Algoritma Deep Learning dalam Memprediksi Hasil Panen Padi di Kabupaten Lamongan

Retno Wardhani; Nur Nafiiyah; Muhammad Ali Haydar

doi:10.30591/jpit.v7i1.2581

Algoritma Deep Learning dalam Memprediksi Hasil Panen Padi di Kabupaten Lamongan

Retno Wardhani, Nur Nafiiyah, Muhammad Ali Haydar

Abstract

Based on data, bps.go.id harvest from the 2nd year 018 to 2019 decreased about an 7.76%. The government must constantly analyze the rice yields of farmers in Indonesia to determine whether these crops can meet the Indonesian people's primary food needs. Research this will predict rice yields in Lamongan. This study aims to assist the government in overcoming the occurrence of significant food shortages in Lamongan. A system that can be used as a reference tool to assist in policy or rule in the district Lamongan. This research proposes deep learning algorithms to predict the harvest based on the land area (m²), spacing (cm), the type of rice, the number of times to fertilize, fertilizer, and crop yields (quintals). The dataset used in the study was collected through questionnaires. Questionnaires were distributed via a google form and contained as many as 390 rows of data. Some of the data produced were incorrect, so the processing was carried out. The results of data processing, the data that can be used are 380 rows. The proposed architectural model's test results show that the loss values of MSE, MAE, or MAPE are the same. The MSE, MAE, and MAPE values are 2939977.418, 301,788, and 83,798, respectively.

Full Text:

References

A. S. Budi, P. H. Susilo, and N. Nafi’iyah, “SVM Algorithm for Predicting Rice Yields,” J. Teknol. Inf. dan Pendidik., vol. 13, no. 341, 2020.

B. D. Setiawan, F. A. Bachtiar, and G. Ramadhona, “Prediksi Produktivitas Padi Menggunakan Jaringan Syaraf Tiruan Backpropagation,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., 2018.

N. Nafi’iyah, “ANALISIS ALGORITMA BACKPROPAGATION DENGAN SVM DALAM HASIL PREDIKSI NILAI UJIAN NASIONAL PADA SEKOLAH TINGKAT PERTAMA,” I N F O R M A T I K A, vol. 12, no. 1, pp. 5–13, 2020, doi: 10.36723/juri.v12i1.204.

N. Nafi’iyah, “Tuber Type Classification Based on Image of Bulbs with Deep Learning,” 2020, doi: 10.1109/ICISS50791.2020.9307584.

G. H. Kwak et al., “Automatic mandibular canal detection using a deep convolutional neural network,” Sci. Rep., 2020, doi: 10.1038/s41598-020-62586-8.

N. Nafi’iyah, A. Ahmad Salaffudin1, and N. Q. Nawafilah, “Algoritma Backpropagation untuk Memprediksi Korban Bencana Alam,” SMATIKA J., 2020, doi: 10.32664/smatika.v9i02.400.

H. W. Herwanto, T. Widiyaningtyas, and P. Indriana, “Penerapan Algoritme Linear Regression untuk Prediksi Hasil Panen Tanaman Padi,” J. Nas. Tek. Elektro dan Teknol. Inf., 2019, doi: 10.22146/jnteti.v8i4.537.

E. Triyanto, H. Sismoro, and A. D. Laksito, “IMPLEMENTASI ALGORITMA REGRESI LINEAR BERGANDA UNTUK MEMPREDIKSI PRODUKSI PADI DI KABUPATEN BANTUL,” Rabit J. Teknol. dan Sist. Inf. Univrab, 2019, doi: 10.36341/rabit.v4i2.666.

A. A. Suryanto, “PENERAPAN METODE MEAN ABSOLUTE ERROR (MEA) DALAM ALGORITMA REGRESI LINEAR UNTUK PREDIKSI PRODUKSI PADI,” SAINTEKBU, 2019, doi: 10.32764/saintekbu.v11i1.298.

Supriyanto, Sudjono, and D. Rakhmawati, “Prediksi Luas Panen dan Produksi Padi di Kabupaten Banyumas Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS),” J. Probisnis, 2012.

Djafar, M. I. Sarita, and Y. P. Pasrun, “Peramalan jumlah produksi padi di sulawesi tenggara menggunakan metode,” semanTIK, 2017.

N. L. Taqwa, I. K. D. Nuryana, and A. Andriani, “Sistem Prediksi Produksi Padi di Provinsi Jawa Timur Menggunakan Exponential Smoothing Berbasis Web,” Inovate, 2019.

B. Das, B. Nair, V. K. Reddy, and P. Venkatesh, “Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India,” Int. J. Biometeorol., vol. 62, no. 10, 2018, doi: 10.1007/s00484-018-1583-6.

N. Liundi, A. W. Darma, R. Gunarso, and H. L. H. S. Warnars, “Improving Rice Productivity in Indonesia with Artificial Intelligence,” 2019, doi: 10.1109/CITSM47753.2019.8965385.

A. Hamdianah, “Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java,” J. Inf. Technol. Comput. Sci., vol. 5, no. 3, 2021, doi: 10.25126/jitecs.202053182.

P. Anitha and T. Chakravarthy, “Agricultural Crop Yield Prediction using Artificial Neural Network with Feed Forward Algorithm,” Int. J. Comput. Sci. Eng., vol. 6, no. 11, 2018, doi: 10.26438/ijcse/v6i11.178181.

DOI: https://doi.org/10.30591/jpit.v7i1.2581