PENERAPAN METODE NEURAL NETWORK UNTUK PREDIKSI HARGA CABAI PASAR JOHAN DI KABUPATEN SEMARANG
DOI:
https://doi.org/10.69714/hnqc1f46Keywords:
Price Fluctuations, Neural Network, Chili PredictionAbstract
Chili consumption in Indonesia continues to increase along with population growth, however chili prices often experience fluctuations which are influenced by various factors, such as rainfall, market demand and production costs. These unpredictable price fluctuations can make it difficult for farmers and market players to plan chili production and distribution. This research aims to predict chili prices using the neural network method, by utilizing historical data on chili prices and other supporting factors such as weather conditions, market demand and production costs. The neural network model is expected to be able to produce chili price predictions that are more accurate and reliable compared to conventional methods. With accurate price predictions, it is hoped that it can provide a stronger basis for farmers and market players in making decisions regarding the production, distribution and marketing strategies of chilies, as well as creating price stability in the market.
References
K. H. Suradiradja, “Algoritme Machine Learning Multi-Layer Perceptron dan Recurrent Neural Network untuk Prediksi Harga Cabai Merah Besar di Kota Tangerang,” Fakt. Exacta, vol. 14, no. 4, p. 194, 2022, doi: 10.30998/faktorexacta.v14i4.10376.
D. W. Lestari and R. Yotenka, “Arima Aplikasi Metode Box-Jenkins (Arima) Untuk Meramalkan Harga Komoditas Cabai Merah,” Khazanah J. Mhs., vol. 14, no. 1, pp. 31–37, 2022, doi: 10.20885/khazanah.vol14.iss1.art4.
I. Wahyudi, Panen Cabai Sepanjang Tahun. Ciganjur, Jaagakarta, Jakarta Selatan: PT AgroMedia Pustaka, 2011.
E. Priyanti, “Implementasi Neural Network Pada Prediksi Pendapatan Rumah Tangga,” Swabumi, vol. 6, no. 1, pp. 18–26, 2018, doi: 10.31294/swabumi.v6i1.3312.
N. R. Mufidah and Z. Fatah, “Gudang Jurnal Multidisiplin Ilmu Penerapan Data Mining Untuk Pengelompokan Kepadatan Penduduk Menurut Provinsi 2021 Menggunakan Algoritma K-means Dengan Rapid Miner,” vol. 2, no. November, pp. 167–173, 2024.
D. Natasya and Z. Fatah, “Gudang Jurnal Multidisiplin Ilmu Penerapan Data Mining Untuk Analisis Data Sosial Media Menggunakan Metode K-Nearest Neoghbor ( K-NN ),” vol. 2, no. November, pp. 105–109, 2024.
R. B. P. Nur Iriawan, Brodjol Sutijo Suprih Ulama, Kartika Fithriasari, Achmad Syahrul Choir, Bayesian Neural Network: Dalam Pemodelan Small Area Estimation. Yogyakarta: ANDI, 2020.
S. Kasus, P. T. Pln, and R. Sumatera, “ARTIFICIAL NEURAL NETWORK UNTUK MEMPREDIKSI BEBAN LISTRIK DENGAN MENGGUNAKAN METODE BACKPROPAGATION,” vol. 5, no. 2, pp. 61–70, 2019.
A. S. Lalu Puji Indra Kharisma, Sitti rachmawati Yahya, Sepriono, rahmadya Trias handayanto, Herlawati, Made agus Oka Gunawan, Putu Susila Handika, Heliza rahmania Hatta, Metode SPK Favorit di Masa Depan. Jambi: PT. Sonpedia Publishing Indonesia, 2023.
I. K. P. Suniantara, G. Suwardika, and S. Soraya, “Peningkatan Akurasi Klasifikasi Ketidaktepatan Waktu Kelulusan Mahasiswa Menggunakan Metode Boosting Neural Network,” J. Varian, vol. 3, no. 2, pp. 95–102, 2020, doi: 10.30812/varian.v3i2.651.
M. D. M. Prastyadi wibawa rahayu, Gede Iwan Sudipa, Suryani, Arie Surachman, Achmad Ridwan, Gede Mahendra Darmawiguna, Nurtanzis Sutoyo, Isnandar Slamat, Sitti Harlina, Buku Ajar Data Mining, Cetakan Pe. Jambi: PT.Sonpedia Publishing Indonesia, 2024.
W. Setiawan, Deep Laerning Menggunakan Convolutional Neural Network. Malang: Media Nusa Creative, 2020.











