Jonathan, Leonardo and Young, Julio Cristian and Hansun, Seng (2019) EARLY DETECTION OF PULMONARY TUBERCULOSIS DISEASE WITH FUZZY AHP EXPERT SYSTEM. Compusoft, 8 (104). pp. 3444-3447.

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The development of information technology provides information that is relevant, accurate, on time, and provides information that can be used to assist in making a decision. Pulmonary Tuberculosis disease is one of the fatal and most common diseases in the world. Using information technology, we could develop a system that can be used as a means for users to early detect Pulmonary Tuberculosis disease. In this study, to detect the Pulmonary Tuberculosis disease, Fuzzy Analytical Hierarchy Process (F-AHP) algorithm was used. The system built was divided into two stages, i.e., AHP and F-AHP. AHP algorithm was used to determine the value of data consistency from data that has been used, while F-AHP algorithm was used as a final determinant of the weight value of each criterion data used. Furthermore, the testing system was done by testing the method and testing the usefulness of the system. The method testing was done by comparing the value of the final weight between the system calculation and manual calculation, which produce the same value. The usefulness of the system then was evaluated by using the System Usability Scale (SUS) based on expert's opinion, which produce a score of 82.5 and based on user's opinion produced a score of 86.16. Both of the results can be included in the "Acceptable" category.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods (3D Graphics, Digital Video, Data Mining, Augmented Reality)
600 Technology (Applied Sciences) > 610 Medicine and Health > 610 Medicine and Health
600 Technology (Applied Sciences) > 610 Medicine and Health > 616 Diseases
Divisions: Fakultas Teknik Informatika > Program Studi Sistem Informasi
Depositing User: mr admin umn
Date Deposited: 06 Oct 2021 06:09
Last Modified: 06 Oct 2021 06:09
URI: http://kc.umn.ac.id/id/eprint/18551

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