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Intercropping in Rubber Plantation Ontology for a Decision Support System  

Phoksawat, Kornkanok (Faculty of Management Technology, Rajamangala University of Technology Srivijaya)
Mahmuddin, Massudi (School of Computing, Universiti Utara Malaysia)
Ta'a, Azman (School of Computing, Universiti Utara Malaysia)
Publication Information
Journal of Information Science Theory and Practice / v.7, no.4, 2019 , pp. 56-64 More about this Journal
Planting intercropping in rubber plantations is another alternative for generating more income for farmers. However, farmers still lack the knowledge of choosing plants. In addition, information for decision making comes from many sources and is knowledge accumulated by the expert. Therefore, this research aims to create a decision support system for growing rubber trees for individual farmers. It aims to get the highest income and the lowest cost by using semantic web technology so that farmers can access knowledge at all times and reduce the risk of growing crops, and also support the decision supporting system (DSS) to be more intelligent. The integrated intercropping ontology and rule are a part of the decision-making process for selecting plants that is suitable for individual rubber plots. A list of suitable plants is important for decision variables in the allocation of planting areas for each type of plant for multiple purposes. This article presents designing and developing the intercropping ontology for DSS which defines a class based on the principle of intercropping in rubber plantations. It is grouped according to the characteristics and condition of the area of the farmer as a concept of the rubber plantation. It consists of the age of rubber tree, spacing between rows of rubber trees, and water sources for use in agriculture and soil group, including slope, drainage, depth of soil, etc. The use of ontology for recommended plants suitable for individual farmers makes a contribution to the knowledge management field. Besides being useful in DSS by offering options with accuracy, it also reduces the complexity of the problem by reducing decision variables and condition variables in the multi-objective optimization model of DSS.
ontology development; ontology to decision supporting system; intercropping ontology; knowledge-based decision supporting system; semantic web for decision supporting system;
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