Main Article Content
Abstract
The obstacle that often occur on soybean cultivation activity is a weed problem that could potentially reduce productivity. It’s need an expert system that can represent knowledge regarding the identification of weed species, therefore farmers can decide appropriate controls. This study aims create offline android based expert system is to identify the type of weeds in soybean cultivation based morphological information and determine appropriate advice weed control. Forward chaining used as an inference engine development methods to achieve the conclusion of expert systems. Expert systems of weed species identification contain information of the weeds type and grouping, herbicides information, herbicide dose, and weed control galleries. Application size 6.97 Megabytes (Mb) and run without internet connection. Testing applications can run properly, with accuration of weed species identification reaches 87.5%
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).