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All Journal Journal of Information Technology and Computer Science Jurnal Rekayasa Sistem Informasi dan Teknologi
Hamdianah, Andi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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EVALUASI METODE JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI PRODUKTIVITAS PADI: REVIEW DAN PROSPEK Hamdianah, Andi; Muh. Irsan S; Mardawia Mabe Parenreng
Jurnal Rekayasa Sistem Informasi dan Teknologi Vol. 1 No. 3 (2024): Februari
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jrsit.v1i3.556

Abstract

Memprediksi hasil produksi padi sangatlah kompleks karena memiliki banyak faktor yang mempengaruhi seperi faktor cuaca, luas lahan dan kondisi tanah pada sebuah lahan persawahan. Sehingga diperlukan metode yang baik dalam menyelesaikan masalah ini. Pada makalah ini, dikaji dan diulas metode Jaringan Syaraf Tiruan (JST) untuk memprediksi hasil produksi padi. Dari literatur yang ada, metode JST dan metode perbaikannya dapat memberikan hasil prediksi yang baik dengan waktu komputasi yang singkat. Kata kunci: Extreme Learning Machine, Jaringan Syaraf Tiruan, Long Short Term Memory, Padi, Prediksi,
Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java Hamdianah, Andi; Mahmudy, Wayan Firdaus; Widaryanto, Eko
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1526.133 KB) | DOI: 10.25126/jitecs.202053182

Abstract

Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.
Co-Authors Eko Widaryanto Mardawia Mabe Parenreng Muh. Irsan S Wayan Firdaus Mahmudy