Penerapan Principal Component Analysis pada Model Deteksi Dini Anak Autisme

Penulis

DOI:

https://doi.org/10.32493/jtsi.v7i2.38935

Kata Kunci:

Anak, Autis, Deteksi, Principal Component Analysis

Abstrak

ASD (Autism Spectrum Disorder) adalah gangguan saraf yang menyebabkan gangguan seumur hidup pada anak yang mengakibatkan penyakit jiwa. Perawatan dapat membantu tetapi tidak dapat disembuhkan. Saat ini ASD dideteksi dengan memahami perilaku dan aktivitas intelektual seorang anak. Diagnosis ini bisa subjektif, memakan waktu, tidak meyakinkan, tidak memberikan wawasan yang tepat tentang genetika dan tidak cocok untuk deteksi dini. Dalam Autisme, tantangan besar yang dihadapi dalam banyak kondisi perawatan kesehatan adalah waktu diagnosis. Diperlukan waktu hingga 6 bulan untuk mendiagnosis anak autis dengan pasti karena proses yang lama, dan seorang anak harus menemui banyak spesialis berbeda untuk mendiagnosis autisme, mulai dari dokter anak perkembangan, ahli saraf, psikiater atau psikolog. Metode Pembelajaran Mesin dapat membuat perubahan yang relevan untuk mempercepat proses. Pada penelitian ini diusulkan penerapan PCA (Principal Component Analysis). PCA pada dasarnya adalah dasar untuk analisis data multivariat yang menerapkan metode proyeksi. Teknik analisis ini biasanya digunakan untuk merangkum tabel data multivariat yang besar sehingga dapat digunakan sebagai kumpulan variabel yang lebih kecil atau sebagai indeks ringkasan. Dari sana, variabel dianalisis untuk menemukan tren spesifik, cluster variabel, dan outlier. Pada penelitian ini diusulkan untuk mengimplementasikan algoritma PCA (Principal Component Analysis), yaitu PCA (Principal Component Analysis), Kernel PCA, Sparse PCA, dan Incremental PCA. Pada penelitian ini menggunakan metode eksperimen dengan membuat aplikasi untuk menerapkan algoritma yang diusulkan. Kemudian menguji model menggunakan dataset sekunder dan mengukur kinerja model. Hasil penelitian menunjukkan bahwa model yang menerapkan Sparse PCA memberikan hasil terbaik yang berarti bahwa penerapan PCA dapat digunakan untuk mereduksi jumlah fitur dan meningkatkan kinerja model.

Biografi Penulis

Aries Saifudin, Universitas Pamulang

Received A.Md. (Associate Degree) in Electronic Engineering from Polytechnic of Brawijaya University, Malang, S.T. (Bachelor Degree) in Informatics Engineering from Mercu Buana University, Jakarta, and M.Kom (Master Degree) in Software Engineering from STMIK ERESHA, Jakarta. He is a lecturer at Informatics Engineering, Pamulang University. His current research interests include software engineering, intelligent systems, and machine learning.

Publication:

SCOPUS ID: 57212083789

Researcher ID: AAJ-9700-2021

SINTA ID: 256981

Google Scholar ID: rN9QTUgAAAAJ

ORCID ID: 0000-0002-3882-633X

Garuda ID: 459608

 

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Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 2 April 2024

Unduhan

Diterbitkan

2024-04-30

Cara Mengutip

Saifudin, A., Nirmala, E., & Kusyadi, I. (2024). Penerapan Principal Component Analysis pada Model Deteksi Dini Anak Autisme. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(2), 569–578. https://doi.org/10.32493/jtsi.v7i2.38935

Terbitan

Vol 7 No 2 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Bagian

Article

Lisensi

Hak Cipta (c) 2024 Aries Saifudin, Endar Nirmala, Irpan Kusyadi

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