E3S Web Conf.
Volume 73, 2018The 3rd International Conference on Energy, Environmental and Information System (ICENIS 2018)
|Number of page(s)||6|
|Section||System Information and Decision Support System|
|Published online||21 December 2018|
Android Based Self-Control Management System for Diabetes Mellitus
1 Master Program of Information System, School of Postgraduate Studies, Diponegoro University, Semarang - Indonesia
2 Department of Computer Engineering, Faculty of Engineering, Diponegoro University, Semarang - Indonesia
* Corresponding author: firstname.lastname@example.org
One of the biggest cause of death is Diabetes Mellitus caused by a lack of public understanding of the symptoms of the disease, so that the diagnosis of the disease is not done as early as possible. This paper presents the research and the development of an Android based self-control management expert system for Diabetes Mellitus patients. This expert system purposed to diagnose Diabetes Mellitus disease based on symptoms experienced and to manage the dietary pattern in patients. The method used to develop expert system is forward chaining method. Implementation of the forward chaining method begins with gathering information then applying reasoning as a determinant of diagnosis conclusions using rule based If-Then. The development result is an expert system that can be used to diagnose Diabetes Mellitus and can be used to determine the dietary pattern in patients who are implemented on Android based mobile devices. This system shows more specific results in determining the diagnosis of the disease based on 4 types of Diabetes Mellitus. In addition, more specific in determining dietary pattern such as showing the number of calories, food levels and variations of food that can be consumed by patients.
Key words: Expert system / Diabetes mellitus / Dietary pattern / Android / Forward Chaining
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.