Penerapan Teknik Stacking untuk Optimasi Deteksi Dini Anak Autis berbasis Support Vector Machine

Penulis

  • Yulianti Yulianti Universitas Pamulang
  • Teti Desyani Universitas Pamulang
  • Sri Mulyati Universitas Pamulang

DOI:

https://doi.org/10.32493/jtsi.v7i4.21570

Kata Kunci:

Anak; Autis; Prediksi; Stacking

Abstrak

Autisme tidak sulit dideteksi, tetapi membutuhkan banyak pembelajaran dan pelatihan bagi dokter untuk mendeteksinya. 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. Metode Pembelajaran Mesin (Machine Learning) dapat membuat perubahan yang relevan untuk mempercepat proses. Diketahui bahwa intervensi dini merupakan kunci untuk memperbaiki anak autis. Jelas mempercepat waktu diagnosis bahkan lebih penting dalam kasus Autisme. Teknologi big data dan pembelajaran mesin dapat membuat kemajuan besar untuk memprediksi dan mempercepat proses diagnosis dan pengobatan yang kompleks dan memakan waktu. Sistem pembelajaran mesin dapat dikembangkan untuk memanfaatkan sejumlah besar data kesehatan dan medis yang tersedia untuk pemodelan prediktif dan analisis prediktif. Dalam makalah ini, perbandingan dari beberapa teknik dan model pembelajaran mesin akan diuji dan dianalisis. Pada penelitian ini diusulkan penerapan teknik stacking untuk prediksi adanya ASD. Hasil penelitian menunjukkan bahwa penerapan teknik stacking dapat meningkatkan kinerja model prediksi pada diagnosa ASD.

Referensi

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Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 4 Oktober 2024

Unduhan

Diterbitkan

2024-10-31

Cara Mengutip

Yulianti, Y., Desyani, T., & Mulyati, S. (2024). Penerapan Teknik Stacking untuk Optimasi Deteksi Dini Anak Autis berbasis Support Vector Machine. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(4), 1574–1581. https://doi.org/10.32493/jtsi.v7i4.21570

Terbitan

Vol 7 No 4 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Bagian

Article

Lisensi

Hak Cipta (c) 2024 Yulianti Yulianti, Teti Desyani, Sri Mulyati

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