• Muhammad Ali Ridla AMIK Ibrahimy
Keywords: forecasting sales neural network, attribute weight with pso


The lubricating oil industry is one part of the oil and gas sector which is still one of the main pillars of economic growth in Indonesia. Sales predictions are needed by companies and policy makers as planning materials and economic development strategies to increase income in the future. Predictions that have a better level of accuracy can provide appropriate decisions. Various methods have been used, the Artificial Neural Network algorithm is one of the most widely used, especially in the Backpropagation (BPNN) structure which can predict non linear time series data. Backpropagation has been proven to have a better level of accuracy compared to econometric methods such as ARIMA. The integration of Backpropagation algorithm with other algorithms needs to be done to overcome the shortcomings and improve the ability of the National Land Agency itself. Particle Swarm Optimization (PSO) which is used as an optimization determinant of attribute weight values in the network structure of BPNN shows good results. After testing, BPNN without PSO has a Squared Error (SE) level of 0.012 and a Root Mean Aquared Error (RMSE) of 0.111. While BPNN with PSO has SE levels of 0.004 and RMSE of 0.059. This shows that there is a significant decrease in the error rate after the PSO algorithm is added to the BPNN structure which is 46.85%.


[1] Darmanto, “Mengenal Pelumas Pada Mesin,” Momentum, vol. 7, no. 1, pp. 5–10, 2011.
[2] M. Arisandi, Darmanto, and T. Priangkoso, “Analisa Pengaruh Bahan Dasar Pelumas Terhadap Viskositas Pelumas dan Konsumsi Bahan Bakar,” Momentum, vol. 8, no. 1, pp. 56–61, 2012.
[3] A. Singh and G. C. Mishra, “Application of Box-Jenkins Method and Artificial Neural Network Procedure for Time Series Forecasting of Prices,” vol. 16, no. 1, pp. 83–96, 2015.
[4] T. G. Laksana, “Perbandingan Algoritma Neural Network (NN) dan Support Vector Machines (SVM) dalam Peramalan Penduduk Miskin di Indonesia,” Inf. Comput. Technol., vol. 1, no. 1, pp. 49–58, 2013.
[5] H. G. Nugraha and A. S. N., “Optimasi Bobot Jaringan Syaraf Tiruan Mengunakan Particle Swarm Optimization,” Indones. J. Comput. Cybern. Syst., vol. 8, no. 1, pp. 25–36, 2014.
[6] H. N. A. Hamed, S. M. Shamsuddin, and N. Salim, “Particle Swarm Optimization for Neural Network,” J. Teknol. UTM, vol. 49, no. D, pp. 13–26, 2008.
[7] G. K. Jha, P. Thulasiraman, and R. K. Thulasiram, “PSO based neural network for time series forecasting,” 2009 Int. Jt. Conf. Neural Networks, pp. 1422–1427, 2009.
[8] X. Hu and R. Eberhart, “Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization,” Optimization, vol. 2, no. 1, pp. 1677–1681, 2002.
[9] J. Malik, R. Mishra, and I. Singh, “PSO-ANN Approach For Estimating Drilling Induced Damage In Cfrp Laminates,” Adv. Prod. Eng. Manag. APEM, vol. 6, no. 2, pp. 95–104, 2011.
[10] N. Susanti, “Penerapan Model Neural Network Backpropagation untuk Prediksi Harga Ayam,” Seminar Nasional Teknologi Industri dan Informatika (SNATIF), pp. 325–332, 2014.
[11] S. Ruliah and R. Rolyadely, “Prediksi Pemakaian Listrik Dengan Pendekatan Back Propagation,” J. Tek. Inform. dan Sist. Inf., vol. 3, no. 1, pp. 465–476, 2014.
[12] D. R. M. Gor, Industrial Statistic and Operational Management; 6. Forecasting Techniques. 2015.
[13] C. Gershenson, “Artificial Neural Networks for Beginners,” University of Sussex Brighton United Kingdom, vol. cs.NE/0308, p. 8, 2003.
[14] E. Prasetyo, Data Mining, Mengolah data menjadi informasi menggunakan Matlab. Yogyakarta: Andi Publisher, 2014.
[15] S. Kusumadewi, Membangun Jaringan Syaraf Tiruan Menggunakan MATLAB & EXCEL LINK. Yogyakarta: Graha Ilmu, 2004.
[16] T.-S. Park, J.-H. Lee, and B. Choi, “Optimization for Artificial Neural Network with Adaptive inertial weight of particle swarm optimization,” 2009 8th IEEE Int. Conf. Cogn. Informatics, pp. 481–485, 2009.
[17] Purwanto, C. Eswaran, and R. Logeswaran, “Improved Adaptive Neuro-Fuzzy Inference System for HIV/AIDS Time Series Prediction,” Informatics Eng. Inf. Sci. Pt Iii, vol. 253, pp. 1–13, 2011.