Analisis Sentimen Pengguna Twitter Terhadap Opini Terkait Kebijakan School From Home Pada Jenjang Pendidikan Taman Kanak-Kanak

Anggraeni, Anisa Martha (2021) Analisis Sentimen Pengguna Twitter Terhadap Opini Terkait Kebijakan School From Home Pada Jenjang Pendidikan Taman Kanak-Kanak. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Seluruh dunia tak terkecuali Indonesia sedang dilanda wabah penyakit Covid-19. Penyebaran yang begitu cepat mengharuskan pemerintah mengambil kebijakan salah satunya adalah pelaksanaan belajar daring atau School From Home (SFH). Kebijakan ini tentu menyebabkan perubahan sistem pembelajaran tak terkecuali untuk salah satu satuan pendidikan anak yaitu Taman Kanak-Kanak (TK). Banyak orang tua yang menuangkan keresahannya terkait kebijakan SFH yang dinilai tidak efektif untuk anak di media sosial seperti twitter. Menurut penelitian yang telah dilakukan oleh Samsir, dkk menunjukkan bahwa opini negatif terhadap kebijakan SFH tergolong tinggi yaitu sebesar 69%. Oleh karena itu dilakukan analisis sentimen menggunakan metode Naïve Bayes Classifier (NBC) untuk mengetahui sentimen dari pengguna twitter terhadap kebijakan SFH khususnya pada jenjang pendidikan TK. Hasil penelitian menunjukkan bahwa opini dengan sentimen negatif lebih mendominasi yaitu sebesar 69% yang artinya banyak masyarakat yang memiliki persepsi negatif terhadap kebijakan SFH pada jenjang pendidikan TK. Diperoleh ketepatan klasifikasi dengan nilai AUC sebesar 65,52% yang artinya fair classification dan G-Mean sebesar 67,52% yang artinya model dapat memisahakan sentimen negatif dan sentimen positif sebesar 67,52%.
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The whole world, including Indonesia, is being hit by an outbreak of the Covid-19 disease. The rapid spread requires the government to take policies, one of which is the implementation of online learning or School From Home (SFH). This policy certainly causes changes in the learning system, including for one of the children's education units, namely Kindergarten (TK). Many parents have expressed their concerns regarding the SFH policy which is considered ineffective for children on social media such as twitter. According to research conducted by Samsir, et al., it shows that negative opinion on the SFH policy is quite high at 69%. Therefore, a sentiment analysis was carried out using themethod Naïve Bayes Classifier (NBC)to find out the sentiments ofusers twitter towards SFH policies, especially at the level of kindergarten education. The results of the study indicate that opinions with negative sentiments dominate at 69%, which means that many people have negative perceptions of SFH policies at the kindergarten education level. The classification accuracy is obtained with anvalue AUC of 65,52%, which means fair classification and G-Mean of 67,52%, which means that the model can separate negative sentiment and positive sentiment of 67,52%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Naïve Bayes Classifier, School From Home, Naïve Bayes Classifier, School From Home, Sentiment Analysis
Subjects:	L Education > LB Theory and practice of education > LB1044.87 Internet in education (e-learning). Virtual reality in education. R Medicine > RA Public aspects of medicine > RA644.C67 COVID-19 (Disease)
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Anisa Martha Anggraeni
Date Deposited:	14 Aug 2021 12:17
Last Modified:	14 Aug 2021 12:17
URI:	http://repository.its.ac.id/id/eprint/86542

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