Peramalan Harga Telur Ayam Ras Di Jakarta Timur Berbasis Jaringan Syaraf Tiruan

Nurfia Oktaviani Syamsiah

Abstract


Penelitian ini membahas tentang pemanfaatan jaringan syaraf tiruan untuk peramalan harga telur ayam ras di Jakarta Timur. Data harga yang digunakan adalah data time series harian.  Metode yang dipilih adalah Jaringan syaraf tiruan dengna 2 fungsi aktivasi, yakni sigmoid biner dan sigmoid bipolar dengan memanfaatkan Tools Rapidminer Studio mulai dari tahapan pertama hingga tahapan akhir. Eksperimen dilakukan dengan melakukan perubahan pada beberapa parameter neural network seperti jumlah hidden node, training cycle, learning rate maupun jumlah momentum. Penentuan hidden layer diupayakan semaksimal mungkin bertujuan untuk menghindari terjadinya permasalahan Overfitting dan Underfitting. Hasil yang dicapai, bahwasanya RMSE terkecil diperoleh dari penggunaan fungsi aktivasi sigmoid biner dengan nilai 0.033. dan arsitektur terbaik yakni 7 input, 2 hidden node dan 1 output. Penelitian ini menunjukkan hasil bahwa jaringan syaraf tiruan memberikan hasil yang cukup baik bagi peramalan data harga telur ayam ras di Jakarta Timur yang datanya bersifat time series univariat.

Kata kunci: Jaringan Syaraf Tiruan, Telur, Harga, Peramalan

Keywords


Data mining; Jaringan Syaraf Tiruan, Forecasting

Full Text:

PDF

References


Y. Nuryati and Y. H. Nur, “Variabilitas Harga Telur Ayam Ras di Indonesia,” Bul. Ilm. Litbang Perdagang., vol. 6, no. 2, pp. 235–252, 2012.

N. Ilham and Saptana, “FLUKTUASI HARGA TELUR AYAM RAS DAN FAKTOR PENYEBABNYA Fluctuations in the Prices of Chicken Eggs and Their Causes,” Anal. Kebijak. Pertan., vol. 17, no. 1, pp. 27–38, 2019.

Badan Pengkajian dan Pengembangan Perdagangan, “Analisis Perkembangan Harga Bahan Pokok di Pa sar Domestik dan Internasional,” Jakarta, 2018.

M. A. Rasyidi, “Prediksi Harga Bahan Pokok Nasional Jangka Pendek Menggunakan ARIMA,” J. Inf. Syst. Eng. Bus. Intell., vol. 3, no. 2, p. 107, 2017.

A. Tealab, H. Hefny, and A. Badr, “Forecasting of nonlinear time series using ANN,” Futur. Comput. Informatics J., vol. 2, no. 1, pp. 39–47, 2017.

S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition : 100 , 000 time series and 61 forecasting methods,” Int. J. Forecast., no. xxxx, 2019.

H. V. Nguyen, M. A. Naeem, N. Wichitaksorn, and R. Pears, “A smart system for short-term price prediction using time series models R,” Comput. Electr. Eng., vol. 76, pp. 339–352, 2019.

X. Xu, R. Law, W. Chen, and L. Tang, “Forecasting tourism demand by extracting fuzzy Takagi e Sugeno rules from trained SVMs,” CAAI Trans. Intell. Technol., vol. 1, no. 1, pp. 30–42, 2016.

A. Gouda, H. Hefny, and A. Badr, “Time Series Forecasting using Artificial Neural Networks Methodologies: A Systematic Review,” Futur. Comput. Informatics J., vol. 3, no. 2, pp. 334–340, 2018.

M. Majidpour, H. Nazaripouya, P. Chu, H. Pota, and R. Gadh, “Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System,” Forecasting, vol. 1, no. 1, pp. 107–120, 2018.

T. Phan, “Comparative Study on Univariate Forecasting Methods for Meteorological Time Series,” in 26th European Signal Processing Conference (EUSIPCO), 2018, pp. 2380–2384.

K. Y. Chen, “Combining linear and nonlinear model in forecasting tourism demand,” Expert Syst. Appl., vol. 38, no. 8, pp. 10368–10376, 2011.

N. Sylviani and A. A. Soebroto, “Peramalan Harga Pasar Telur Ayam Ras Di Kota Malang Dengan Menggunakan Metode ‘ PSO-NN ,’” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 12, pp. 6737–6745, 2018.

R. P. Destiarni, “Peramalan Harga Telur Ayam Ras pada Hari Besar Keagamaan di Pasar Jawa Timur,” Agridevina, vol. 7, no. 1, pp. 62–76, 2018.

R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” in Appears in the International Joint Conference on Arti?cial Intelligence (IJCAI), 1995, vol. 2, no. 14, pp. 1137–1143.

A. R. Kadafi, “Perbandingan Algoritma Klasifikasi Untuk Penjurusan Siswa SMA,” J. ELTIKOM, vol. 2, no. 2, pp. 67–77, 2018.

D. R. Wilson and T. R. Martinez, “The need for small learning rates on large problems,” in International Joint Conference on Neural Networks (IJCNN), 2001, vol. 1, pp. 115–119.

G. Astray, J. C. Mejuto, V. Martinez, I. Nevares, M. Alamo-sanza, and J. Simal-gandara, “Prediction Models to Control Aging Time in,” Molecules, vol. 24, no. 5, pp. 1–11, 2019.

C. W. Dawson and R. L. Wilby, “Hydrological modelling using artificial neural networks,” Prog. Phys. Geogr., vol. 1, no. 25, pp. 80–108, 2001.




DOI: https://doi.org/10.24114/cess.v5i1.15554

Article Metrics

Abstract view : 153 times
PDF - 90 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

CESS (Journal of Computer Engineering, System and Science)

Creative Commons License
CESS (Journal of Computer Engineering, System and Science) is licensed under a Creative Commons Attribution 4.0 International License