PERBANDINGAN METODE PERHITUNGAN JARAK DALAM ALGORITMA K-NEAREST NEIGHBOUR UNTUK KLASIFIKASI DATA PENYAKIT PADA MANUSIA

MUHAMMAD, HEZAR and Rini, Dian Palupi and Saputra, Danny Matthew (2022) PERBANDINGAN METODE PERHITUNGAN JARAK DALAM ALGORITMA K-NEAREST NEIGHBOUR UNTUK KLASIFIKASI DATA PENYAKIT PADA MANUSIA. Undergraduate thesis, Sriwijaya University.

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Abstract

One of the algorithms in Machine Learning principles that can be used to classify large amounts of data by producing clear and effective results is the K-Nearest Neighbour Algorithm. This research aims to prove which of the distance calculation formulas in the K-Nearest Neighbour Algorithm has an accuracy value with good performance in classifying human disease datasets. The proof is done by comparing the existing distance calculation formulas, namely Euclidean distance, Manhattan distance, Minkowsky distance and Chebyshev distance. The dataset uses data on diseases that are often experienced by humans such as Heart Attack, Breast Cancer, Diabetes and Stroke. The results show that Euclidean distance is the distance calculation formula that gets the most accuracy values with good performance compared to other distance calculation formulas. Even the highest accuracy results obtained can reach 90% with performance values namely Precision of 0.921053, Recall of 0.833333 and F-Measure of 0.875 with a K value of 3 for the classification of breast cancer datasets. So it is concluded that Euclidean distance is a distance calculation formula that is able to produce high accuracy values with good performance for classifying human disease datasets.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Machine Learning, Algoritma K-Nearest Neighbour, Euclidean distance, Manhattan distance, Minkowsky distance, Chebyshev distance, Serangan Jantung, Kanker Payudara, Diabetes, Stroke
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Mr. Hezar Muhammad
Date Deposited:	22 Jul 2022 04:10
Last Modified:	22 Jul 2022 04:10
URI:	http://repository.unsri.ac.id/id/eprint/74442

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