Skripsi

KLASIFIKASI SENTIMEN TERHADAP DATA TEXT JEJARING SOSIAL DENGAN TOPIK PEMBELAJARAN DARING MENGGUNAKAN LOGISTIC REGRESSION

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Penilaian

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The outbreak of Corona Virus Disease (Covid-19) which has hit more than 200 countries in the world, has presented its own challenges for educational institutions, especially higher education. Anticipating the transmission of the virus, the government has issued various policies, such as isolation, social and physical distancing to large-scale social restrictions (PSBB). This condition requires citizens to stay at home, study at home, so the classification of public sentiment towards online learning is deemed necessary. Community sentiments are recorded and collected from a social network, namely the Twitter social network where there are many public sentiments about online learning topics. The classification of these sentiments will use Natural Language Processing (NLP) Logistic Regression as the classification method. From the experiments conducted with 80% data split of training data with 20% of test data resulted in the highest Precision, Recall and F1-Score values, while for experiments with 20% data split of training data with 80% of test data yielded Precision, Recall and The lowest F1-Score. Data classification can be carried out properly using the Logistic Regression method, so that it can create a system that is able to classify sentiments with online learning topics. The results of the prediction of sentiment text data using the Logistic Regression method can be used as consideration or reference for government decision making in making policies regarding online learning topics.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002243	T53683	T536832021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T536832021

Publisher

Inderalaya : Fakultas Ilmu komputer, Prodi Sistem Komputer., 2021

Collation

xix, 70 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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