KLASIFIKASI BERITA HOAX BAHASA INDONESIA MENGGUNAKAN METODE LSTM (LONG SHORT TERM MEMORY)

FADLURRAHMAN, FAIQ and Utami, Alvi Syahrini and Kurniati, Junia (2024) KLASIFIKASI BERITA HOAX BAHASA INDONESIA MENGGUNAKAN METODE LSTM (LONG SHORT TERM MEMORY). Undergraduate thesis, Sriwijaya University.

	Text RAMA_55201_09021382025119.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_55201_09021382025119_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (4MB) | Request a copy
	Text RAMA_55201_09021382025119_0022127804_0008118205_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (842kB)
	Text RAMA_55201_09021382025119_0022127804_0008118205_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (242kB) | Request a copy
	Text RAMA_55201_09021382025119_0022127804_0008118205_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (442kB) | Request a copy
	Text RAMA_55201_09021382025119_0022127804_0008118205_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (293kB) | Request a copy
	Text RAMA_55201_09021382025119_0022127804_0008118205_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (280kB) | Request a copy
	Text RAMA_55201_09021382025119_0022127804_0008118205_06.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (33kB) | Request a copy
	Text RAMA_55201_09021382025119_0022127804_0008118205_07_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (93kB) | Request a copy
	Text RAMA_55201_09021382025119_0022127804_0008118205_08_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (120kB) | Request a copy

Abstract

Hoax news poses a serious threat in the current digital environment as it harms society by disseminating false and misleading information. Detecting hoax news is essential to maintain the integrity of information, prevent the spread of misinformation, and safeguard public safety. This research adopts a classification approach to hoax news based on LSTM (Long Short-Term Memory) with the utilization of Word2Vec for word representation. This approach is chosen because LSTM can comprehend the context and sequence of words in a sentence, while Word2Vec provides a meaningful representation of words. By combining the strengths of both methods, this study aims to contribute to improving the detection of Indonesian hoax news. The data used in this study consist of 17,656 records, with 2 classes namely real and hoax. The research findings indicate that the LSTM model with the best configuration, including a learning rate of 0.0001, dropout of 0.2, hidden layer of 64, and batch size of 64, achieves high accuracy in classifying hoax news. The model evaluation using accuracy metric of 94%, recall of 96%, precision of 93%, and f1-score of 94% confirms the reliability of this model in identifying and distinguishing hoax news with a high level of accuracy. Key word : Long Short Term Memory, Word2Vec, Hoax News, News Classification

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Long Short Term Memory, Word2Vec, Hoax News, News Classification
Subjects:	Q Science > Q Science (General) > Q1-295 General Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Faiq Fadlurrahman
Date Deposited:	04 Apr 2024 01:55
Last Modified:	04 Apr 2024 01:55
URI:	http://repository.unsri.ac.id/id/eprint/143114

Actions (login required)

View Item