Aplikasi Prediksi Harga Jahe Merah Metode Time Series Autoregressive Integrated Moving Average

Ruhmi Sulaehani, Bahrin Bahrin

Abstract


Harga jahe merah naik secara signifikn sebelum wabah Covid-19 harga jahe di pasaran normal berkisar Rp.20.000/kg, tapi selama pandemi harga jahe merah naik secara signifikan. Harga naik secara signifikan tanpa pemberitahuan sebelumnya terjadi karena permintaan yang sangat melonjak baik dalam wilayah Kabupaten Pohuwato maupun diluar wilayah Kabupaten Pohuwato. Tujuan Penelitian ini yaitu membantu pemerintah memprediksi harga jahe merah dipasaran dalam kurung waktu tertentu dengan membuat aplikasi prediksi berbasis Web, bahasa pemrograman PHP dan untuk menganalisis digunakan aplikasi software Statistik R, metode yang digunakan yaitu Time series model ARIMA. Berdasarkan nilai aktual dan nilai hasil prediksi harga jahe merah yang diperoleh, pada model ARIMA didapatkan nilai AIC dengan model terbaik adalah -3048,61, yang merupakan nilai minimun. Aplikasi yang dibuat dapat membantu masyarakat dan pemerintah setempat mendapatkan info perkiraan atau prediksi harga jahe merah dimasa depan. Hasil prediksi harga jahe merah memperlihatkan harga jahe merah turun di bulan Juni yaitu sebesar Rp. 21.932/kg

The price of red ginger had increased significantly. Before the Covid-19 outbreak the price of red ginger on the market was normal. It was around Rp. 20.000/kg but during the pandemic the price of the red ginger increased significantly without any prior notification due to the red ginger was in demand both inside and outside the Pohuwato Regency. The purpose of this research is to help the Government control the prices in the market and to help the public society to get fast information about the estimation of the future prices during the particular time by creating a web-based prediction application using the PHP Programming Language and Data Analysis using the R Statistical Software Application. The research merhod used is the ARIMA time series model based on the actual value and predicted value of the red ginger prices obtained. There is an AIC value in the ARIMA model. The best model is -3048.61 the best model has the minimum AIC value. The application created is able to help the community and local government to get information on the estimated prices of the red ginger in the future. The results of the estimated prices of the red ginger showed the prices decreased in june by Rp. 21.932/kg.


Keywords


Prediksi; Time Series; ARIMA, PHP, R

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References


T. Lentera, “Khasiat dan Manfaat JAhe Merah,” Buku.

S. Pangesti, C. Suhery, and T. Rismawan, “Aplikasi Prediksi Harga Sembako Menggunakan Metode Box-Jenkins Berbasis Website,” Coding J. Komput. dan Apl. Untan, vol. 06, no. 03, pp. 139–149, 2018.

S. Aktivani, “Pemodelan Harga Cabai Merah Menggunakan Model ARIMA (Studi Kasus: Harga Cabai Merah di Kota Padang Periode Januari 2010 – Desember 2020),” Stat. J. Theor. Stat. Its Appl., vol. 21, no. 1, pp. 51–60, 2021, doi: 10.29313/jstat.v21i1.7935.

R. H. R. Bangun, “Penerapan Autoregressive Integrated Moving Average (ARIMA) Pada Peramalan Produksi Kedelai di Sumatera Utara,” vol. 9, no. 2, 2019.

F. Nur Hadiansyah, “Prediksi Harga Cabai dengan Menggunakan pemodelan Time Series ARIMA,” Indones. J. Comput., vol. 2, no. 1, p. 71, 2017, doi: 10.21108/indojc.2017.2.1.144.

H. Djoni, “Penerapan Model Arima Untuk Memprediksi Application Of Arima To Forecasting Stock Price Of PT . Telokm Tbk .,” J. Ilm. Sains, vol. 11, no. 1, pp. 117–119, 2011.

Y. Wigati, R. Rais, and I. T. Utami, “Pemodelan Time Series Dengan Proses Arima Untuk Prediksi Indeks Harga Konsumen (Ihk) Di Palu – Sulawesi Tengah,” J. Ilm. Mat. Dan Terap., vol. 12, no. 2, pp. 149–159, 2017, doi: 10.22487/2540766x.2015.v12.i2.7908.

M. A. Rasyidi, “Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan ARIMA,” J. Inf. Syst. Eng. Bus. Intell., vol. 3, no. 2, p. 107, 2017, doi: 10.20473/jisebi.3.2.107-112.

D. Suprianto and P. N. Malang, “Buku pintar pemograman PHP,” vol. 2, no. 2, p. 113, 2018.

Forkas, “Aplikasi Software Statistik R,” Artikel, 2012, [Online]. Available: http://forkas.stis.ac.id/2012/12/aplikasi-software-statistik-r.html.

S. W. Kartiko, “Mengenal Software Statistika ‘ R ’ sebagai Datamining Tool di Linux,” pp. 1–12, 2012.

N. Salwa, N. Tatsara, R. Amalia, and A. F. Zohra, “Peramalan Harga Bitcoin Menggunakan Metode ARIMA (Autoregressive Integrated Moving Average),” J. Data Anal., vol. 1, no. 1, pp. 21–31, 2018, doi: 10.24815/jda.v1i1.11874.

None, “ARIMA (Autoregressive Integrated Moving Average),” Artikel.

R. Sulaehani, “Prediksi Keputusan Klien Telemarketing Untuk Deposito Pada Bank Menggunakan Algoritma Naive Bayes Berbasis Backward Elimination,” Ilk. J. Ilm., vol. 8, no. 3, pp. 182–189, 2016, doi: 10.33096/ilkom.v8i3.83.182-189.




DOI: https://doi.org/10.37905/jjeee.v4i1.12077

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