Measurement of Similarity and Design Interface for Soybean Disease Diagnosis
Adriana Sari Aryani1, Dian Kartika Utami2, Hermawan3
1Adriana Sari Aryani, Department of Computer Science, Pakuan Bogor University, Indonesia.
2Dian Kartika Utami, Department of Information System, Pakuan Bogor University, Indonesia.
3Hermawan, Department of Computer Science, Pakuan Bogor University, Indonesia.
Manuscript received on 03 August 2019 | Revised Manuscript received on 26 August 2019 | Manuscript Published on 05 September 2019 | PP: 142-145 | Volume-8 Issue-2S7 July 2019 | Retrieval Number: B10340782S719/2019©BEIESP | DOI: 10.35940/ijrte.B1034.0782S719
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The similarity of the target case is determined by measuring how close each attribute of the target is similar to the stored case in the case base. Similarities are usually normalized to fall within a range of 0 to 1. The soybean is one of the most important bean in the world, providing vegetable protein for millions of people and ingredients for hundreds of chemical products. Several diseases, including root and stem rot, pod and stem blight, frogeye leaf spot, brown spot, downy mildew, leaf blight and purple seed stain, and stem rot (white mold). In a case-based reasoning system for the identification of diseases of soybean plants provide solutions recommended by experts in diseases of soybean in accordance with a similar case or a similar matches within the database storage plant disease cases. Similarity value where 0 is totally dissimilar and 1 is an exact match. if similarity value equal zero then system will keep a set of data will be save temporary and need validation as a new case from the expert. The system give a recommended solution from similarity formula with the threshold which given by the expert.
Keywords: Similarity, Design, Soybean Disease.
Scope of the Article: Design and Diagnosis