sehingga dapat mudah dilakukan pengolahan untuk keperluan analisis dan prediksi perubahan iklim melalui metode data mining dan algoritma Naïve Bayes.…”

Section: Pendahuluanunclassified

Prediksi Perubahan Iklim Untuk Pertumbuhan Tanaman Jeruk Keprok Menggunakan Naïve Bayes

Chusyairi¹,

Haryanto²,

Hayat³

2023

JIM

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Iklim merupakan suatu pola data yang sangat berpengaruh pada pertumbuhan tanaman di masa mendatang, Tanaman jeruk keprok merupakan bahan baku untuk kebutuhan pangan masyarakat, sehingga diperlukan strategi untuk menjaga stabilitasi produksi melalui berbagai strategi terutama memaksimalkan data yang mudah di akses di internet. Iklim memiliki komponen yang dapat mempengaruhi pertumbuhan suatu tanaman diantaranya temperatur udara, kelembapan udara, lama penyinaran, dan curah hujan. Dalam rangka memaksimalkan penelitian ini, metode teorema bayes digunakan untuk dapat melakukan klasifikasi data didasarkan pada nilai probabilitas iklim baik atau buruk pada pertumbuhan tanaman, diharapkan naïve bayes dengan akurasi sebesar 85%, presisi sebesar 83%, recall sebesar 100%, dan F1 Score sebesar 90% dapat membantu upaya menjaga stabilitasi pertumbuhan tanaman dengan memanfaatkan data iklim yang tersedia.

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“…Data yang digunakan adalah variabel tekanan (P), evaporasi (E), suhu maksimal (T), kelembaban rata-rata (RH), dan lama penyinaran matahari (LS) sebagai variabel input sedangkan data curah hujan (CH) sebagai variabel target. Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) mengkategorikan intensitas curah hujan berdasarkan 4 kriteria seperti pada Tabel 2 [31]. Dataset penelitian ini ditunjukkan pada Tabel 1.…”

Section: Sumber Dataunclassified

Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian

Musfiroh

Novitasari

Intan

et al. 2023

bits

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Since the last three years North Luwu has experienced frequent hydrological disasters in the form of floods and landslides. The disaster had a negative impact on the availability of clean water, failed to plant and even tended to reduce the quality of the harvest. Cocoa is one of the leading commodities of North Luwu Regency whose productivity has decreased due to the impact of climate change so that it will affect the sustainability of the local population's income. Therefore, the purpose of this research is to anticipate rainfall that will occur to prevent or reduce the risk of failure and loss. Principal Component Analysis (PCA) Method is used as feature extraction to find out the most influential variables and the Long Short-Term Memory (LSTM) method is used as a prediction method. Future rainfall is predicted using meteorological variables such as pressure, evaporation, maximum temperature, average humidity, and sunshine duration from 1 January 2017 to 30 September 2022. Based on the PCA results, 4 variables are obtained that have the most influence on rainfall, namely: variable evaporation, maximum temperature, average humidity, and length of sunlight. These variables are used as input to predict rainfall using LSTM. In this study using trial parameters, namely the number of hidden, batch size, and learn rate drop period. The best prediction results were obtained for MAPE of 0.0018 with the number of hidden, batch size and learn rate drop periods of 100, 32, and 50 respectively. The prediction results show very heavy rainfall occurring on August 28, 2021 of 101.9734 mm, 21 September 2021 of 108.6528 mm, and 5 April 2022 of 116.5510 mm. In this study PCA was able to increase accuracy in considering all parameters and choosing the most effective.

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Predicting Rainfall Intensity using Naïve Bayes and Information Gain Methods (Case Study: Sleman Regency)

Cited by 5 publications

References 6 publications

Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

Prediksi Perubahan Iklim Untuk Pertumbuhan Tanaman Jeruk Keprok Menggunakan Naïve Bayes

Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian

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