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IMPLEMENTASI BIDIRECTIONAL LONG SHORT TERM MEMORY (BiLSTM) UNTUK MENDETEKSI DEPRESI PADA AUGMENTASI TWEET BAHASA INDONESIA MENGGUNAKAN EASY DATA AUGMENTATION (EDA) DAN BACK TRANSLATION

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Depression is a mental disorder that can interfere with daily activities. One way that can be done to detect depression on social media, especially Twitter, is by classifying the text in the tweets that are shared. In classification, a large amount of data is needed so that the results obtained are optimal, but sometimes the availability of data is limited. Augmentation is one way that can be used to increase the amount of data. Several augmentation techniques that can be used, namely Easy Data Augmentation (EDA) and back translation. Augmentation techniques help deep learning-based classification methods work more optimally. Bidirectional Long Short Term Memory (BiLSTM) is a deep learning algorithm that can be used for text classification. The advantage of BiLSTM is that it can identify a pattern in a sentence because each word is processed sequentially. In this study, augmentation of Indonesian tweets used EDA and back translation followed by classification using the BiLSTM architecture. The stages in this study are data collection, text preprocessing, data augmentation, tokenization, pad sequences, training, testing, evaluation, analysis and interpretation of results, and making conclusions. The results obtained from this study, namely accuracy, precision, recall, and f1-score were 98.68% each. These results indicate that the BiLSTM architecture is able to classify text in determining depressed and not depressed classes very well.

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Inventory Code Barcode Call Number Location Status
2307000654T89692T896922023Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T896922023
Publisher
Indralaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya.,
Collation
ix, 81 hlm.; Ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
519.507
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Jurusan Matematika
Matematika Statistikal
Specific Detail Info
-
Statement of Responsibility
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No other version available

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