* Corresponding Author
Sistem Pendukung Keputusan Monitoring dan Peramalan Harga Beras di Kabupaten Deli Serdang, Sumatera Utara
https://doi.org/10.22146/agritech.16833 Ferlando Jubelito Simanungkalit(1*), Benika Naibaho(2)
(1) Fakultas Pertanian, Universitas HKBP Nommensen Jl. Sutomo No. 4A Medan, Sumatera Utara 20234
(2) Fakultas Pertanian, Universitas HKBP Nommensen Jl. Sutomo No. 4A Medan, Sumatera Utara 20234
(*) Corresponding Author
Abstract
The goal of this research was to design a Decision Support System (DSS) to monitor and forecast the price of rice. This system was designed to help the policy makers in decision making process to stabilize the rice price. The most fitted model base of the DSS forecasting method was selected by analyzing the architecture of Artificial Neural Network (ANN). The best fitted ANN architecture was selected based on the smallest value of Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) in training, testing, and validation. The research was done using the monthly price of rice IR64 in District Deli Serdang, North Sumatera from January 2000 to December 2015. Decision support system developing phases was used to create the best match of ANN architecture for the model base of the DSS along with the database, the knowledge base, as well as the user interface. DSS was programmed using the PHP programming and was designed in a web base to facilitate the interaction between the DSS, the system's users, and the flow of data exchange. From 73 trials unit of the ANN architecture analysis, it has been obtained that an ANN 12-1-1, purelin activation function inside the hidden layer, purelin activation function inside the output layer, traingda training algorithm (gradient descent with adaptive learning rate) and the value of learning rate was 0,1 were the best match for developing the DSS forecasting method. Furthermore, the MSE and MAPE of the training, testing and validation in a row were 0.00128 and 3.57%; 0.0319 and 5.47%; 0.0052 and 2.51%. The validation results showed that the forecasting results that has been produced by the DSS has a 90 % accuracy.
ABSTRAK
Sistem pendukung keputusan monitoring dan peramalan harga beras dirancang untuk memberikan prediksi harga masa depan dan dukungan keputusan bagi para pembuat kebijakan dalam melakukan stabilisasi harga beras. Tujuan penelitian ini adalah merancang prototipe Sistem Pendukung Keputusan (SPK) dengan terlebih dahulu menganalisis arsitektur Jaringan Saraf Tiruan (JST) yang paling sesuai untuk digunakan sebagai metode peramalan/subsistem model SPK. Kajian dilakukan dengan menggunakan data harga bulanan komoditas beras IR64 di Kabupaten Deli Serdang, Sumatera Utara bulan Januari 2000–Desember 2015. Arsitektur model JST terbaik dipilih berdasarkan pada nilai Mean Square Error (MSE) dan Mean Absolute Percentage Error (MAPE) terkecil dari hasil pelatihan, pengujian dan validasi. Arsitektur model JST terbaik kemudian dirancang menjadi subsistem model SPK bersamaan dengan basis data, komponen pengetahuan dan tampilan antarmuka menggunakan fase-fase perancangan sistem pendukung keputusan. SPK dirancang untuk digunakan berbasis web (web base) agar memudahkan interaksi dengan pengguna (user) dan arus pertukaran data. SPK diprogram menggunakan bahasa pemrograman PHP. Dari 73 percobaan analisis arsitektur model JST yang telah dilakukan, diperoleh satu arsitektur JST dengan performa peramalan terbaik yang digunakan sebagai metode peramalan dengan arsitektur 12-1-1, fungsi aktivasi purelin pada lapisan tersembunyi, fungsi aktivasi purelin pada lapisan output, algoritma pelatihan traingda (gradient descent with adaptive learning rate) dan nilai laju pembelajaran 0,1. Nilai MSE dan MAPE dari hasil pelatihan, pengujian dan validasi berturut-turut adalah 0,00128 dan 3,57%; 0,0319 dan 5,47%; 0,0052 dan 2,51%. Hasil validasi menunjukkan bahwa hasil peramalan yang dihasilkan oleh SPK memiliki tingkat akurasi 90%.
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Candea, C. ., Candea, G. ., & Filip, F. G. . (2012). IDecisionSupport - A web-based framework for decision support systems. IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 14). IFAC. https://doi.org/10.3182/20120523-3-RO-2023.00332.
Efendigil, T., Önüt, S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36(3 PART 2), 6697–6707. https://doi.org/10.1016/j.eswa.2008.08.058.
Hamzaçebi, C. (2008). Improving artificial neural networks’ performance in seasonal time series forecasting. Information Sciences, 178(23), 4550–4559. https://doi.org/10.1016/j.ins.2008.07.024.
Jakku, E., & Thorburn, P. J. (2010). A conceptual framework for guiding the participatory development of agricultural decision support systems. Agricultural Systems, 103(9), 675–682. https://doi.org/10.1016/j.agsy.2010.08.007.
Jha, G. K., & Sinha, K. (2014). Time-delay neural networks for time series prediction: An application to the monthly wholesale price of oilseeds in India. Neural Computing and Applications, 24(3–4), 563–571. https://doi.org/10.1007/s00521-012-1264-z.
Karmakar, S., Laguë, C., Agnew, J., & Landry, H. (2007). Integrated decision support system (DSS) for manure management: A review and perspective. Computers and Electronics in Agriculture, 57(2), 190–201. https://doi.org/10.1016/j.compag.2007.03.006.
Khin, A. A. A., F.C, E. C., Shamsudin, M. N., & Mohamed, Z. A. (2008). Natural rubber price forecasting in the world market. Agriculture Sustainability through Participative Global Extension, 1–13. Retrieved from http://www.fachmannhunter.com/home/wp-content/uploads/2011/05/aye-eye-khin-66.pdf.
Laboissiere, L. A., Fernandes, R. A. S., & Lage, G. G. (2015). Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Applied Soft Computing, 35, 66–74. https://doi.org/10.1016/j.asoc.2015.06.005.
Leal, I., & Melin, P. (2007). Time series forecasting of tomato prices in Mexico using modular neural networks and processing in parallel. Studfuzz, 208, 385–402.
Li, G., Xu, S., & Li, Z. (2010). Short-term price forecasting for agro-products using artificial neural networks. Agriculture and Agricultural Science Procedia, 1, 278–287. https://doi.org/10.1016/j.aaspro.2010.09.035.
Li, Z. min, Cui, L. guo, Xu, S. wei, Weng, L. yun, Dong, X. xia, Li, G. qiong, & Yu, H. peng. (2013). Prediction model of weekly retail price for eggs based on chaotic neural network. Journal of Integrative Agriculture, 12(12), 2292–2299. https://doi.org/10.1016/S2095-3119(13)60610-3.
Nainggolan, K. (2007). Perberasan sebagai bagian dari ketahanan pangan nasional. Majalah Agrimedia, 12(2), 1–10.
Panapakidis, I. P., & Dagoumas, A. S. (2016). Day-ahead electricity price forecasting via the application of artificial neural network based models. Applied Energy, 172, 132–151. https://doi.org/10.1016/j.apenergy.2016.03.089.
Pao, H.-T. (2007). Forecasting electricity market pricing using artificial neural networks. Energy Conversion and Management, 48(3), 907–912. https://doi.org/10.1016/j.enconman.2006.08.016.
Pavan, W., Fraisse, C. W., & Peres, N. A. (2011). Development of a web-based disease forecasting system for strawberries. Computers and Electronics in Agriculture, 75(1), 169–175. https://doi.org/10.1016/j.compag.2010.10.013.
Rachman, H. P. S. (2005). Metode analisis harga pangan. In Sistem Distribusi Pangan dan Harga Pangan (pp. 78–85). Bogor: Badan Ketahanan Pangan Republik Indonesia. Retrieved from http://pse.litbang.deptan.go.id/ind/pdffiles/Mono26-7.pdf.
Said, E. G., Bantacut, T., & Hasbullah, R. (2007). Manajemen rantai pasok beras dan fitur terminal agibisnis biji-bijian. Majalah Agrimedia, 12(2), 35–42.
Simanungkalit, F. J., Sutiarso, L., & Purwadi, D. (2013). Sistem pendukung keputusan berbasis jaringan syaraf tiruan untuk peramalan harga komoditas tanaman pangan. Agritech, 33(1), 70–80.
Singhal, D., & Swarup, K. S. (2011). Electricity price forecasting using artificial neural networks. International Journal of Electrical Power & Energy Systems, 33(3), 550–555. https://doi.org/10.1016/j.ijepes.2010.12.009.
Surjasa, D., Sa’id, E. G., Arifin, B., & Sukardi. (2011). Rancang bangun model prakiraan dan peringatan dini untuk pasokan dan harga beras di Propinsi DKI Jakarta menggunakan jaringan syaraf tiruan. Jurnal Teknik Industri, 1(3), 231–240.
DOI: https://doi.org/10.22146/agritech.16833
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