Klasifikasi Penerimaan Tenaga Kerja Tertutup Menggunakan Algoritma K-Nearest Neighbor (K-NN). (Classification Of Labor In Closed Recruitment Using The K-Nearest Neighbor (KNN)Algorithm).

Pratama, Rizal Kurnia (2023) Klasifikasi Penerimaan Tenaga Kerja Tertutup Menggunakan Algoritma K-Nearest Neighbor (K-NN). (Classification Of Labor In Closed Recruitment Using The K-Nearest Neighbor (KNN)Algorithm). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Companies that have implemented the use of technology will not be separated from processing data or information, but not all companies maximize the processing of the data they have, one of which is CV. Arfa Nusantara Teknologi, data on prospective workers entering the company is not subjected to further processing. This research tries to process data on prospective workers who enter the company with the aim of classifying data on prospective workers based on the variables of technical test scores from the assessment team, total skill weight scores, minimum salary requested and number of years of work experience using an algorithm. K-Nearest Neighbor (KNN), with this algorithm will produce several data classifications of prospective workers, namely the best, quite good and not so good, which can help the company assessment team in making decisions to accept the best prospective workers for the company and find out the level of accuracy of the algorithm. Apart from that, this research tries several numbers of KNN neighbor retrievals and applies K-Fold Cross Validation to determine the optimal classification model. In the optimal model that was successfully obtained, the average value of accuracy, precision and recall was 100% in the division k = 4 and k = 5 in the 10 K-Fold Cross Validation retrieval trials. Meanwhile, the best neighbor value for the KNN algorithm is k = 7. In testing the System Usability Scale, the score obtained from respondents was 80.21, which is in the B value category.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Classification, K-Nearest Neighbor, K-Fold Cross Validation, Labor Recruitment
Subjects:	T Technology > T Technology (General)
Divisions:	Fakultas Teknik > Program Studi Teknik Informatika
Depositing User:	Rizal Kurnia Pratama
Date Deposited:	10 Jul 2024 01:30
Last Modified:	10 Jul 2024 01:30
URI:	http://repository.untag-sby.ac.id/id/eprint/29035

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