Penerapan Machine Learning untuk Prediksi Kenaikan Harga Beras Premium Menggunakan Algoritma Regresi Linier
Application of Machine Learning for Premium Rice Price Increase Prediction Using Linear Regression Algorithm
DOI:
https://doi.org/10.57152/malcom.v5i3.2123Keywords:
Mean Absolute Error, Prediksi Harga Beras, Regresi Linier, StreamlitAbstract
Ketidakstabilan harga beras premium sebagai komoditas pangan pokok memerlukan solusi prediksi yang akurat untuk membantu perencanaan ekonomi. Penelitian ini menerapkan algoritma Machine Learning, yaitu Regresi Linier, untuk memprediksi kenaikan harga beras premium. Model dilatih menggunakan data historis harga dan dievaluasi kinerjanya dengan metrik MAE (0.244), MSE (0.092), dan R-squared (0.893), menunjukkan tingkat akurasi yang cukup baik dalam memprediksi harga. Selanjutnya, model yang berhasil dikembangkan diimplementasikan ke dalam aplikasi web interaktif berbasis Streamlit. Aplikasi ini memungkinkan pengguna untuk memasukkan tanggal dan secara langsung mendapatkan prediksi harga beras premium. Hasil penelitian menunjukkan bahwa Regresi Linier efektif dalam memprediksi harga beras premium, dan implementasi ke dalam aplikasi Streamlit berhasil menyediakan alat prediksi yang mudah diakses. Meskipun demikian, penelitian lanjutan dapat berfokus pada peningkatan akurasi model dan eksplorasi algoritma Machine Learning lainnya untuk prediksi harga komoditas
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References
Ugi Sugiharto, “Factors Affecting Rice Prices in Indonesia (Production, Consumption, Imports, International Prices, Crop Damage),” International Journal of Economics and Management Sciences, vol. 1, no. 3, pp. 329–337, Aug. 2024, doi: 10.61132/ijems.v1i3.183.
L. R. Shaffitri, E. A. Suryana, and J. F. Sinuraya, “Market integration and rice price transmission in Indonesia,” BIO Web Conf, vol. 119, p. 02007, Jul. 2024, doi: 10.1051/bioconf/202411902007.
M. K. Afkar, M. Wali, and Imilda, “Aplikasi Prediksi Produksi Cabai dengan Algoritma C.45 untuk Dinas Pertanian Provinsi Aceh Berbasis Web,” Jurnal Ilmu Komputer dan Teknologi Informasi, vol. 1, no. 1, pp. 1–13, Mar. 2024, doi: 10.35870/jikti.v1i1.732.
Respatiwulan, D. Prabandari, Y. Susanti, S. S. Handayani, and Hartatik, “The stochastic model of rice price fluctuation in Indonesia,” J Phys Conf Ser, vol. 1217, no. 1, p. 012107, May 2019, doi: 10.1088/1742-6596/1217/1/012107.
D. Maulud and A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning,” Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 140–147, Dec. 2020, doi: 10.38094/jastt1457.
K. F. Nimon and F. L. Oswald, “Understanding the Results of Multiple Linear Regression,” Organ Res Methods, vol. 16, no. 4, pp. 650–674, Oct. 2013, doi: 10.1177/1094428113493929.
D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to linear regression analysis. John Wiley & Sons, 2021.
Y. Zhao, “Stock price prediction based on linear regression,” Theoretical and Natural Science, vol. 84, no. 1, pp. 85–90, Feb. 2025, doi: 10.54254/2753-8818/2025.21203.
I. Journal, “Forecasting Commodity Prices,” Interantional Journal Of Scientific Research In Engineering And Management, vol. 08, no. 01, pp. 1–13, Jan. 2024, doi: 10.55041/ijsrem28035.
J. Bi, E. Li, and Y. Luo, “Petroleum Price Prediction Based on the Linear Regression and Random Forest,” Applied and Computational Engineering, vol. 8, no. 1, pp. 292–296, Aug. 2023, doi: 10.54254/2755-2721/8/20230170.
S. D. Saputra and A. D. Widiantoro, “BBCA Stock Price Prediction Using Linear Regression Method,” International Journal of Artificial Intelligence and Science, vol. 1, no. 1, pp. 25–36, Sep. 2024, doi: 10.63158/IJAIS.v1.i1.7.
Azhar Dyo Pramono, Herlina Latipa Sari, and Ila Yati Beti, “Penerapan Regresi Linear dalam Perkiraan Harga Beras di Kota Bengkulu,” VISA: Journal of Vision and Ideas, vol. 4, no. 2, Jul. 2024, doi: 10.47467/visa.v4i2.3662.
L. H. Hasibuan and S. Musthofa, “Penerapan Metode Regresi Linear Sederhana Untuk Prediksi Harga Beras di Kota Padang,” JOSTECH: Journal of Science and Technology, vol. 2, no. 1, pp. 85–95, Mar. 2022, doi: 10.15548/jostech.v2i1.3802.
I. R. Muchtar and A. Afiyati, “Comparison of Linear Regression and Random Forest Algorithms for Premium Rice Price Prediction (Case Study: West Java),” Jurnal Indonesia Sosial Teknologi, vol. 5, no. 7, pp. 3122–3132, Jul. 2024, doi: 10.59141/jist.v5i7.1184.
M. Hanif, M. Abdurohman, and A. G. Putrada, “Rice consumption prediction using linear regression method for smart rice box system,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 4, pp. 284–288, Oct. 2020, doi: 10.14710/jtsiskom.2020.13353.
Rahmat Hidayat and Irawan Wibisonya, “Rice Price Prediction with Long Short-Term Memory (LSTM) Neural Network,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 5, pp. 658–664, Oct. 2024, doi: 10.29207/resti.v8i5.6041.
M. Benchekroun, B. Chevallier, V. Zalc, D. Istrate, D. Lenne, and N. Vera, “The Impact of Missing Data on Heart Rate Variability Features: A Comparative Study of Interpolation Methods for Ambulatory Health Monitoring,” IRBM, vol. 44, no. 4, p. 100776, Aug. 2023, doi: 10.1016/j.irbm.2023.100776.
T. Decorte et al., “Missing Value Imputation of Wireless Sensor Data for Environmental Monitoring,” Sensors, vol. 24, no. 8, p. 2416, Apr. 2024, doi: 10.3390/s24082416.
A. M. Alonso, A. E. Sipols, and S. Quintas, “A single-index model procedure for interpolation intervals in time series,” Comput Stat, vol. 28, no. 4, pp. 1463–1484, Aug. 2013, doi: 10.1007/s00180-012-0355-8.
M. Sivakumar, S. Parthasarathy, and T. Padmapriya, “Trade-off between training and testing ratio in machine learning for medical image processing,” PeerJ Comput Sci, vol. 10, p. e2245, Sep. 2024, doi: 10.7717/peerj-cs.2245.
V. Kholiev and O. Barkovska, “Analysis of the of training and test data distribution for audio series classification,” Information and control systems at railway transport, vol. 28, no. 1, pp. 38–43, Mar. 2023, doi: 10.18664/ikszt.v28i1.276343.
X. Su, X. Yan, and C. Tsai, “Linear regression,” WIREs Computational Statistics, vol. 4, no. 3, pp. 275–294, May 2012, doi: 10.1002/wics.1198.
D. Maulud and A. M. Abdulazeez, “A Review on Linear Regression Comprehensive in Machine Learning,” Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 140–147, Dec. 2020, doi: 10.38094/jastt1457.
I. R. Muchtar and A. Afiyati, “Comparison of Linear Regression and Random Forest Algorithms for Premium Rice Price Prediction (Case Study: West Java),” Jurnal Indonesia Sosial Teknologi, vol. 5, no. 7, pp. 3122–3132, Jul. 2024, doi: 10.59141/jist.v5i7.1184.
M. L. Zakaria, S. A. Wibowo, and I. Kurniawan, “Implementation of Temporal Fusion Transformer Optimized by Grey Wolf Optimizer In Predicting Rice Price In Bandung Regency,” in 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), IEEE, Aug. 2024, pp. 127–132. doi: 10.1109/ICITISEE63424.2024.10730751.
I. N. G. A. M. Wardhiana et al., “Comparative Study of Statistical, Machine Learning, and Deep Learning for Rice Retail Price Forecasting in West Java,” in 2024 2nd International Conference on Technology Innovation and Its Applications (ICTIIA), IEEE, Sep. 2024, pp. 1–6. doi: 10.1109/ICTIIA61827.2024.10761587.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, p. e623, Jul. 2021, doi: 10.7717/peerj-cs.623.
E. Fradinata, S. Suthummanon, and W. Suntiamorntut, “Forecasting Determinant of Cement Demand in Indonesia with Artificial Neural Network,” Journal of Asian Scientific Research, vol. 5, no. 7, pp. 373–384, 2015, doi: 10.18488/journal.2/2015.5.7/2.7.373.384.
C. Wang et al., “Predicting Plant Growth and Development Using Time-Series Images,” Agronomy, vol. 12, no. 9, p. 2213, Sep. 2022, doi: 10.3390/agronomy12092213.
J. M. Nápoles-Duarte, A. Biswas, M. I. Parker, J. P. Palomares-Baez, M. A. Chávez-Rojo, and L. M. Rodríguez-Valdez, “Stmol: A component for building interactive molecular visualizations within streamlit web-applications,” Front Mol Biosci, vol. 9, Sep. 2022, doi: 10.3389/fmolb.2022.990846.
L. Setivani, H. H. Handayani, and W. A. Geraldine, “Rice Price Forecasting Using GridSearchCVand LSTM,” in 2023 International Conference on Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), IEEE, Nov. 2023, pp. 127–131. doi: 10.1109/ICMERALDA60125.2023.10458179.
M. P. Keerthi, G. S. Reddy, V. S. Raghava, and K. B. Reddy, “Streamlit Interface for Multiple Disease Diagnosis,” Int J Res Appl Sci Eng Technol, vol. 11, no. 2, pp. 1159–1164, Feb. 2023, doi: 10.22214/ijraset.2023.49166.
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