Image of IMPLEMENTASI ALGORITMA RECURRENT NEURAL NETWORK DENGAN ARSITEKTUR LONG SHORT-TERM MEMORY DALAM KLASIFIKASI SENTIMEN PENYELENGGARAAN FIFA WORLD CUP 2022 QATAR

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IMPLEMENTASI ALGORITMA RECURRENT NEURAL NETWORK DENGAN ARSITEKTUR LONG SHORT-TERM MEMORY DALAM KLASIFIKASI SENTIMEN PENYELENGGARAAN FIFA WORLD CUP 2022 QATAR

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The text classification of social media posts poses certain challenges due to the linguistic diversity that exists, making it an interesting problem to research. This research was conducted to classify sentiments towards the opinion of the community. The use of the RNN algorithm which stores previous data for the latest output results is suitable in this study because it can process sequential data in the form of text well in accordance with this study, although it is unable to store these data for a long time. The LSTM architecture can complement the shortcomings of RNNs which cannot predict a word based on past information that has been stored for a long time. Thus, LSTM can make the data classification process more efficient because it is able to remember information that has been stored for a long time and delete information that is no longer relevant. The data used in this study amounted to 440 data with 200 positive classes, 150 neutral classes, and 90 negative classes. The process of making the model was carried out in several scenarios by tuning the learning rate and batch size parameters, each of which had a value of 0.1 to 0.0001 and 8 to 64. The best model that was obtained was a model with a learning rate value = 0.001 and a batch size = 16 which has a high level of accuracy. by 69.32%.

Availability
Inventory Code Barcode Call Number Location Status
2307002731T123339T1233392023Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1233392023
Publisher
Indralaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xvi, 101 hlm.; Iluis.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.754
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Jurusan Teknik Informatika
Sosial media
Specific Detail Info
-
Statement of Responsibility
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No other version available

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