Skripsi

PENENTUAN PRODUK WHEY PROTEIN TERBAIK MENGGUNAKAN METODE SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE (SMART)

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Penilaian

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Spyware malware attacks are a significant threat to computer hardware and user privacy. To analyze and make it easier to recognize such attacks effectively, visualization techniques can provide valuable insights into their patterns and characteristics. This research using a machine learning algorithm, Support Vector Machine (SVM). The dataset used in this study is from the CIC MalMem 2022 which is benign data and spyware Transponder. Feature selection was applied to this study to identify the features that were most relevant in distinguishing between normal data and attacks. The results obtained using the SVM model with 3 SVM kernels are Linear, Polynomial, and Radial Basis Function (RBF). Based on the validation of the classification process using the SVM kernel got quite high results, where the Linear kernel got the most superior accuracy results, for 8 features accuracy that is 99.53%, 15 features accuracy is 99.77% and 24 features accuracy is 99.96%. Keyword: Spyware, Visualization, Support Vector Machine (SVM), Confusion Matrix

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003706	T99831	T1276502023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T1276502023

Publisher

Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xiiix, 91 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Metode Komputer Tertentu

Specific Detail Info

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Statement of Responsibility

FIRA

Other version/related

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