Prediksi Harga Beras Medium Di Indonesia Dengan Membandingkan Metode Regresi Linear Dan Regresi Polinomial

Firdho Akbar Bilawa(1*), Hanny Hikmayanti(2), R Rahmat(3),

(1) Universitas Buana Perjuangan Karawang, Indonesia
(2) Universitas Buana Perjuangan Karawang, Indonesia
(3) Universitas Buana Perjuangan Karawang, Indonesia
(*) Corresponding Author

Abstract


Availability of sufficient and equitable food is one of the pillars of realizing national  food security. Rice, as an important aspect of Indonesian food, has a strategic role and its availability must always be ensured. The majority of Indonesian people’s needs are medium types of rice. The price of medium rice fluctuates, but tends to increase over time. Changes in rice prices have a significant impact on people’s lives and can threaten household food security. Predicting the price of medium rice is very important for the Indonesian government to maintain economic stability. It is hoped that the accurate prediction results can be taken into consideration by the Indonesian government in controlling and determining medium rice price policies in Indonesia. The data used is medium rice price data in Indonesia from January 2013 to February 2024, totaling 134 data. The method used to predict rice prices is the linear regression and polynomial regression methods. This research focuses on the applying and comparing the effectiveness of the two methods by considering their accuracy and error rates.  The accuracy of the prediction results is assessed by calculating the MAPE value.  The research result show that both methods have accurate prediction model performance because the MAPE value is less than 10%. The linear regression method can predict the medium rice price more accurately because it has a smaller MAPE value of 6,29%, compared to the polynomial regression method of  6,88%.

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References


R. E. Putra and A. S. Sinaga, “Perkiraan Harga Beras Premium DKI Jakarta menggunakan Regresi Linier,” Journal Information Engineering and Educational Technology, vol. 06, no. 02, pp. 80–85, 2022.

BPS, “Rata-Rata Harga Beras Bulanan di Tingkat Penggilingan Menurut Kualitas (Rupiah/Kg), 2024,” Badan Pusat Statistik. Accessed: Feb. 05, 2024. [Online]. Available: https://www.bps.go.id/id/statistics-table/2/NTAwIzI=/rata-rata-harga-beras-bulanan-di-tingkat-penggilingan-menurut-kualitas.html

R. A. Nugrahapsari and M. P. Hutagaol, “Tinjauan Kritis terhadap Kebijakan Harga Gabah dan Beras di Indonesia,” Forum Penelitian Agro Ekonomi, vol. 39, no. 1, pp. 11–26, Aug. 2021, doi: 10.21082/fae.v39n1.2021.11-26.

F. Ustadatin, A. Muqtadir, and A. Arifia, “Implementasi Metode Weighted Moving Average (WMA) pada Prediksi Harga Bahan Pokok,” Jurnal Sistem Komputer, vol. 12, no. 2, pp. 203–210, Sep. 2023, doi: 10.34010/komputika.v12i2.10304.

V. Arinal and M. Azhari, “Penerapan Regresi Linear untuk Prediksi Harga Beras di Indonesia,” Jurnal Sains dan Teknologi, vol. 5, no. 1, pp. 341–346, 2023, doi: 10.55338/saintek.v5i1.1417.

Rahmadini, E. E. L. Lubis, A. Priansyah, R. W. N. Yolanda, and T. Meutia, “Penerapan Data Mining untuk Memprediksi Harga Bahan Pangan di Indonesia menggunakan Algoritma K-Nearest Neighbor,” Jurnal Mahasiswa Akuntansi Samudra, vol. 4, no. 4, pp. 223–235, 2023.

L. H. Hasibuan, S. Musthofa, and U. Imam Bonjol Padang, “Penerapan Metode Regresi Linear Sederhana untuk Prediksi Harga Beras di Kota Padang,” Journal of Science and Technology, vol. 2, no. 1, pp. 85–95, 2022.

A. E. Putra and A. Juarna, “Prediksi Produksi Daging Sapi Nasional dengan Metode Regresi Linier dan Regresi Polinomial,” Jurnal Ilmiah Komputasi, vol. 20, no. 2, pp. 209–215, Jun. 2021, doi: 10.32409/jikstik.20.2.2722.

R. Heni, Solihin, J. Supratman, and R. Muhendra, “Pengembangan Model Peramalan Penjualan menggunakan Metode Regresi Linier dan Polinomial pada Industri Makanan Ringan (Studi Kasus: CV. Stanley Mandiri Snack),” TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 2, pp. 185–192, Jul. 2023, doi: 10.37373/tekno.v10i2.456.

F. Putra, H. F. Tahiyat, R. M. Ihsan, Rahmaddeni, and L. Efrizoni, “Penerapan Algoritma K-Nearest Neighbor menggunakan Wrapper Sebagai Preprocessing untuk Penentuan Keterangan Berat Badan Manusia,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 273–281, Jan. 2024, doi: 10.57152/malcom.v4i1.1085.

Y. Supriyanto, M. Ilhamsyah, and U. Enri, “Prediksi Harga Minyak Kelapa Sawit menggunakan Linear Regression dan Random Forest,” Jurnal Ilmiah Wahana Pendidikan, vol. 8, no. 7, pp. 178–185, 2022.

N. Fitriyani, D. R. Amalia, H. H. Handayani, and A. F. N. Masruriyah, “Aplikasi Berbasis Web Berdasarkan Model Klasifikasi Algoritma SVM dan Logistic Regression Terhadap Data Diabetes 1,” Riset dan E-Jurnal Manajemen Informatika Komputer, vol. 7, no. 4, pp. 1762–1771, 2023, doi: 10.33395/remik.v7i4.13001.

V. R. Danestiara, E. Setiana, I. Akbar, and T. Hidayah, “Algoritma Gated Recurrent Unit untuk Prediksi Harga Indeks Penutupan Saham LQ45,” Jurnal Accounting Information System (AIMS), vol. 7, no. 1, pp. 1–8, 2024, doi: 10.32627.

S. A. Laksono, A. R. Pratama, and Rahmat, “Perbandingan Metode Linear regresi dan Polynomial Regresi untuk Memprediksi Harga Saham Studi Kasus Bank BCA,” INFOTECH : Jurnal Informatika & Teknologi, vol. 4, no. 1, pp. 59–70, Jun. 2023, doi: 10.37373/infotech.v4i1.602.

V. Koerniawan, A. Nilsen, F. P. Sari, M. Y. Ayyasy, and S. W. Indratno, “Pemodelan Peluang Transisi Rantai Markov dengan Simulasi Monte Carlo Berdasarkan Distribusi Multinoulli untuk Memprediksi Harga Indeks Saham,” Jurnal Statistika dan Aplikasinya, vol. 6, no. 2, pp. 276–287, 2022.

G. Budiprasetyo, M. Hani’ah, and D. Z. Aflah, “Prediksi Harga Saham Syariah menggunakan Algoritma Long Short-Term Memory (LSTM),” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 8, no. 3, pp. 164–172, Jan. 2022, doi: 10.25077/teknosi.v8i3.2022.164-172.

I. Nabillah and I. Ranggadara, “Mean Absolute Percentage Error untuk Evaluasi Hasil Prediksi Komoditas Laut,” JOINS (Journal of Information System), vol. 5, no. 2, pp. 250–255, Nov. 2020, doi: 10.33633/joins.v5i2.3900.




DOI: http://dx.doi.org/10.30645/jurasik.v9i2.810

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v9i2.810.g784

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