Perbandingan Metrik Jarak Pada Metode K Neirest Neighbor

MANGKUNEGARA, NOORDIN AS-SHIDDIQ and Rini, Dian Palupi and Saputra, Danny Matthew (2020) Perbandingan Metrik Jarak Pada Metode K Neirest Neighbor. Undergraduate thesis, Sriwijaya University.

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Abstract

Data mining adalah proses untuk mencari ilmu pengetahuan di dalam data. Klasifikasi adalah ilmu didalam data mining yang menganalisa data dengan membangun model untuk mendeskripsikan kelas data. K Nearest Neighbor (KNN) adalah algoritma klasifikasi yang berguna untuk mencari kelas dari data yang tidak diketahui. Metrik jarak memiliki peran yang penting dalam pengklasifikasian menggunakan metode K Nearest Neighbor (KNN), dimana metrik jarak berguna untuk menghitung jarak antara data training dan data testing. Oleh karena itu, penelitian ini dilakukan untuk menganalisa dan membandingkan metrik jarak euclidean, manhattan dan chebycev distance dalam melakukan perhitungan jarak pada metode K Nearest Neighbor (KNN). Penelitian ini menghasilkan metrik jarak manhattan distance yang memiliki nilai akurasi, precision, dan recall terbaik pada hasil pengklasifikasian menggunakan metode K Nearest Neighbor. Kata Kunci : Data Mining, Klasifikasi, K Nearest Neighbor, Metrik Jarak

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Data Mining, Klasifikasi, K Nearest Neighbor, Metrik Jarak
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.Z55 Apache Hadoop (Computer file) Electronic data processing--Distributed processing. File organization (Computer science) Data mining. Streaming technology (Telecommunications)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 7312 not found.
Date Deposited:	12 Aug 2020 07:59
Last Modified:	12 Aug 2020 07:59
URI:	http://repository.unsri.ac.id/id/eprint/32905

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