Skripsi

PENERAPAN ALGORITME NAIVE BAYES CLASSIFIERS PADA SISTEM PENDETEKSI BERITA HOAX BERBAHASA INDONESIA

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Penilaian

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Hoax is a recent issues that many people use as political weapons, unrelevant truth, or distributed purposely false news. Along with the growth of technology which is too fast, at this time fake news are very easily spread over through the internet, such as social media. Many cases of deception caused by the news that someone receives are fake news. Researches in this field has so much popped out of with variety of algorithms, however the application of algorithms in a hoax news detection system that can be used by everyone is still rare to find. Therefore, to make it easier for someone to know the category of news received, this research builds a hoax news detection system that applies Naive Bayes algorithm. The Naive Bayes Classifiers algorithm is a classification method that using probability and statistics to predict the odds based on existing data. In the application of this algorithm, been done by the test to determine the effect of the algorithm that used in this study. The test has was carried out 9 times on 1000 news texts as knowledge base. From the tested, was determined by the percentage of recall value is 82%, precision is 88%, accuracy is 78% and f-measure is 85%. With the results of that performance measurement, hopefully this hoax news detection system can be used as a reference in knowing the type of news text that will approve by someone.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002726	T49533	T495332021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T495332021

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

xviii, 75 hlm,: lus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Pemrosesan Data, Teknik Informatika
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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