ANALISIS SENTIMEN MASYARAKAT TERHADAP PROGRAM PERTUKARAN MAHASISWA MERDEKA PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER

GUSTRIYO, IRFAN (2023) ANALISIS SENTIMEN MASYARAKAT TERHADAP PROGRAM PERTUKARAN MAHASISWA MERDEKA PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER. S1 thesis, Universitas Muhammadiyah Purwokerto.

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Abstract

The use of information technology is growing rapidly marked by public opinion that can be conveyed indefinitely through social media. Microblogging services such as Twitter allow users to express opinions, feelings, experiences and other things that concern them. The topics discussed by the public on Twitter also vary, including the Pertukaran Mahasiswa Merdeka program which is one of the Merdeka Belajar Kampus Merdeka (MBKM) programs. This program was made with the aim of one of them being to study across campuses (domestic and overseas), so that cross-cultural and ethnic brotherhoods are built, with a credit transfer system of 20 credits or for 1 semester. This program raises sentiments in the community, the large number of opinions that require classification according to sentiments owned so that it is easy to get opinion trends towards the Pertukaran Mahasiswa Merdeka program whether they tend to have positive or negative opinions. In conducting the analysis, the data was obtained from the scraping process using the Python programming language. Data from the results of scraping were obtained as many as 11,319 which were then pre-processed by going through the stages of cleansing, case folding, tokenizing, normalization, filtering, and stemming. The data labeling process was carried out using Lexicon Senticnet 7 by obtaining a total of 7.107 positive sentiment data and 1.850 negative sentiment data. The classification method used is the Naïve Bayes Classifier Algorithm and obtains an Accuracy value of 82%. Confusion Matrix results get the Precision score of 84%, the Recall of 95% and the F1-Score of 89%.

Item Type: Thesis (S1)
Uncontrolled Keywords: pertukaran mahasiswa merdeka, twitter, sentiment analysis, classification, naïve bayes classifier
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Fakultas Teknik > Teknik Informatika S1
Depositing User: Catur Indra H.
Date Deposited: 15 Feb 2023 02:25
Last Modified: 15 Feb 2023 02:25
URI: https://repository.ump.ac.id:80/id/eprint/15177

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