An Evaluation of Suitable Landscape to Crop Food Cultivation By Using Neural Networks

Abstract

Penentuan jenis tanaman pangan yang sesuai ditanam pada lahan tertentu berdasarkan nilai-nilai karakteristik lahan sangat diperlukan sebagai pendukung pengambilan keputusan, koordinasi, dan pengendalian bagi para peneliti, praktisi, dan perencana penggunaan lahan, sehingga kerugian (finansial) yang cukup besar tidak terjadi nantinya. Program komputer dengan menggunakan Jaringan Syaraf Tiruan (JST) metode Learning Vector Quantization (LVQ) dapat digunakan sebagai alat yang tepat dalam memberikan informasi tanaman yang cocok ditanam dengan mudah, cepat, dan akurat. Data pelatihan didapat dari kombinasi nilai karakteristik lahan yang termasuk dalam kelas kesesuaian S1 dan S2. Hasil pengujian menunjukkan bahwa nilai Eps (error minimum yang diharapkan) = 0.005, nilai ?? ?? = 0.05, nilai maksimum epoh = 10, dan nilai pengurangan learning rate sebesar 0.1*?? ?? merupakan nilai-nilai yang cukup efektif dan efisien dalam melakukan prediksi jenis tanaman pangan yang sesuai ditanam pada lahan tertentu karena tingkat ketepatan prediksinya adalah 100%.

Extended Fuzzy Decision Support Model for Cropland Recommendation of Food Cropping in Indonesia

“…Food crop productivity is determined by the quality of the land used. If unproductive regions are not put aside while picking land at the start of plant growth, significant (financial) losses will occur later (Azis et al, 2006). Climate and weather are the two most important factors influencing food security in Indonesia.…”

“…As a result, the competent authorities can make objective judgments on food crop planting methods for future food security in Indonesia. Azis et al (2006) applied the Artificial Neural Network (ANN) and the Learning Vector Quantization (LVQ) approach to create a computer software that may be utilized as an acceptable tool in delivering information on acceptable plants for planting conveniently, swiftly and precisely. The training data were generated by combining land characteristic values from the S1 and S2 suitability classes.…”

Suitability analysis of rice varieties using learning vector quantization and remote sensing images

“…In addition, combining the machine learning algorithm with the remote sensing images will As the machine learning algorithm, we implemented Learning Vector Quantization (LVQ), since it has been implemented to predict 12 kinds of food plants with the high accuracy value, i.e. 100% [19]. Furthermore, LVQ processing time is relatively faster than other artificial neural network methods because the LVQ network architecture consists of only 2 layers, that are, the input layer and the output layer.…”