Kombinasi Linier Target Data Untuk Regresi Multitarget Menggunakan Principal Component Analysis
DOI:
https://doi.org/10.54914/jtt.v9i1.516Kata Kunci:
PCA, reduksi dimensi, regresi linier, regresi multidimensi, regresi multitargetAbstrak
Regresi linier adalah metode untuk memprediksi sebuah nilai (variabel dependen) berdasarkan beberapa input (variabel independen). Permasalahan pada regresi linier adalah beberapa data tidak termasuk ke dalam kategori linier. Sebuah metode bernama RLC diciptakan untuk menemukan korelasi antara data output dengan cara memproyeksikan data ke dalam dimensi yang lebih tinggi. Sayangnya, metode RLC tidak dapat diinvers transformasinya. Selain itu, dengan memproyeksikan data ke dimensi yang lebih tinggi akan menambah kompleksitas dari algoritma pembelajaran. Oleh karena itu, PCA akan digunakan untuk memecahkan masalah ini dengan cara memproyeksikan data ke dimensi yang lebih rendah sembari mempertahankan kemampuan untuk melakukan invers proyeksi. Penelitian ini diimplementasikan dengan bantuan library scikit-learn untuk membuat model regresi dan transformasi data dengan menggunakan bahasa pemrograman Python. Hasilnya, untuk 12 dataset, metode augmentasi PCA mampu mendapatkan nilai error yang lebih rendah dalam 7 dataset dibandingkan dengan RLC dengan rata-rata nilai error 0.3270 untuk metode augmentasi PCA dan 0.4003 untuk metode augmentasi RLC.
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