Decision Support System Using Artificial Neural Network to Predict Rice Production in Phimai District, Thailand

Jabjone, Saisunee; Wannasang, Sura

doi:10.7763/ijcee.2014.v6.814

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…Algorithm implementation backpropagation in information systems can predict the wind [9]. The Backpropagation algorithm has applied to predict rice production; thus, it can be anticipated in advance so that rice production can be obtained become self-sufficient in food [10]. The backpropagation Algorithm can produce information systems that can predict the oil palm commodity price as an essential reference in taking company policy [11].…”

Section: Previousresearchmentioning

confidence: 99%

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Backpropagation Neural Network for Predict Sugarcane Stock Availability

2020

IJATCSE

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National sugar consumption from 2013 to 2016 experienced an upward trend of 6.14%, especially for 2016, demand increased by 5.08% from the previous year [1]. Increment consumption of this sugar is inversely proportional to production results domestic sugarcane. Domestic sugarcane yields each year has decreased due to the problem of sugarcane on-farm or off the farm. Hence the government is trying to import sugarcane raw materials to fulfill national sugar needs. In importing raw materials sugarcane, the government needs predictive data for sugarcane production and the national sugar needs that will come used as a reference for the amount of raw sugarcane that needs to be imported. This study aims to estimate the results of sugarcane production based on historical data from 2017 to 2019. After making further predictions, it is necessary to evaluate the prediction model used. Method research used to predict production yield sugarcane, namely the Backpropagation Neural Network.Furthermore, the evaluation of the model used uses the Mean Absolute Percentage Error (MAPE). MAPE delivers a hint of how large the forecast error is compared with the series's real value. The following result 97% predictable production and 3% MAPE yield, then it can be said that the result of the method Backpropagation Neural Network has modeling capabilities decent forecast.

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Section: Previousresearchmentioning

confidence: 99%

“…Which, is the weight correction factor; ; iscorrection factor; is input. Calculate corrections for weights andbias by equation (10).…”

Section: = ( -) ' ( )(6)mentioning

confidence: 99%

Backpropagation Neural Network for Predict Sugarcane Stock Availability

2020

IJATCSE

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“…The review reports the application of machine learning techniques with artificial neural networks, Bayesian networks, support vector machines and k-means. In [35], researchers predict Rice Production in Phimai District, Thailand Decision Support System Using Artificial Neural Network. A study by [36] implemented C 4.…”

Section: Wheat Using Fuzzy C Means Clustering and Neuralmentioning

confidence: 99%

A Survey on Rice Crop Yield Prediction in India Using Improved Classification Technique

Sukhadia¹,

Chaudhari

2019

IJSRCSEIT

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<p>India is an agricultural country. Agriculture is the important contributor to the Indian economy. There are many classification techniques like Support Vector Machine(SVM), LADTree, Natve Bayes, Bayesnet, K Nearest Neighbour(KNN), Locally Weighted Learning(LWL) on rice crop production datasets. They have some drawbacks like low accuracy and more errors. To achieve more significant result, To increase classification accuracy and reducing classification errors, our research uses classification method Bayesnet based adaboost will be proposed in work. Rice crop yield depend on environment's parameters like Rainfall, minimum temperature, average temperature, Maximum temperature, Vapour Pressure, potential evapotranspiration, reference crop evapotranspiration, cloud cover, wet day frequency for the kharif season. our dataset containing these environmental parameters for accurate prediction of Rice crop yield.</p>

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“…Kemampuan memprediksi masa yang akan datang akan membuat petani bisa mengambil keputusan yang tepat dalam mengantisipasi keadaan di masa depan. Jaringan Syaraf Tiruan (JST) menawarkan kemampuan yang baik untuk mesin pembelajaran, prediksi, termasuk juga prediksi dalam bidang pertanian (Jabjone, Wannasang, 2014).…”

Section: Pendahuluanunclassified

Peramalan Produksi Padi Di Indonesia Menggunakan Jaringan Syaraf Tiruan Bilangan Kompleks

Azis

2019

Sebatik

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Ketahanan pangan nasional tetap membutuhkan beras dalam memenuhi kecukupan pangan, namun demikian tekanan terhadap peningkatan produksi padi terkendala oleh permasalahan konversi lahan pertanian, kerusakan jaringan irigasi, perubahan iklim, dan serangan hama penyakit. Kemampuan memprediksi masa yang akan datang akan membuat petani bisa mengambil keputusan yang tepat dalam mengantisipasi keadaan di masa depan. Jaringan Syaraf Tiruan (JST) telah banyak digunakan dalam prediksi, termasuk dalam bidang pertanian. JST bilangan kompleks (JSBK) memiliki struktur pemrosesan bilangan kompleks sehingga memiliki kemampuan pelatihan yang lebih baik, kemampuan generalisasi yang lebih baik, konvergensi yang lebih cepat, kompleksitas yang lebih baik, dan data pelatihan yang lebih sedikit. Pada JST dengan arsitektur multi-layer perceptron (MLP) biasa yang bernilai real, nilai dari parameter yang ada, yaitu elemen dari matriks bobot dan vektor bias, dinyatakan dalam bilangan real, sedangkan pada JST bernilai kompleks, nilai dari parameter yang ada dinyatakan dalam bilangan kompleks. Jaringan Syaraf Tiruan (JST) bilangan kompleks dengan algoritma Backpropagation, umumnya digunakan dalam data citra atau suara, namun pada pada penelitian ini digunakan untuk memprediksi produksi padi tahun 2015 di 34 provinsi Indonesia. Data produksi padi diperoleh dari dari Badan Pusat Statistik (BPS), sedangkan data curah hujan, suhu, kelembaban, tekanan udara diperoleh dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG). Atribut masukan pada pelatihan JST pada penelitian ini adalah data iklim dari 34 propinsi di Indonesia dari tahun 2011-2015, yaitu nilai dari : curah hujan, kelembaban, rata-rata suhu, dan rata-rata tekanan udara. Atribut keluaran JST adalah nilai produktifitas padi. Berdasarkan hasil pengujian, JST Backpropagation bilangan kompleks berhasil memprediksi produksi padi tahun 2015 dengan sangat baik pada 32 provinsi (jika tidak menyertakan data provinsi Kepulauan Riau dan DKI Jakarta) dengan rata-rata akurasi 98,9 %.

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Decision Support System Using Artificial Neural Network to Predict Rice Production in Phimai District, Thailand

Cited by 13 publications

References 4 publications

Backpropagation Neural Network for Predict Sugarcane Stock Availability

Backpropagation Neural Network for Predict Sugarcane Stock Availability

A Survey on Rice Crop Yield Prediction in India Using Improved Classification Technique

Peramalan Produksi Padi Di Indonesia Menggunakan Jaringan Syaraf Tiruan Bilangan Kompleks

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