Comparison of Linear Regression and Polynomial Regression for Predicting Rice Prices in  Lhokseumawe City

Muhammad Iqbal; Rozzi Kesuma Dinata; Rizki Suwanda

doi:10.32736/sisfokom.v14i3.2396

Authors

Muhammad Iqbal Malikussaleh University
Rozzi Kesuma Dinata Malikussaleh University
Rizki Suwanda Malikussaleh University

DOI:

https://doi.org/10.32736/sisfokom.v14i3.2396

Keywords:

Rice Price, Prediction, Linear Regression, Polynomial Regression

Abstract

Rice is a strategic food commodity in Indonesia, and its price fluctuations significantly impact inflation, economic stability, and poverty levels. Accurate price prediction is, therefore, essential for effective policymaking. The objective of this research is to develop a system for predicting the price of rice in Lhokseumawe City, employing a comparison of the accuracy of linear and polynomial regression models. To this end, daily price data from the Strategic Food Price Information Center (PIHPS) from 2020 to 2024 were utilized, with both models being implemented in Python. The findings indicate that 4th-order polynomial regression exhibited optimal performance, attaining a mean absolute percentage error (MAPE) of 1.85%, a mean absolute error (MAE) of 205.23, and a root mean squared error (RMSE) of 284.88. Conversely, the implementation of linear regression resulted in substantially elevated error metrics, with a mean absolute percentage error (MAPE) of 5.16%, a mean absolute error (MAE) of 553.91, and a root mean square error (RMSE) of 614.14. The findings indicate that 4th-order polynomial regression is a substantially more effective model for predicting rice prices in Lhokseumawe. The latter's superiority suggests that local rice price dynamics are characterized by significant non-linear patterns, rendering it a more robust tool for capturing data volatility and supporting data-driven policy.

References

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, Sep. 2023.

Y. P. P. Pasaribu and I. M. K. Karo, “ANALISIS PREDIKSI HARGA BERAS DI INDONESIA DENGAN METODE MONTE CARLO,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 4, Art. no. 4, Jun. 2024, doi: 10.36040/jati.v8i4.9962.

N. Nafi’iyah and P. A. Wulandari, “Prediksi Harga Beras Berdasarkan Kualitas Beras dengan Metode Long Short Term Memory,” vol. 7, no. 2, 2022.

A. R. Anandyani, D. K. A. Astutik, N. Bariroh, and A. Indrasetianigsih, “PREDIKSI RATA-RATA HARGA BERAS YANG DIJUAL OLEH PEDAGANG BESAR (GROSIR) MENGGUNAKAN METODE ARIMA BOX JENKINS,” TEKNOSAINS, vol. 15, no. 2, p. 151, Aug. 2021, doi: 10.24252/teknosains.v15i2.17721.

M. Andriyani, S. Nurwilda, D. Z. Haq, and D. C. R. Novitasari, “PREDIKSI HARGA BERAS PREMIUM TAHUN 2024 MENGGUNAKAN METODE GRADIENT BOOSTED TREES REGRESSION,” Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, vol. 18, no. 2, Art. no. 2, Aug. 2024, doi: 10.47111/jti.v18i2.14859.

N. Nurdin, “ANALISA DATA MINING DALAM MEMPREDIKSI MASYARAKAT KURANG MAMPU MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” JITET, vol. 12, no. 2, Apr. 2024, doi: 10.23960/jitet.v12i2.4131.

M. Sholeh, E. K. Nurnawati, and U. Lestari, “Penerapan Data Mining dengan Metode Regresi Linear untuk Memprediksi Data Nilai Hasil Ujian Menggunakan RapidMiner,” JISKA (Jurnal Informatika Sunan Kalijaga), vol. 8, no. 1, Art. no. 1, Jan. 2023, doi: 10.14421/jiska.2023.8.1.10-21.

D. A. Ferryan, P. K. Intan, and M. Hafiyusholeh, “Peramalan Harga Minyak Mentah di Indonesia dengan Metode Regresi Polinomial,” JIMT, vol. 19, no. 1, pp. 13–18, Jun. 2022, doi: 10.22487/2540766X.2022.v19.i1.15779.

I. R. Harahap, M. Z. Siambaton, and H. Santoso, “IMPLEMENTASI METODE REGRESI LINEAR SEDERHANA UNTUK PREDIKSI HARGA BERAS DI KOTA MEDAN,” 2023.

Z. Setiawan et al., BUKU AJAR DATA MINING. PT. Sonpedia Publishing Indonesia, 2023.

S. Karbala and I. Ali, “MEMPREDIKSI HARGA BERAS ECERAN MENGGUNAKAN ALGORITMA REGRESI LINIER,” jati, vol. 7, no. 3, pp. 1554–1559, Oct. 2023, doi: 10.36040/jati.v7i3.6901.

R. Rahman, A. Sudiarjo, and Y. Sumaryana, “PREDIKSI UPAH MINIMUM PROVINSI 10 TAHUN KEDEPAN DENGAN MENGGUNAKAN MODEL POLYNOMIAL REGRESSION,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 4, Art. no. 4, Aug. 2024, doi: 10.36040/jati.v8i4.9889.

R. Putra and A. Sindar, “Perkiraan Harga Beras Premium DKI Jakarta Menggunakan Regresi Linier,” Journal of Information Engineering and Educational Technology, vol. 6, pp. 80–85, Dec. 2022, doi: 10.26740/jieet.v6n2.p80-85.

A. E. Putra and A. Juarna, “Prediksi Pro duksi Daging Sapi Nasional dengan Meto de Regresi Linier dan Regresi Polinomial,” Jurnal Ilmiah Komputasi, vol. 20, no. 2, Art. no. 2, Jun. 2021.

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, Art. no. 2, Jul. 2023, doi: 10.37373/tekno.v10i2.456.