Putri, Maurisa Arimbi (2023) Implementasi Algoritma Fuzzy K-Nearest Neighbor dalam Sistem Diagnosis Awal Autism Spectrum Disorder. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by abnormalities in cognitive, behavioral, and social interaction aspects. ASD can occur in anyone and has seen an increasing incidence over the years. Experts believe that identifying ASD early allows for more effective interventions as children's brains are more responsive to changes during their early development. Early ASD diagnosis often involves consultations with a psychologist, which can incur significant costs. The utilization of computer technology presents a potential solution in early ASD prediction based on emerging symptoms. One approach used in this research is data mining, involving the exploration of data from various sources within a database. In this study, the data mining algorithm used is the Fuzzy K-Nearest Neighbor (FKNN) to determine the possibility of ASD. The research findings indicate that utilizing the FKNN algorithm in early ASD diagnosis achieves the highest accuracy rate of 90.5%, with a precision value of 96.4%, a recall value of 86.7%, and an F1-score of 90.4%. These values were obtained when cv=7 with k=5 and k=7.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | T Technology > T Technology (General) | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Maurisa Arimbi Putri | ||||||||||||
| Date Deposited: | 21 Nov 2023 06:24 | ||||||||||||
| Last Modified: | 21 Nov 2023 06:24 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/18745 |
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