Prediksi Harga Bawang Merah menggunakan Algoritma Fuzzy Inference System (FIS)
DOI:
https://doi.org/10.30865/resolusi.v3i4.677Keywords:
Shallot Price; Fuzzy Inference System; Sugeno Method; RMSE; MAPEAbstract
Consumption of shallots in Indonesia is still relatively large. This affects price movements, production to market needs. Associated with changes in the amount of production with public consumption needs affect the price variations in each period. Shallot price conditions in the market that experience changes can affect losses or profits for shallot entrepreneurs. This creates problems in the sale of bawal because the price of onions is difficult to predict. To minimize losses, a system or technology is needed that can help predict shallot prices. As an illustration of the shallot entrepreneurs. Shallot price prediction system can be done using the calculation method "Algorithm Fuzzy Inference System (FIS) Sugeno method". The use of this algorithm does not require independent assumptions, homoscedasticity, and normally distributed residuals which are often not found in the data so that this method is considered suitable for predicting data that has extreme values. The price of shallots is influenced by two variables, namely the amount needed by the amount of market demand. The test results show a Mean Square Error (MSE) value of 137.671697. then the Root Mean Square Error (RMSE) value is the result of the square of the Mean Square Error (MSE) value, namely: 1.0541 The Mean Absolute Percentage Error (MAPE) value which has an error rate of 40%.
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References
Putri Hayati Rahmi, “Analisis Trend dan Estimasi Harga Bawang Merah di Kabupaten Banyumas Periode Januari 2008-Desember 2017.,” Univ. Muhammadiyah Purwokerto, vol. 11, no. 1, pp. 65–69, 2017.
Menteri Pertanian, “Program Kementerian Pertanian TA 2022,” pp. 1–7, 2022.
S. Nasional, F. Pertanian, and U. Jambi, “tahun 2018,” pp. 591–604, 2018.
N. Hendiyani and A. W. Sugiyarto, “Prediksi Harga Bawang Merah Rata-Rata Perbulan Menggunakan Logika Fuzzy Metode Tsukamoto,” Semin. Mat. dan Pendidik. Mat., pp. 1–7, 2019.
N. M. Sunariadi, P. K. Intan, D. C. R. Novitasari, and Y. Hariningsih, “Prediksi Produksi Bawang Merah Di Kabupaten Nganjuk Dengan Metode Seasonal Arima (Sarima),” Transform. J. Pendidik. Mat. dan Mat., vol. 6, no. 1, pp. 49–60, 2022.
R. Ardiansyah, R. Jaya, and C. H. Rahmi, “Prediksi Pasokan Bawang Merah Mendukung Desain Pengembangan Agroindustri Di Provinsi Aceh,” J. Teknol. Ind. Pertan., vol. 31, no. 1, pp. 46–52, 2021.
P. Rani and A. Raj, “Fermatean fuzzy Einstein aggregation operators-based MULTIMOORA method for electric vehicle charging station selection,” Expert Syst. Appl., vol. 182, no. May, p. 115267, 2021.
A. Astrilyana and N. Afni, “Penerapan Metode Fuzzy Inference System (Fis) Dalam Membuat Model Penilaian Pemahaman Mata Pelajaran Pemrograman Web,” None, vol. 13, no. 2, pp. 281–288, 2017.
D. Virdaus and P. T. Prasetyaningrum, “Penerapan Data Mining Untuk Memprediksi Harga Bawang Merah Di Yogyakarta Menggunakan Metode K-Nearest Neighbor,” J. …, no. 84, pp. 1–8, 2020.
H. Afridar, … G. G.-I. J. of, and undefined 2022, “Penerapan Metode ARIMA untuk Prediksi Harga Komoditi Bawang Merah di Kota Tegal,” Journal.Peradaban.Ac.Id, vol. 3, no. 2, pp. 18–29, 2023.
R. Faulina and Suhartono, “Hybrid ARIMA-ANFIS for Rainfall Prediction in Indonesia,” Int. J. Sci. Res., vol. 2, no. 2, pp. 159–162, 2013.
J.-S. Roger Jang, “01_NeuroFuzzyApproach.pdf.” p. 614, 2000.
R. Adha, N. Nurhaliza, U. Sholeha, and M. Mustakim, “Perbandingan Algoritma DBSCAN dan K-Means Clustering untuk Pengelompokan Kasus Covid-19 di Dunia,” SITEKIN J. Sains, Teknol. dan Ind., vol. 18, no. 2, pp. 206–211, 2021.
G. Andriani, L. Mahfiroh, D. C. R. Novitasari, N. Ulinnuha, and Y. Farida, “Aplikasi Fuzzy Inference System Dengan Metode Mamdani Untuk Menentukan Status Gizi Balita Di Kota Surabaya,” J. Unirow, vol. 01, no. 01, pp. 1–6, 2019.
S. Purnama, K. Firdausy, and A. Yudhana, “Sistem Pakar Pendeteksi Kerusakan Mesin Motor Menggunakan Borland Delphi 7,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 5, no. 1, p. 33, 2007.
S. Wasista, B. S. B. D, and S. A. Putra, “Sistem Pengenalan Wajah Pada Mesin Absensi Mahasiswa Menggunakan Metode PCA Dan DTW,” 13th Ind. Electron. Semin. 2011 (IES 2011), vol. 2011, no. Ies, pp. 224–229, 2011.
D. Vinsensia and Y. Utami, “Penerapan Fuzzy Inference System (FIS) Metode Mamdani dalam Pemilihan Jurusan Perguruan Tinggi,” Publ. J. Penelit. Tek. Inform., vol. 2, no. 2, pp. 28–36, 2018.
K. Harefa, “Penerapan Fuzzy Inference System untuk Menentukan Jumlah Pembelian Produk Berdasarkan Data Persediaan dan Penjualan,” J. Inform. Univ. Pamulang, vol. 2, no. 4, p. 205, 2017.
A. Hendrani, N. U. Hasibuan, and D. Septyanto, “The effect of the roa, audit committee, and the company size on tax avoidance (metal and the like) listed on indonesia stock exchange (idx) period 2014 - 2018,” Pros. ICSMR, vol. 1, no. 1 SE-Articles, pp. 85–101, 2020.
R. P. Trevino, T. J. Lamkin, R. Smith, S. A. Kawamoto, and H. Liu, “Maximum Distance Minimum Error ( MDME ): A Non-Parametric Approach to Feature Selection for Image-based High Content Screening Data,” no. September, 2017.
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