Agricultural Strategic Commodity Price Forecasting Using Artificial Neural Network

Anggraeni, Wiwik; Mahananto, Faizal; Rofiq, M.Aunu; Andri, Kuntoro Boga; Sumaryanto,; Zaini, Zulkifli; Subriadi, Apol Pribadi

doi:10.1109/isriti.2018.8864442

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…As a comparison, Figure 3 shows the actual and predicted agricultural commodity price on training and testing data. Chili price forecasting using ANN [24] resulted in 16.19 MAPE value. This result is much higher than the forecasting model using PSO-RBFNN.…”

Section: Resultsmentioning

confidence: 99%

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

Raflesia¹,

Taufiqurrahman²,

Iriyani³

et al. 2021

IJEEI

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Agricultural commodity price forecasting becomes important for farmers since the knowledge of agriculture commodity price fluctuation can help the farmers to identify the right selling time. Recently, the absence of such the forecasting system makes the farmers decide to sell their commodities to middlemen which in turn, reduces their exchange rate as the length of distribution flow is complicated. The length of distribution flow is started from farmers, middlemen, wholesalers, retailers, and consumers. To address this problem, a forecasting system based on radial basis function neural network (RBFNN) is proposed. To optimize the network's learning process, particle swarm optimization (PSO)-based learning technique is applied. The RBFNN is chosen because of its ability to generally track irregular signal changing, good speed in learning process, and robustness. Meanwhile, the implementation of PSO aims to improve weight values towards global optimum in RBFNN model.

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Section: Resultsmentioning

confidence: 99%

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

Raflesia¹,

Taufiqurrahman²,

Iriyani³

et al. 2021

IJEEI

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“…Cabai merupakan salah satu komoditas pertanian yang banyak dibudidayakan petani di Indonesia (Anggraeni et al, 2018). Salah satu komoditas yang sering dibudidayakan oleh petani adalah cabai merah (Satria P et al, 2017).…”

Section: Pendahuluanunclassified

Education on the use of compost blocks for chili cultivation for coastal sand farming groups

Susanawati,

Mulyono,

Akhmadi

2023

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High temperatures in the land are one of the obstacles faced in cultivating chilies on coastal sand, resulting in nutrients being easily lost and not lasting long. An alternative solution to overcome this problem is to use compost block planting media. This service activity aims to provide knowledge and skills in utilizing agricultural waste for compost as a medium for planting chilies in beach sand. The service methods used are (1) Counseling and Focus Group Discussions about compost blocks; (2) training and demonstration on making block compost; (3) demonstration plot for the use of compost blocks for chili plants, and (4) monitoring and evaluation before, during, and after the activity takes place. The results of service activities show that partners are very enthusiastic about participating in all activities, as shown by an increase in partners' knowledge about compost blocks by 47%. There are 12% of chili plants that die when planted using compost blocks. Meanwhile, chili plants planted conventionally without compost blocks produced more dead plants, namely 30%.

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“…In [11] this research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year. Can predict prices with a good accuracy and the use of RNA enhance the percentage of right prediction.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Functional Link Neural Networks for Soybean Price Forecast

Dhanalakshmi¹,

Rajakumar²,

Anakath³

et al. 2023

Advances in Computer Science Research

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Drastic change in crop prices is observed due to climatic changes, natural calamities and lack of quantity of a specific commodity. Crop price prediction plays key role in effective farm management. Farmers are not able to predict these crop prices and facing massive loss. These aspects pressure us to use advanced technology and develop accurate, reliable and efficient crop price prediction system. Crop price prediction also helps agriculture based industries and policymakers. There are many price-sensitive crops like tomatoes, onions, potatoes, Soybean and other food grains, which need prior price prediction so that farmers can take wise decisions on which crop to cultivate. Functional link neural network is chosen to develop Basic network for Soybeans price prediction. Optimization algorithms like whale optimization, particle swarm optimization and Harris Hawks Optimization are used to calculate appropriate biases and weights. Dataset is taken from daily reports issued by Chicago Mercantile Exchange (CME).Most efficient hybrid FLNN with associated Expansion function, activation function and learning scheme for predicting crop price could be found out through our study.

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Agricultural Strategic Commodity Price Forecasting Using Artificial Neural Network

Cited by 14 publications

References 15 publications

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

Education on the use of compost blocks for chili cultivation for coastal sand farming groups

Hybrid Functional Link Neural Networks for Soybean Price Forecast

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