Analisis Sentimen Pada Media Sosial X Terhadap Implementasi Kurikulum Merdeka Menggunakan Metode Fasttext Dan Long Short-Term Memory (LSTM)

Pangestu, Arif Fajar (2024) Analisis Sentimen Pada Media Sosial X Terhadap Implementasi Kurikulum Merdeka Menggunakan Metode Fasttext Dan Long Short-Term Memory (LSTM). Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Curriculum changes are a must to keep up with the times and ensure educational standards are met. However, these changes often cause confusion among educators and parents, disrupting the educational process. The Merdeka Curriculum, which was introduced as an important innovation in Indonesian education, provides a framework that is more flexible and suited to current needs. However, as the number of students increases, the challenges faced by the Indonesian education system also increase. This research aims to analyze opinions that appear on social media X regarding the implementation of the Merdeka Curriculum, using the FastText word embedding method and the Long Short-Term Memory (LSTM) classification model. Two test datasets were used in this study, the first containing 7,500 entries and the second 3,000 entries. This research also tested eight different scenarios, with a combination of Continuous Bag of Words (CBOW) and Skip-Gram feature extraction methods, as well as variations in data splitting of 80:20 and 85:15. The results show a high level of accuracy in all scenarios, above 85%. These findings reveal the dominance of negative sentiment in each category observed during the implementation of the Merdeka Curriculum, which indicates the existence of several challenges or obstacles in the acceptance and application of the curriculum in various educational environments.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Rahmat, Basuki	NIDN0023076907	basukirahmat.if@upnjatim.ac.id
Thesis advisor	Sihananto, Andreas Nugroho	NIDN0012049005	andreas.nugroho.jarkom@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Arif Fajar Pangestu

Date Deposited:

04 Jun 2024 08:10

Last Modified:

04 Jun 2024 08:10

URI:

https://repository.upnjatim.ac.id/id/eprint/24241