Rice Price Prediction System Based on Rice Quality and Milling Level using Multilayer Perceptron

Authors

  • Nur Nafi'iyah Universitas Islam Lamongan
  • Muchammad Khudori Universitas Islam Lamongan

DOI:

https://doi.org/10.32493/informatika.v7i1.15326

Keywords:

rice price prediction, backpropagation, architecture

Abstract

Bacpropagation algorithm is a neural network algorithm that has good performance. In addition to Backpropagation, SVM also includes a neural network algorithm. SVM also has a good performance in making predictions. This is based on previous research related to rice price predictions. During the COVID-19 pandemic, the government is obliged to maintain the stability of the price of basic necessities or basic commodities, the reason is to maintain the availability of basic commodities. Based on the conditions in the field, this research will create a rice price prediction system based on the type of rice quality at the milling level. The purpose of this research is to help stabilize rice prices in the market. The method used to predict is Backpropagation by proposing 2 architectures, namely 3-25-1, and 3-35-1. The dataset used is taken from bps.go.id, the total dataset is 318, and the method of evaluating the prediction results is using the MAE value. Based on the trial the lowest MAE value is 305.93 in the first architecture 3-25-1.

References

Budi, A. S., Susilo, P. H., & Nafi’iyah, N. (2020). SVM Algorithm for Predicting Rice Yields. Jurnal Teknologi Informasi Dan Pendidikan, 13(341).

Fardhani, A. A., Simanjuntak, D. I. N., & Wanto, A. (2018). Prediksi Harga Eceran Beras Di Pasar Tradisional Di 33 Kota Di Indonesia Menggunakan Algoritma Backpropagation. Jurnal Infomedia, 3(1).

Fraticasari, S. Y., Ratnawati, D. E., & Wihandika, R. C. (2018). Optimasi Pemodelan Regresi Linier Berganda Pada Prediksi Jumlah Kecelakaan Sepeda Motor Dengan Algoritme Genetika. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (JPTIIK) Universitas Brawijaya.

Herwanto, H. W., Widiyaningtyas, T., & Indriana, P. (2019). Penerapan Algoritme Linear Regression untuk Prediksi Hasil Panen Tanaman Padi. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI). https://doi.org/10.22146/jnteti.v8i4.537

Ischak, R., Asrof, A., & Darmawan, G. (2018). Peramalan Rata-Rata Harga Beras Di Tingkat Penggilingan Menggunakan Model Singular Spectrum Analysis (SSA). Seminar Nasional Matematika Dan Pendidikan Matematika.

Nafi’iyah, N., Ahmad Salaffudin1, A., & Nawafilah, N. Q. (2020). Algoritma Backpropagation untuk Memprediksi Korban Bencana Alam. SMATIKA JURNAL. https://doi.org/10.32664/smatika.v9i02.400

Ramadania, R. (2018). Peramalan Harga Beras Bulanan di Tingkat Penggilingan dengan Metode Weighted Moving Average. Bimaster, 7(4).

Sen, S., Sugiarto, D., & Rochman, A. (2020). Komparasi Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM) dalam Peramalan Harga Beras. ULTIMATICS, XII(1).

Suryanto, A. A. (2019). Penerapan Metode Mean Absolute Error (Mea) dalam Algoritma Regresi Linear untuk Prediksi Produksi Padi. SAINTEKBU. https://doi.org/10.32764/saintekbu.v11i1.298

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

Wuwung, V., Nainggolan, N., & Paendong, M. (2013). Prediksi Harga Beras Sultan dan Membramo di Kota Manado dengan Menggunakan Model ARIMA. Jurnal MIPA. https://doi.org/10.35799/jm.2.1.2013.739

Yuwantoro, M., Mahmud, I., & Murdiansyah Danang Triantoro, T. U. (2019). Prediksi Harga Beras Premium dengan Metode Algoritma K-Nearest Neighbor. E-Proceeding of Engineering, 7(1), 2714–2724.

Jurnal Informatika Universitas Pamulang Vol. 7 No. 1 Maret 2022

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Published

2022-05-31

Issue

Vol. 7 No. 1 (2022): JURNAL INFORMATIKA UNIVERSITAS PAMULANG

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