Abstract
<p>Crop growth and production on particular land and climate is strongly influenced by the interaction between plants, climate, soil, and management. Land quality and climate greatly affect the expected production of oil palm are: soil type, soil depth, altitude, soil pH, rainfall / year, average temperature, water deficit in mm / yr, air humidity, and solar radiation. Oil palm production as a function of land quality and climate can be predicted using various methods. Artificial Neural Network (ANN) is one recognized method for predict land productivity. In this study ANN Back propagation algorithm is used. The aim of this research is to develop ANN model and simulation of Oil Palm Plantation Productivity. Through the optimization procedure obtained the best ANN architecture is 11 neurons in input layer - 3 neurons in the hidden layer and - 1 neuron in the output layer, at 30,000 iterations of training step obtained the best model of oil palm productivity prediction with a value of R2: 0.98 and RMSE: 0:49, while from the test step obtains the value of R2: 0.94 and RMSE: 1.63. The results of simulation show that the simultaneous influence of several climatic changes that decrease the quantity of rainfall 100 mm / yr, 1 0C temperature rise, and increasing water deficit 50 mm / yr reduce the productivity of oil palm plantations for 2.15 tons / ha / year. From this research can be concluded that ANN can be used to predict the production of palm oil based on quality of land and local climate with very good results.</p>
Highlights
Data perkiraan produksi suatu perkebunan diperlukan sejak mulai evaluasi kesesuaian lahan untuk memperoleh land economic value dari suatu penggunaan lahan tertentu atau seara periodik dalam perkiraan produksi
Crop growth and production on particular land and climate is strongly influenced by the interaction between plants, climate, soil, and management
Through the optimization procedure obtained the best Artificial Neural Network (ANN) architecture is 11 neurons in input layer - 3 neurons in the hidden layer and - 1 neuron in the output layer, at 30,000 iterations of training step obtained the best model of oil palm productivity prediction with a value of R2: 0.98 and Root Mean Square Error (RMSE): 0:49, while from the test step obtains the value of R2: 0.94 and RMSE: 1.63
Summary
Pertumbuhan dan produksi tanaman pada wilayah tertentu sangat tergantung pada interaksi antara parameter iklim, tanah, tanaman dan pengelolaannya, dengan kata lain produksi tanaman dengan sistem pengelolaan tertentu merupakan fungsi dari kualitas/karakteristik lahan dan iklim disekitarnya. Produksi tanaman sebagai fungsi dari kualitas lahan dan iklim tersebut dapat diprediksi menggunakan berbagai metode. Jaringan syaraf tiruan merupakan salah satu metode prediksi yang diakui keunggulannya, terutama untuk prediksi yang melibatkan banyak parameter yang bekerja secara simultan dengan bentuk hubungan fungsional yang tidak linier. Input layer mempunyai n node, hidden layer mempunyai h node dan output layer mempunyai m node, seperti pada Gambar 1.Penggunaan metode JST diperkirakan dapat memberikan jawaban yang lebih baik dalam memprediksi produksi tanaman perkebunan sebagai fungsi parameter karakteristik/kualitas lahan. Sifat non–linier yang merupakan kekuatan jaringan syaraf tiruan yang lain dapat mengatasi kekurangan dari metode konvensional yang rumit dan tidak disukai apabila memasuki model yang non–linier. Model JST yang diperoleh digunakan untuk simulasi pengaruh perubahan kualitas lahan dan iklim secara simultan pada produktivitas lahan kelapa sawit
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