SISTEM PENGENAL KATA UCAPAN BERBASIS SPEKTOGRAM MENGGUNAKAN METODE K-NEAREST NEIGHBORS

Amborgang Sitorus, Zest (2023) SISTEM PENGENAL KATA UCAPAN BERBASIS SPEKTOGRAM MENGGUNAKAN METODE K-NEAREST NEIGHBORS. Thesis thesis, Institut Teknologi Dirgantara Adisutjipto.

	Text (ABSTRAK) 18010002_ABSTRAK.pdf Download (232kB)
	Text (BAB I) 18010002_BAB 1.pdf Download (210kB)
	Text (DAFTAR PUSTAKA) 18010002_DAFTAR PUSTAKA.pdf Download (190kB)
	Text (FULL SKRIPSI) 18010002.pdf Restricted to Repository staff only Download (5MB)

Abstract

A speech recognition system is an application to recognize spoken words through audio inputs. On the other hand, spectrograms are visual representations of audio signals and effectively represent words numerically. In this research, for developed a spectrogram-based speech recognition system using machine learning’s K-Nearest Neighbors (KNN) method to recognize aerospace terminologies, some uncommon for ordinary people. This study used 300 audio signal data consisting of two categories, namely 15 spectrogram data for aerospace terminologies and 15 spectrogram data for non-aerospace ones, each taken ten times. By conducting a 70:30 training and testing scheme, strengthened by k-fold cross-validation. The optimum K for KNN is 13, which can get an accuracy of 75,56% with a precision of 75,56%, recall of 75,56%, and F-1 score of 75,56%. With that accuracy, our developed intelligent system is considered as a good system.

Actions (login required)

View Item

Downloads