The Support Vector Regression Method Performance Analysis in Predicting National Staple Commodity Prices


Huzain Azis(1*); Purnawansyah Purnawansyah(2); Nirwana Nirwana(3); Felix Andika Dwiyanto(4);

(1) Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(3) Universitas Muslim Indonesia
(4) AGH University of Science and Technology
(*) Corresponding Author

  

Abstract


Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in the data set, namely commodity, date, and price. This research divides the entire dataset into 80% training and 20% testing data. The results of this research show that SVR using the RBF kernel produces good forecasting accuracy for all datasets with an average Mean Square Error (MSE) training data of 6,005 while data testing is 6,062, Mean Absolute Deviation (MAD) of training data is 6,730 while data testing is 6.6831, Mean Absolute Percentage Error (MAPE) training data is 0.0148 while data testing is 0.0147, and Root Mean Squared Error (RMSE) training data is 7.772 while data testing is 7.746.


Keywords


Food Prices; Machine Learning; Predictions; Support Vector Regression.

  
  

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doi  https://doi.org/10.33096/ilkom.v15i2.1686.390-397 		  

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A. E. Wardoyo and A. Nilogiri, “Prediksi Harga Sembako Menggunakan Algoritma Memetika Dan Scatter Search Studi Kasus Di Kabupaten Jember,” J. Apl. Sist. Inf. Dan Elektron., vol. 2, no. 1, pp. 1–6, 2020.

R. E. Cahyono, J. P. Sugiono, and S. Tjandra, “Analisis Kinerja Metode Support Vector Regression ( SVR ) dalam Memprediksi Indeks Harga Konsumen ( Performance Analysis of Support Vector Regression ( SVR ) Methods in Predicting the Consumer Price Index ),” J. Teknol. Inf. dan Multimed., vol. 1, no. 2, pp. 106–116, 2019.

A. Nastuti, “Teknik Data Mining Untuk Penentuan Paket Hemat Sembako Dan Kebutuhan Harian Dengan Menggunakan Algoritma Fp-Growth (Studi Kasus Di Ulfamart Lubuk Alung),” J. Ilm. Fak. Sains dan Teknol., vol. 7, no. 3, pp. 111–119, 2019.

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

J. T. Informatika, U. Jenderal, and A. Yani, “Prediksi Harga Sembako di DKI Jakarta Menggunakan Artificial Neural Network,” JUMANJI, vol. 3, no. 2, pp. 34–41, 2019.

M. Astiningrum, I. Putri, and V. Wijayaningrum, “Peramalan Harga Bahan Pokok Menggunakan Support Vector Regression,” Sentia 2020, vol. 12, no. October, pp. 77–82, 2020.

J. E. Islam, “Analisis Pandemic Covid-19 Terhadap Harga Sembako,” Al-Sharf J. Ekon. Islam, vol. 1, no. 1, pp. 43–51, 2020, doi: 10.56114/al-sharf.v1i1.34.

S. Saadah, F. Z. Z, and H. H. Z, “Support Vector Regression (SVR) Dalam Memprediksi Harga Minyak Kelapa Sawit di Indonesia dan Nilai Tukar Mata Uang EUR/USD,” J. Comput. Sci. Informatics Eng., vol. 5, no. 1, pp. 85–92, 2021, doi: 10.29303/jcosine.v5i1.403.

L. Y. Kurniawati, H. Tjandrasa, and I. Arieshanti, “Prediksi pergerakan harga saham menggunakan support vector regression,” J. SimanteC, vol. VIII, no. 1, pp. 11–21, 2013.

N. D. Maulana, B. D. Setiawan, and C. Dewi, “Implementasi Metode Support Vector Regression (SVR) Dalam Peramalan Penjualan Roti (Studi Kasus : Harum Bakery),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2986–2995, 2019.

R. K. D. Olivia Bonita, Lailil Muflikhah and Program, “Prediksi Harga Batu Bara Menggunakan Support Vector Regression ( SVR ),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 12, pp. 6603–6609, 2018.

K. Puteri and A. Silvanie, “Machine Learning Untuk Model Prediksi Harga Sembako Dengan Metode Regresi Linier Berganda,” J. Nas. Inform., vol. 1, no. 2, pp. 82–94, 2020.

H. Sumarno, “Analisis Support Vector Regression Dengan Algoritma Grid Search Untuk Memprediksi Harga,” Math. Subj. Classif., vol. 18, no. 1, pp. 41–60, 2022.

K. Dewi, P. P. Adikara, and S. Adinugroho, “Prediksi Indeks Harga Konsumen ( IHK ) Kelompok Perumahan , Air , Listrik , Gas Dan Bahan Bakar Menggunakan Metode Support Vector Regression,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3856–3862, 2018.

N. P. N. Hendayanti and M. Nurhidayati, “Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali,” J. Varian, vol. 3, no. 2, pp. 149–162, 2020, doi: 10.30812/varian.v3i2.668.

R. Tange, M. A. Rasmussen, E. Tairac, and R. Bro, “Application of support vector regression for simultaneous modelling of near infrared spectra from multiple process steps,” J. Near Infrared Spectrosc., vol. 23, no. 2, pp. 75–84, 2015, doi: 10.1255/jnirs.1149.

S. Lutfia Septiningrum1, Hasbi Yasin2, “Prediksi Indeks Harga Saham Gabungan Menggunakan Support Vector Regression (Svr) Dengan Algoritma Grid Search,” Gaussian, vol. 4, no. 2, pp. 315–321, 2015.

R. E. Caraka, H. Yasin, and A. W. Basyiruddin, “Peramalan Crude Palm Oil (CPO) Menggunakan Support Vector Regression Kernel Radial Basis,” J. Mat., vol. 7, no. 1, pp. 43–57, 2017, doi: 10.24843/jmat.2017.v07.i01.p81.

S. S. M. Evy Sulistianingsih, “Prediksi Nilai Tukar Dolar Amerika Serikat Terhadap Rupiah Dengan Metode Support Vector Regression (Svr),” Bimaster Bul. Ilm. Mat. Stat. dan Ter., vol. 8, no. 1, pp. 1–10, 2018, doi: 10.26418/bbimst.v8i1.30503.

D. Sepri, A. Fauzi, R. Wandira, O. S. Riza, Y. F. Wahyuni, and H. Hutagaol, “Prediksi Harga Cabai Merah Menggunakan Support Vector Regression,” Comput. Based Inf. Syst. J., vol. 08, no. 02, pp. 1–5, 2020.


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