KLASIFIKASI SPAM EMAIL MENGGUNAKAN ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN DECISION TREE

SHOLIHAH, SITI PEBSYA ROISATUN and Stiawan, Deris (2020) KLASIFIKASI SPAM EMAIL MENGGUNAKAN ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN DECISION TREE. Undergraduate thesis, Sriwijaya University.

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Abstract

Email spam is a topic of problem that will continue to increase because it is easy and cheap to send email, which can be annoying and time-consuming for users. For that reason, the classification of spam emails is still challenging because there are still a lot of spam emails. This research was conducted on two email spam datasets, namely the Spambase dataset obtained from UCI Machine Learning, and the Emails dataset obtained from Kaggle. Spam classification is done using the Decision Tree algorithm. The classification process is carried out after the pre-processing stage, namely by doing text mining (Email dataset only), separating data, scaling data, and applying the Principal Component Analysis (PCA) algorithm as a sign of the number of features in the dataset based on the value that is important the influence of each feature. . The results of the classification using the Decision Tree Algorithm are 93.16% for the Spambase dataset and 94.24% for the Emails dataset. Meanwhile, the application of PCA to the Decision Tree resulted in a value of 90% for the Spambase dataset and 89.53% for the Emails dataset.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Spam Email, Klasifikasi, Decision Tree, Principal Component Analysis (PCA)
Subjects:	T Technology > T Technology (General) > T10.5-11.9 Communication of technical information T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis 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 8193 not found.
Date Deposited:	24 Sep 2020 06:42
Last Modified:	24 Sep 2020 06:42
URI:	http://repository.unsri.ac.id/id/eprint/35599

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