FORECASTING OF VETIVER PRICES: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK METHOD 1)

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1 58 Indonesian Journal of Agriculture 1(1), 28: Chandra Indrawanto et al. FORECASTING OF VETIVER PRICES: AN APPLICATION OF ARTIFICIAL NEURAL NETWORK METHOD 1) Chandra Indrawanto a), Eriyatno b), Anas M. Fauzi b), Machfud b), Sukardi b), and Noer Soetrisno b) a) Indonesian Center for Estate Crops Research and Development, Jalan Tentara Pelajar No. 1 Bogor b) Faculty of Agricultural Technology, Bogor Agricultural University, Kampus IPB Darmaga, Bogor 1668 ABSTRACT Vetiver and vetiver oil price forecasting with artificial neural network method has been done. Time series data from January 2 to August 26 were used to forecast the prices for 24 months ahead. The best result for forecasting of vetiver prices was derived using binary sigmoid activation in hidden layer, bipolar sigmoid activation in output layer, and transformation data spread (,1). The best result for forecasting of vetiver oil prices was obtained using bipolar sigmoid activation in hidden layer, binary sigmoid activation in output layer, and transformation data spread (,1). The results showed that the average forecasting prices of vetiver and vetiver oil in 27 and 28 were higher than the prices needed for vetiver farming and vetiver oil industry to reach break event point. [Keywords: Vetiveria zizanioides, prices, forecasting, artificial neural network] INTRODUCTION Indonesia has a lot of variety of essential crops. This condition makes essential oil industry is potential to be developed in the country. The development of essential oil industry also has a multiplier effect in increasing the income of essential crops farmers. Vetiver oil industry is one of the essential oil industries that has been developed in Indonesia. Vetiver oil is utilized as a raw material in making perfume, detergent, soap, and the compounding of vetiver oil with patchouli oil can be used to drive away moths (Sabini 26). Indonesia s export of vetiver oil in last five years was around 8 t/year equal to 9% of Indonesia s vetiver oil production (Statistic Indonesia of the Republic of Indonesia 26). Share of Indonesia in vetiver oil international market is around 25%. With this size of market share, Indonesia can not be a price leader in vetiver oil international market. 1) Article in bahasa Indonesia has been published in Jurnal Penelitian Tanaman Industri Vol. 13 No. 1, 27, p The price of vetiver oil in Indonesia is fluctuated and influenced by the price of the oil in international market. This price fluctuation makes the profits in vetiver farming and vetiver oil processing are also fluctuating. This condition will increase the risk, decrease the number of financial institution that will finance the industry and then decrease the performance of Indonesia s vetiver industry. Wahyudi and Wulandari (26) reported that the external factors such as price fluctuation could give a negative impact to the industry performance. Forecasting of vetiver and vetiver oil prices will help the farmers, agroindustry entrepreneurs, and financial institutions to estimate the profit that will be gained in vetiver farming or vetiver oil industry. Information on price estimation is important in preparing necessary actions to avoid loss or to get potential opportunities in vetiver farming and/or vetiver oil industry. A research aimed to forecast vetiver and vetiver oil prices. MATERIALS AND METHODS Types and Sources of Data Time series data of the vetiver and vetiver oil prices from January 2 to August 26 were used in this research. The data were collected from Agricultural Office and Industrial Office of the Garut Regency, West Java. More than 9% of Indonesia s vetiver production is come from Garut Regency. Analysis Methods Artificial neural network (ANN) method was used to forecast the vetiver and vetiver oil prices. The method has been used in forecasting the price of palm oil (Salya 26), the profit of stocks (Zhang et al. 24), and energy consumption (McMenamin and Monforte 1998). ANN method is an information processing system that has

2 Forecasting of vetiver price performance characteristics in common with biological neural networks (Marimin 25). Through training process, ANN can memorize the patron of price fluctuation in the last time and use that patron to forecast the price. ANN is characterized by (1) its pattern of connection between the neurons (called its architecture), (2) its method of determining the weights on the connections (called its training algorithm), and (3) its activation function (Fausett 1994). A single-layer net has one layer of connection weights. Often, the units can be distinguished as input units, which received signals from outside world, and output units, from which the response of the net can be read. A multiplier net is a net with one or more layers of nodes (or called hidden units) between input units and output units (Rumelhart et al. 1986). Multilayer net can solve more complicated problems than the single layer net, but training may be more difficult (Haykin 1999). The net architecture is also influenced by the problems that will be solved. If the inputs or the outputs have a big dimension, the net needs more layers of nodes (Siang 25). Training algorithm is a method of setting the values of connection weights. Two types of training are supervised training and unsupervised training. Supervised training is accomplished by presenting a sequence of training vectors each with an associated target output vector. The difference in net output values and target output values is used to correct the connection weights. This training algorithm is called back-propagation. In unsupervised training, there is not any target output vectors. The changes in connection weights are based on some parameters. The basic operation of ANN involves summing its weighted input signal and applying an output, or activation function. The most common activation functions are purelin or identity function, binary sigmoid or logistic sigmoid function, and bipolar sigmoid or hyperbolic tangent function. In binary sigmoid, the data range between (,1), while in bipolar sigmoid, the data range between (-1,1). The input data then has to be transformed first to that range of data. Back-propagation training algorithm in multilayer net with one layer of nodes or hidden units is as follows: 1. Presenting the input vectors (x) and target output vectors (T). 2. Initializing connection weights (w ij ), fixed training rate (lr) and number of input units, output units and hidden units. 3. Calculating the response of the net. If the activation function in node layer is binary sigmoid, the output is calculated using a formula as follows: 1 A1 = Sð xi wij 1 + e If the activation function in output unit layer is binary sigmoid, the output is calculated as follows: 1 A2 = Sð A1i wij 1 + e Error (E) and mean square error (MSe) are defined by: E = T A2 MSe = Sð E 2 / n Connection weights are corrected by: D2 = A2 x (1-A2) x E dw 2 = dw 2 + (lr x D2 x A1) D1 = A1 x (1-A1) x (w 2 x D2) dw 1 = dw 1 + (lr x D1 x P) w 2 = w 2 + dw 2 w 1 = w 1 + dw 1 4. One cycle of step 3 is called one epoch. Step 3 is done until stopping condition of epoch (number of epoch) or until stopping condition of MSe value. 5. The result of the training is the value of the connection weights w ij, which will be used for testing and forecasting. In this research, 7% of the data is used for training and 3% for testing the performance of the net. This 7% and 3% combination was also used by Salya (26) to forecast cooking oil prices. The number of data in input vector is 12, it is to reflect the annual cycle. The numbers of nodes or hidden units tested are 14 nodes, 18 nodes, 22 nodes and 26 nodes. This number is choosen based on the formula made by Skapura (1996) as follows: nh = ½ (ni+no) + ndt Where: nh = number of minimal nodes or hidden units ni = number of units in input unit layer no = number of units in output unit layer ndt = number of data for training The forecasting value resulted by the ANN has a high reliability if the results of training and testing process have small MSe value and high correlation (r) value. RESULTS AND DISCUSSION Vetiver Prices From three types of activation functions (binary sigmoid, bipolar sigmoid, and purelin) applied to hidden units layer and output unit layers, three ranges of transformation data

3 6 Chandra Indrawanto et al. {(-1,), (,1), and (-1,1)}, and four numbers of neuron in hidden units layer (14, 18, 22, and 26 neuron), 18 combinations have been tried for training and testing ANN to forecast vetiver prices. The best performance of training and testing result has been come from combination of number of input data 12, 7% data were used for training and 3% for testing, number of neuron in hidden units layer was 22, range of data transformation (,1) activation function in hidden unit layer was binary sigmoid, and activation function in output unit layer was bipolar sigmoid. Using this combination, MSe value of training is.12 and correlation (r) value.9876, while MSe value of testing is.14 and correlation (r) value Error plot from training and testing process is presented in Figure 1 and 2. Based on that combination, the forecasting value of vetiver prices for next 24 months can be seen in Table Figure Figure 2. Error plot of artificial neural network vetiver price training. Error plot of artificial neural network vetiver price testing. and Figure 3. The results showed that the vetiver prices would be stable until August 28. The average price per month in 27 will be higher than the average price in 26, however, in 28 the average price would slightly decrease. The results also showed that the price fluctuation was consistent between years. The highest prices would be reached in March-April and the lowest prices would be achieved in October-November. This is because the farmers usually plant vetiver in December and harvest it after 1-11 months, so the supply of vetiver would be high in October-November, which makes the price of vetiver decreases. Vetiver Oil Prices From three types of activation functions (binary sigmoid, bipolar sigmoid and purelin) applied to hidden unit layers and output unit layers, three ranges of transformation data {(-1,), (,1), and (-1,1)}, and four numbers of neuron in hidden unit layers (14, 18, 22, and 26 neuron), 18 combinations have been tried for training and testing ANN to forecast vetiver oil prices. The best performance of training and testing result has been come from combination of number of input data 12, 7% data were for training and 3% for testing, number of neuron in hidden units layer was 22, range of data transformation (,1) activation function in hidden unit layer was bipolar sigmoid, and activation function in output unit layer was binary sigmoid. Using this combination, MSe value of training is.7 and correlation (r) value.9961, while MSe value of testing is.77 and correlation (r) value Error plot from training and testing process can be seen in Figure 4 and 5. Based on that combination, the forecasting value of vetiver oil prices for next 24 months can be seen in Table 2 and Figure 6. The results showed that the vetiver oil prices will be stable until August 28. The average price per month in 27 would be higher than the average price in 26; and in 28 the average price would slightly increase Table 1. Vetiver prices in January 2 to August 26 and its forecasting until August 28. Year Price (Rp/kg) Jan. Feb. March Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec

4 Forecasting of vetiver price Price Training Testing Forecasting 6 Price (Rp/kg) Figure 3. Vetiver prices in 2-26 and its forecasting until August Figure Figure 5. Error plot of artificial neural network vetiver oil price training. Error plot of artificial neural network vetiver oil price testing. again. The results also showed that the price fluctuation of vetiver oil was different with the vetiver price fluctuation. This is because the vetiver oil price was very influenced by its international prices, while vetiver prices was affected by domestic vetiver production. Financial Implication There are 33 vetiver oil industries with 43 steam boilers in Garut Regency, West Java. In average, capacity of the boilers used was 3.5 litres with diameter of 1.5 m and height 4.2 m. The boiler was made using steel plat with 6 mm thick. In general, the industries use wind dried vetiver with the average weight of 4 g/l. It means that the average capacity of steam boiler used is around 1.4 kg of vetiver. In average, each industry destilates vetiver 16 times per month. It takes around 12 hours to finish one destilation process. In average, vetiver oil produced is around.3% of the vetiver weight. The results of sensitivity analysis of vetiver oil industry showed that the break event point would be reached when the price of vetiver is Rp535/kg and the price of vetiver oil is Rp4,/kg. If the price of vetiver was Rp5/kg, the break event point would be reached when the price of vetiver oil is Rp388,/kg (Indrawanto 27). The forecasting results showed that the vetiver oil prices in 27 and 28 ranged between Rp392, and Rp463,/ kg, and the vetiver price ranged between Rp441 and Rp53/ kg. It means that the vetiver and vetiver oil prices in 27 and 28 were still above the break event point level. The result of sensitivity analysis of vetiver farming showed that the break event point would be reached when the price of vetiver is Rp35/kg (Hobir et al. 27). It means, from the forecasting result, the vetiver price in 27 and 28 were still above the break event point level. To increase the performance of vetiver oil industry in facing the price fluctuation, efforts to decrease the

5 62 Chandra Indrawanto et al. Table 2. Vetiver oil prices in January 2 to August 26 and its forecasting until August 28. Year Price (Rp/kg) Jan. Feb. March Apr. May Jun Jul. Aug. Sept. Oct. Nov. Dec Price Training Testing Forecasting 4 Price (Rp) Figure 6. Vetiver oil prices in 2-26 and its forecasting until August 28. operational cost, increase productivity and improve quality of the product are needed. Risfaheri and Mulyono (26) pointed that the quality of vetiver oil was mostly influenced by quality of raw material and condition of distillation process. Vetiver oil produced from vetiver which more than 1-month age, had a high specific weight and a better fragrant (Tasma et al. 199). To increase the vetiverol content in vetiver oil, redistillation vacuum can be used (Suryatmi et al. 26). The level of pressure in distillation process can affect the vetiver oil contents. Distillation pressure of 3 atmg increased vetiverol contents than 1 or 2 atmg pressure (Suryatmi 26). Purification process could also increase the quality of vetiver oil by make the oil brighter (Hernani and Marwati 26). CONCLUSION AND SUGGESTION Artificial neural network can be used to forecast vetiver and vetiver oil prices. A high performance forecasting with low MSe value can only be resulted if the combination between net architecture, training algorithm, and activation function used in the method are fitted. The forecasting result showed that the vetiver and vetiver oil prices in 27 and 28 were above the break event point of vetiver farming and vetiver oil industry. Some suggestions come out from this research are that the ANN should be tried to forecast prices of other commodities. It is necessary to compare the performance of price forecasting between ANN method and other

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