K-Nearest Neighbor for Gorontalo City Chili Price Prediction Using Feature Selection, Backward Elimination, and Forward Selection
DOI:
https://doi.org/10.35870/ijsecs.v3i3.1709Keywords:
Chili Price Prediction, K-Nearest Neighbor (K-NN), Feature Selection, Market Demand, Forecasting AccuracyAbstract
This study addresses chili price volatility, an important concern that impacts the national economy and societal welfare. Fluctuations in chili prices in the retail market greatly influence market demand, thereby influencing farming decisions, especially chili cultivation. To help make better decisions, Researchers use forecasting, which is defined as the projection of future trends based on the analysis of historical data, using statistical methods. The K-Nearest Neighbor (K-NN) algorithm is used because of its resistance to high noise on large training datasets. However, challenges arise in determining the optimal value of 'k' and selecting related attributes. To overcome this, Feature Selection is applied to refine the model by removing irrelevant features, resulting in a significant reduction in the model error rate. This improvement indicates an increase in the efficiency of the K-NN algorithm with the incorporation of Feature Selection. Our findings show that the model, with backward elimination in Feature Selection, achieves a Root Mean Square Error (RMSE) of 0.202, outperforming the model using forward selection. The prediction accuracy of this model reaches an average of 78.86%, which is much higher than the baseline data of 50%. This shows the success of the proposed method in predicting chili prices.
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Soewignyo, F. and Simatupang, N., 2020. PENGARUH PERUBAHAN HARGA KOMODITAS PERTANIAN TERHADAP KESEJAHTERAAN PETANI DI PROPINSI SULAWESI UTARA. Klabat Accounting Review, 1(1), pp.14-26. DOI: https://doi.org/10.60090/kar.v1i1.454.14-26.
Suma, D.V., 2020. Data mining based prediction of demand in Indian market for refurbished electronics. Journal of Soft Computing Paradigm, 2(2), pp.101-110.
Bode, A., 2017. K-nearest neighbor dengan feature selection menggunakan backward elimination untuk prediksi harga komoditi kopi arabika. ILKOM Jurnal Ilmiah, 9(2), pp.188-195. DOI: https://doi.org/10.33096/ilkom.v9i2.139.188-195.
Wanto, A. and Windarto, A.P., 2017. Analisis prediksi indeks harga konsumen berdasarkan kelompok kesehatan dengan menggunakan metode backpropagation. Sinkron: jurnal dan penelitian teknik informatika, 2(2), pp.37-43.
Hadiansyah, F.N., 2017. Prediksi Harga Cabai dengan Menggunakan pemodelan Time Series ARIMA. Indonesia Journal on Computing (Indo-JC), 2(1), pp.71-78. DOI: https://doi.org/10.21108/INDOJC.2017.2.1.144.
Fatkhuroji, F., Santosa, S. and Pramunendar, R.A., 2019. Prediksi Harga Kedelai Lokal Dan Kedelai Impor Dengan Metode Support Vector Machine Berbasis Forward Selection. Jurnal Cyberku, 15(1), pp.61-76.
Utomo, P.B., Utami, E. and Raharjo, S., 2019. Implementasi Metode K-Nearest Neighbor Dan Regresi Linear Dalam Prediksi Harga Emas. Informasi Interaktif, 4(3), pp.155-159.
Lasulika, M.E. and Bode, A., 2021. Komparasi Algoritma Data Mining Menggunakan Forward Selection pada Prediksi Harga Jagung. JURNAL TECNOSCIENZA, 5(2), pp.157-172. DOI: https://doi.org/10.51158/tecnoscienza.v5i2.392.
Karo, I.M.K., Khosuri, A., Septory, J.S.I. and Supandi, D.P., 2022. Pengaruh Metode Pengukuran Jarak pada Algoritma k-NN untuk Klasifikasi Kebakaran Hutan dan Lahan. Jurnal Media Informatika Budidarma, 6(2), pp.1174-1182. DOI: http://dx.doi.org/10.30865/mib.v6i2.3967.
Bode, A., 2019. Perbandingan metode prediksi support vector machine dan linear regression menggunakan backward elimination pada produksi minyak kelapa. Simtek: jurnal sistem informasi dan teknik komputer, 4(2), pp.104-107. DOI: https://doi.org/10.51876/simtek.v4i2.57.
Harafani, H. and Al-Kautsar, H.A., 2021. Meningkatkan Kinerja K-Nn Untuk Klasifikasi Kanker Payudara Dengan Forward Selection. Jurnal Pendidikan Teknologi Dan Kejuruan, 18(1), pp.99-110. DOI: https://doi.org/10.23887/jptk-undiksha.v18i1.29905.
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