@thesis{thesis,
		author={Amborgang Sitorus  Zest},
		title ={SISTEM PENGENAL KATA UCAPAN BERBASIS SPEKTOGRAM MENGGUNAKAN METODE K-NEAREST NEIGHBORS},
		year={2023},
		url={http://eprints.stta.ac.id/1180/},
		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.}
		}