Pemodelan Berat Badan Balita dengan Menggunakan Regresi Kernel

Authors

  • Agni Horti Maharani
  • Hazmira Yozza
  • Yudiantri Asdi

DOI:

https://doi.org/10.25077/jmu.4.3.31-40.2015

Abstract

Dalam analisis regresi terdapat dua pendekatan yang digunakan untuk mengestimasi fungsi regresi yaitu pendekatan parametrik dan pendekatan nonparametrik. Pendekatan parametrik digunakan apabila informasi hubungan antara variabel prediktor dengan variabel respon diketahui. Namun apabila informasi hubungan antara variabel prediktor dengan variabel respon tidak diketahui maka alternatif lain yang dapat digunakan adalah dengan pendekatan nonparametrik. Estimator kernel adalah metode yang digunakan pada penelitian ini. Penelitian ini bertujuan untuk mengetahui bentuk model regresi kernel dengan fungsi kernel Gaussian untuk memodelkan berat badan balita berdasarkan umur serta membandingkan model yang dibentuk dengan Metode Kuadrat Terkecil. Dari hasil perhitungan diperoleh nilai koefisien determinasi (R2 ) dengan regresi linier sederhana adalah sebesar 0,719 dan nilai koefisien determinasi (R2 ) dengan regesi nonparametrik menggunakan estimator kernel adalah sebesar 0,770606. Dari hasil perhitungan tersebut disimpulkan bahwa pada kasus ini analisis regresi nonparametrik menggunakan estimator kernel dengan fungsi kernel Gaussian mampu menghasilkan model yang jauh lebih baik pada data berat badan balita terhadap umur daripada analisis regresi dengan Metode Kuadrat Terkecil.

Kata Kunci: Analisis regresi, estimator kernel, fungsi kernel gaussian, berat badan balita

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Published

26-07-2019

Issue

Vol. 4 No. 3 (2015)

Section

Articles

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