Komparasi Metode Machine Learning dan Metode Non Machine Learning untuk Estimasi Usaha Perangkat Lunak

Ega Kartika Adhitya; Romi Satria Wahono; Hendro Subagyo

Komparasi Metode Machine Learning dan Metode Non Machine Learning untuk Estimasi Usaha Perangkat Lunak

Ega Kartika Adhitya, Romi Satria Wahono, Hendro Subagyo

Abstract

Estimasi usaha adalah proses yang sangat penting dalam kesuksesan pelaksanaan suatu proyek perangkat lunak. Memilih metode estimasi yang sesuai dengan proyek yang akan dikerjakan diperlukan pemahaman yang jelas tentang metode-metode estimasi usaha yang salah satunya mengetahui kelemahan dan kelebihan dari masing - masing metode tersebut. Dalam penelitian ini dikaji dua kelompok besar metode estimasi biaya perangkat lunak yakni metode machine learning dan metode non machine learning untuk mengetahui metode mana yang paling baik. Pada penelitian pertama mengunakan metode machine learning dapat kita ketahui bahwa K-NN(k-nearnest neigbhors) mempunyai nilai RSME yang paling baik. Pada penelitian Kedua mengunakan metode non machine learning Dari hasil tersebut dapat kita ketahui bahwa FP (fungsion point ) mempunyai nilai RSME yang paling baik. Pada Penelitian Ketiga diantara metode machine learning dan non machine learning didapatkan K-NN yang mempunyai nilai RSME yang paling baik. Pada penelitian Keempat penambahan seleksi atribut forward selection mendapatkan hasil yang paling baik untuk digunakan pada estimasi usaha perangkat lunak.

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