Penerapan Algoritma Backpropagation untuk Prediksi Hasil Panen Padi di Kabupaten Labuhan Batu Utara

Authors

  • Ahmad Fauza Anshori Tbn Universitas Islam Negeri Sumatera Utara
  • Rakhmat Kurniawan R. Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.32493/jtsi.v7i1.38318

Keywords:

Rice; JST; RMSE

Abstract

Rice is a food crop that has vital benefits and important benefits for human survival. Rice plants are often found in the surrounding environment, especially those living in rural areas. Farmers use various methods to continue to increase rice yields. However, in reality the rice harvest results are not stable from year to year, this is because farmers' businesses still depend on natural factors which have a risk of causing a high chance of crop failure, thus accumulating the risk of low income received by farmers, including in the Regency. North Labauhanbatu so a prediction is needed to find out the future picture of the rice harvest. Predictions are also made so that lowland rice production remains stable. One way that is often used to make predictions is to use artificial neural networks. Artificial Neural Networks (ANN) are designed based on the structure and function of the human brain as a model of intuitive imitation. In an artificial neural network there are a number of neurons. One network can connect to many other networks, and each connection (link) has a weight (weight). Prediction of rice harvest results in Kabuipatein Labuihan Batui Uitara using the backpropagation algorithm. The results of trials carried out with the Rapid Mineir architectural model software with an RMSEi amount of 0.403 +/- 0.000 in the implementation of backpropagation. The smaller the RMSEi (Root Meian Squiareid Eirror) the better the teirseibuit model.

References

Alimuddin, A. (2020). Teori dan Aplikasi Dasar Sistem Kendali Cerdas. Untirta Press.

Ambarwati, A. (2019). Nusantara dalam Piringku. Gramedia Pustaka Utama.

Cahyaningtyas, C., Manongga, D., & Sembiring, I. (2022). Algorithm Comparison and Feature Selection for Classification of Broiler Chicken Harvest. Jurnal Teknik Informatika (Jutif), 3(6), 1717–1727.

Hidayat, R., & Darussalam, U. (2022). Perbandingan Metode Saw Dan Ahp Pada Sistem Pendukung Keputusan Web Based Seleksi Karyawan Terbaik. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 7(1), 209–223. https://doi.org/10.29100/jipi.v7i1.2627

Irnanda, K. F., Windarto, A. P., & Damanik, I. S. (2022). Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner. JURIKOM (Jurnal Riset Komputer), 9(1), 122–130.

Kaswanto, R. L., Aurora, R. M., Yusri, D., Sjaf, S., & Barus, S. (2021). Kesesuaian lahan untuk komoditas unggulan pertanian di Kabupaten Labuhanbatu Utara. Analisis Kebijakan Pertanian, 19(2), 189–205.

Kusumastuti, A., Khoiron, A. M., & Achmadi, T. A. (2020). Metode Penelitian Kuantitatif. Deepublish. Medan.

Maiyuriska, R. (2022). Penerapan Jaringan Syaraf Tiruan dengan Algoritma Backpropagation dalam Memprediksi Hasil Panen Gabah Padi. Jurnal Informatika Ekonomi Bisnis, 28–33.

Mukhtar, H., Rifaldo, M., Taufiq, R. M., & Rizki, Y. (2021). Peramalan Kedatangan Wisatawan Mancanegara ke Indonesia Menurut Kebangsaan Perbulannya Menggunakan Metode Multilayer Perceptron. Jurnal CoSciTech (Computer Science and Information Technology), 2(2), 113–119.

Pellokila, V. A. A., Usman, K., & Pratiwi, N. K. C. (2023). Pemanfaatan Convolutional Neural Network (Cnn) Untuk Klasifikasi Jenis Beras Berbasis Citra. EProceedings of Engineering, 10(5) : 1-10.

Rahmadani, F., Pardede, A. M. H., & Nurhayati, N. (2021). Jaringan Syaraf Tiruan Prediksi Jumlah Pengiriman Barang Menggunakan Metode Backpropagation (Studi kasus: Kantor Pos Binjai). JTIK (Jurnal Teknik Informatika Kaputama), 5(1), 100–106.

Ramadhan, M. A. (2020). Penerapan Farm From Home Melalui Kegiatan Vertikultur sebagai Solusi Antisipatif terhadap Krisis Ketahanan Pangan Akibat Pandemi Covid-19. Minda Mahasiswa Indonesia: Antisipasi Resesi dan Krisis Pangan Akibat Pandemi, 5(5) : 1-10.

Sitepu, N. L. B. (2021). Jaringan Saraf Tiruan Memprediksi Nilai Pemelajaran Siswa Dengan Metode Backpropagation (Studi kasus: SMP Negeri 1 Salapian). Journal of Information and Technology, 1(2), 54–58.

Wahyudi, T. (2020). Pengelolaan Komoditas Hortikultura Unggulan Berbasis Lingkungan. Jurnal Biologi, 2(1) : 1-10.

Wardani, D. K., Panunggul, V. B., Ibrahim, E., Laeshita, P., Rachmawati, Y. S., Tuhuteru, S., & Nugrahani, R. A. G. (2023). Dasar Agronomi. Tohar Media. Deepublish. Medan.

Yuniar, R. (2022). Jembatan Emas Ketahanan Pangan-Perspektif Komunikasi. Yayasan Pustaka Obor Indonesia. Jakarta.

Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 Januari 2024

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Published

2024-01-30

How to Cite

Tbn, A. F. A., & R., R. K. (2024). Penerapan Algoritma Backpropagation untuk Prediksi Hasil Panen Padi di Kabupaten Labuhan Batu Utara. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(1), 335–342. https://doi.org/10.32493/jtsi.v7i1.38318

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Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

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