PERMODELAN PREDIKTIF AUTISTIC SPECTRUM DISORDER DENGAN ALGORITMA C.45

  • Oktafian Farhan Program Pascasarjana Magister Ilmu Komputer STMIK Nusa Mandiri
  • Agus Subekti Pusat Penelitian Elektronika dan Telekomunikasi Lembaga Ilmu Pengetahuan Indonesia (LIPI) https://orcid.org/0000-0002-4525-4747
DOI: https://doi.org/10.33480/techno.v15i2.18
Keywords: AUTISTIC SPECTRUM DISORDER, C.45, C.45 algorithm, Prediction Modelling

Abstract

Autism is a developmental disability experienced throughout the life of a patient with Autistic Spectrum Disorder (ASD). The sooner it is
handled, the more likely the child will return to normal. For this reason, a new method is needed that can help parents to quickly recognize the
symptoms of autism in their children. In a previous study conducted by Fadi Fayez Tabhtah a data set was produced to detect whether a child has autism or not. But the research only produces data sets, he does not examine more in which algorithm is suitable for the data sets that have been produced. The data set attributes have some mising value, which invite a question about the accuracy of data. In this study researchers used the CRISP-DM method and test the accuracy of data sets of previous studies using the C.45 algorithm. Furthermore, the WEKA application
using feature selection and influence of the missing value for each attribute and find the most significant. These attributes are then tested with
the C.45 algorithm so that the predictive model of the data set is obtained. The A6 attribute of the decision tree calculation does not appear at all as a branch. A new model is obtained where the A6 attribute is omitted, so that when measured by the C.45 algorithm, a better accuracy value is
obtained. The results of the new model were then tested on the new questionnaire data, which produced precise predictions.

References

Adinugroho., S., & Sari, Y. A. (2018). Implementasi Data mining Menggunakan WEKA (1st ed.). Malang: Brawijaya Press.

Dheeru, D., & Karra Taniskidou, E. (2017). {UCI} Machine Learning Repository.

Farhan, O., & Subekti, A. (2018). Laporan Penelitian: Permodelan Prediktif Autistic Spectrum Disorder Dengan Algoritma C.45. Jakarta.

Hall, M. A. (1999). Correlation-based Feature Selection for Machine Learning. https://doi.org/10.1.1.149.3848

Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (3rd ed.). USA: Morgan Kaufmann.

Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining (1st ed.). Yogyakarta: Andi Publisher. Retrieved from http://andipublisher.com/produk-0907003050-algoritma-data-mining.html

Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5(1). https://doi.org/10.1016/0022-4073(81)90019-4

Thabtah, F. (2017). Autism Spectrum Disorder screening: Machine learning adaptation and DSM-5 fulfillment. Proceedings of the 1st International Conference on Medical and Health Informatics 2017 - ICMHI ’17, 1–6. https://doi.org/10.1145/3107514.3107515

Thabtah, F. (2018). Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. Informatics for Health and Social Care, 8157, 1–20. https://doi.org/10.1080/17538157.2017.1399132

Witten, I., & Frank, E. (2005). Datamining. Practical Machine Learning Tools and Technicals. https://doi.org/0120884070, 9780120884070
Published
2018-09-15
How to Cite
Farhan, O., & Subekti, A. (2018). PERMODELAN PREDIKTIF AUTISTIC SPECTRUM DISORDER DENGAN ALGORITMA C.45. Jurnal Techno Nusa Mandiri, 15(2), 99-106. https://doi.org/10.33480/techno.v15i2.18
Issue
Vol 15 No 2 (2018): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode September 2018
Section
Articles
Copyright (c) 2018 Oktafian Farhan & Agus Subekti

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