KLASIFIKASI TEKS KOMENTAR PRODUK PADA TOKOPEDIA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM)

RABBANI, MUHAMMAD ARIQ and Abdiansah, Abdiansah and Rodiah, Desty (2023) KLASIFIKASI TEKS KOMENTAR PRODUK PADA TOKOPEDIA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM). Undergraduate thesis, Sriwijaya University.

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Abstract

Text data in comments is often unstructured, so to classify comments requires the application of appropriate methods. The method used in this study involves the use of algorithms from Deep Learning, namely Long Short-Term Memory (LSTM) to classify texts. In this study using 4060 imbalance dataset so that an Upsampling method is needed to adjust the distribution of data, so that the distribution of data changes to 8709, then it will be divided into 80% training data and 20% for testing data. The use of Word2Vec word embedding was also applied to this study. After tuning the LSTM hyperparameters, the final results were obtained using the adam optimizer, dropout layer of 0.5, hidden units of 400, 200, and 100 on each LSTM layer used, epochs of 100, batch size of 32 so that the accuracy obtained reached 80% with the average value of macro precision, macro recall, and macro F-measure of 36%, 36%, respectively. and 34% and accuracy obtained by 42%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Komentar Teks, Long Short-Term Memory
Subjects:	T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Ariq Rabbani
Date Deposited:	14 Aug 2023 06:04
Last Modified:	14 Aug 2023 06:04
URI:	http://repository.unsri.ac.id/id/eprint/127117

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