Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity
DOI:
https://doi.org/10.22219/JTIUMM.Vol20.No2.152-160Keywords:
Jaringan Syaraf Tiruan, Peramalan, Model Prediksi, Ladang Garam, Manajemen Rantai PasokAbstract
In recent years, Indonesia needs import millions of tons of salt to satisfy domestic industries' demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity, and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method was used to develop a model based on data collected from Sumenep Madura Indonesia. The model analysis used the complete experimental factorial design to determine the effect of the ANN parameter differences. Furthermore, the selected model performance compared with the estimate predictor of Holt-Winters. The results presented that ANN-based models were more accurate and efficient for predicting salt field productivity.
Downloads
References
R. F. Aji and W. M. Suryaningrat, "Faktor-faktor yang mempengaruhi permintaan dan efektivitas kebijakan impor garam indonesia/Ahmad Syariful jamil, Netty Tinaprilla, Suharno," Policy, vol. 534, p. 546, 2017.
M. Effendy and M. Zainuri, "Intensifikasi Lahan Garam Rakyat Di Kabupaten Sumenep. Program Studi Ilmu Kelautan Universitas Trunojoyo Madura
" pp. 22-43, 2014.
Y. Adiraga and A. H. Setiawan, "Analisis Dampak Perubahan Curah Hujan, Luas Tambak Garam Dan Jumlah Petani Garam Terhadap Produksi USAha Garam Rakyat Di Kecamatan Juwana Kabupaten Pati Periode 2003-2012," Diponegoro Journal of Economics, vol. 3, pp. 41-53, 2014. https://ejournal3.undip.ac.id/index.php/jme/article/view/5314.
I. R. Suhelmi and P. Indonesia. Badan Penelitian dan Pengembangan Kelautan dan, Garam Madura: tradisi dan potensi usaha garam rakyat: Badan Penelitian dan Pengembangan Kelautan dan Perikanan, Kementerian Kelautan dan Perikanan, 2013..
C. Satryo, "Free Trade Intervention: Political-Economic Intervention on Global Business Environment," Asia-Pacific Management and Business Application, vol. 7, pp. 21-30, 2018. http://doi.org/10.21776/ub.apmba.2018.007.01.3.
S. Chopra and P. Meindl, "Supply chain management. Strategy, planning & operation," in Das summa summarum des management, ed: Springer, 2007, pp. 265-275. https://doi.org/10.1007/978-3-8349-9320-5_22.
F. Ibrahim, "Integrated Supply Chain Contract Model With Drop-Shipper Players," Jurnal Teknik Industri, vol. 19, pp. 58-72, 2018. https://doi.org/10.22219/JTIUMM.Vol19.No1.58-72.
I. Masudin and M. S. Kamara, "Electronic Data Interchange and Demand Forecasting Implications on Supply Chain Management Collaboration: A Customer Service Perspective," Jurnal Teknik Industri, vol. 18, pp. 138-148, 2017. https://doi.org/10.22219/JTIUMM.Vol18.No2.138-148.
I. Masudin and M. S. Kamara, "Impact Of Just-In-Time, Total Quality Management And Supply Chain Management On Organizational Performance: A Review Perspective," Jurnal Teknik Industri, vol. 19, pp. 11-20, 2018. https://doi.org/10.22219/JTIUMM.Vol19.No1.11-20.
S. Dharmayanti, S. Suharno, and A. Rifin, "Analisis Ketersediaan Garam Menuju Pencapaian Swasembada Garam Nasional Yang Berkelanjutan (Suatu Pendekatatan Model Dinamik)," Jurnal Sosial Ekonomi Kelautan dan Perikanan, vol. 8, pp. 103-115, 2013. http://dx.doi.org/10.15578/jsekp.v8i1.1201
S. Herman, E. Noor, and D. Mulyadi, "Identifikasi Faktor Kunci Krisis Pada Tataniaga Garam Konsumsi Di Indonesia Menggunakan Proses Jejaring Analitik (Analytic Network Process," Journal of Industrial Research (Jurnal Riset Industri), vol. 8, 2014. http://ejournal.kemenperin.go.id/jri/article/view/154/139.
A. S. Perbangsa, W. Wijaya, A. Amelia, and B. Pardamean, "Model of web-based supply chain management system for salt industry," in 2017 International Conference on Information Management and Technology (ICIMTech), 2017, pp. 232-237. https://doi.org/10.1109/ICIMTech.2017.8273543.
D. Chen and D. Ware, "A neural network model for forecasting fish stock recruitment," Canadian Journal of Fisheries and Aquatic Sciences, vol. 56, pp. 2385-2396, 1999. https://doi.org/10.1139/f99-178.
A. Ihwan, "Metode Jaringan Saraf Tiruan Propagasi Balik Untuk Estimasi Curah Hujan Bulanan di Ketapang Kalimantan Barat," Prosiding SEMIRATA 2013, vol. 1, 2013. http://jurnal.fmipa.unila.ac.id/semirata/article/view/743/562.
G. Zhang, B. E. Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks:: The state of the art," International journal of forecasting, vol. 14, pp. 35-62, 1998. https://doi.org/10.1016/S0169-2070(97)00044-7.
M. Lydia, S. Suresh Kumar, A. Immanuel Selvakumar, and G. Edwin Prem Kumar, "Linear and non-linear autoregressive models for short-term wind speed forecasting," Energy Conversion and Management, vol. 112, pp. 115-124, 2016. https://doi.org/10.1016/j.enconman.2016.01.007.
S. Ferlito, M. Atrigna, G. Graditi, S. De Vito, M. Salvato, A. Buonanno, et al., "Predictive models for building's energy consumption: An Artificial Neural Network (ANN) approach," in 2015 xviii aisem annual conference, 2015, pp. 1-4. https://doi.org/10.1109/AISEM.2015.7066836.
R. P. S. Hermanto and A. Nugroho, "Waiting-Time Estimation in Bank Customer Queues using RPROP Neural Networks," Procedia Computer Science, vol. 135, pp. 35-42, 2018. https://doi.org/10.1016/j.procs.2018.08.147.
S. Nissen, "Implementation of a fast artificial neural network library (fann)," Report, Department of Computer Science University of Copenhagen (DIKU), vol. 31, p. 29, 2003. http://fann.sourceforge.net/fann.pdf.
C. Igel and M. Hüsken, "Empirical evaluation of the improved Rprop learning algorithms," Neurocomputing, vol. 50, pp. 105-123, 2003. https://doi.org/10.1016/S0925-2312(01)00700-7.
J. Heaton, Introduction to neural networks with Java: Heaton Research, Inc., 2008.
T. Masters, Practical neural network recipes in C++: Morgan Kaufmann, 1993.
L.-F. Hsieh, S.-C. Hsieh, and P.-H. Tai, "Enhanced stock price variation prediction via DOE and BPNN-based optimization," Expert Systems with Applications, vol. 38, pp. 14178-14184, 2011. https://doi.org/10.1016/j.eswa.2011.04.229.
E. T. Tan, J. R. Tang, and N. A. M. Isa, "Applying Design of Experiment to Optimise Artificial Neural Network for Classification of Cervical Cancer," Journal of Engineering Science, vol. 12, p. 65, 2016. http://web.usm.my/jes/12_2016/JES%20Vol.%2012%202016%20-%20Art.%206(65-75).pdf.
F. J. Pontes, G. Amorim, P. P. Balestrassi, A. Paiva, and J. R. Ferreira, "Design of experiments and focused grid search for neural network parameter optimization," Neurocomputing, vol. 186, pp. 22-34, 2016. https://doi.org/10.1016/j.neucom.2015.12.061.
R. J. Hyndman, "Another look at forecast-accuracy metrics for intermittent demand," Foresight: The International Journal of Applied Forecasting, vol. 4, pp. 43-46, 2006. https://econpapers.repec.org/RePEc:for:ijafaa:y:2006:i:4:p:43-46.
A. Š. Tomić, D. Antanasijević, M. Ristić, A. Perić-Grujić, and V. Pocajt, "Application of experimental design for the optimization of artificial neural network-based water quality model: a case study of dissolved oxygen prediction," Environmental Science and Pollution Research, vol. 25, pp. 9360-9370, 2018. https://doi.org/10.1007/s11356-018-1246-5.
L. F. Tratar and E. Strmčnik, "The comparison of Holt–Winters method and Multiple regression method: A case study," Energy, vol. 109, pp. 266-276, 2016. https://doi.org/10.1016/j.energy.2016.04.115.
V. C. Sugiarto, R. Sarno, and D. Sunaryono, "Sales forecasting using Holt-Winters in Enterprise Resource Planning at sales and distribution module," in 2016 International Conference on Information & Communication Technology and Systems (ICTS), 2016, pp. 8-13. https://doi.org/10.1109/ICTS.2016.7910264.
D. Fang, Y. Zhang, and K. Spicher, "Forecasting accuracy analysis based on two new heuristic methods and Holt-Winters-Method," in 2016 IEEE International Conference on Big Data Analysis (ICBDA), 2016, pp. 1-6. https://doi.org/10.1109/ICBDA.2016.7509814.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Indra Cahyadi, Heri Awalul Ilhamsah, Ika Deefi Anna
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
