KLASIFIKASI SPAM EMAIL MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DAN CHI SQUARE

ULVIYANA, ULVIYANA and Stiawan, Deris (2020) KLASIFIKASI SPAM EMAIL MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DAN CHI SQUARE. Undergraduate thesis, Sriwijaya University.

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Abstract

Email spam classification on the Spambase dataset obtained from the UCI Machine Learning Repository using the K-Nearest Neighbor (KNN) algorithm during the classification process of spam and non spam data. This method classifies objects based on their nearest neighbor distance (k). Where the value of k can be determined using distance measurement or the euclidean function. In this study, pre-processing was carried out by dividing the data into training and testing data, normalizing the data, and selecting features by applying the Chi Square algorithm. The results of the accuracy of the classification process using the K-Nearest Neighbor algorithm before applying the Chi Square feature selection when the k = 1 value is 89.58%. While the accuracy value when classification uses the K-Nearest Neighbor algorithm after applying the Chi Square algorithm as a feature selection is equal to 90.45%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Spam, K-Nearest Neighbor, Seleksi Fitur, Chi Square.
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control) T Technology > TA Engineering (General). Civil engineering (General) > TA174.A385 Engineering design--Data processing. Manufacturing processes--Data processing. Computer integrated manufacturing systems. Manufacturing processes--Automation. CAD/CAM systems.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 8194 not found.
Date Deposited:	24 Sep 2020 06:46
Last Modified:	24 Sep 2020 06:46
URI:	http://repository.unsri.ac.id/id/eprint/35604

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