Analisis Sentimen Pengguna Twitter Terhadap Metaverse Menggunakan Metode Long Short-Term Memory dan Word2Vec
Kata Kunci:
analisis sentimen, twitter, metaverse, LSTM, deep learning, word2vecAbstrak
Naskah ini akan diterbitkan di Jurnal Teknologi Informasi dan Ilmu Komputer
Referensi
Andriani, F. (2018). Fenomena Social Climber Melalui Twitwar. Jurnal Pustaka Komunikasi, 1(2), 349–360.
Brownlee J. (2021). Machine Learning Mastery. Https://Machinelearningmastery.Com/Adam-Optimization-Algorithm-for-Deep-Learning/.
Damar, M. (2021). Metaverse shape of your life for future: A bibliometric snapshot. Journal of Metaverse, 1(1), 1–8.
Díaz, J., Saldaña, C., & Avila, C. (2020). Virtual world as a resource for hybrid education. International Journal of Emerging Technologies in Learning (IJET), 15(15), 94–109.
Haddi, E., Liu, X., & Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26–32.
Ibrohim, M. O., Sazany, E., & Budi, I. (2019). Identify abusive and offensive language in indonesian twitter using deep learning approach. Journal of Physics: Conference Series, 1196(1), 012041.
Isnain, A. R., Sihabuddin, A., & Suyanto, Y. (2020). Bidirectional long short term memory method and Word2vec extraction approach for hate speech detection. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(2), 169–178.
Li, Y., & Xiong, D. (2022). The metaverse phenomenon in the teaching of digital media art major. 2021 Conference on Art and Design: Inheritance and Innovation (ADII 2021), 348–353.
Narayanan, R., Liu, B., & Choudhary, A. (2009). Sentiment analysis of conditional sentences. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 180–189.
Nugraha, F. A., Harani, N. H., & Habibi, R. (2020a). Analisis Sentimen Terhadap Pembatasan Sosial Menggunakan Deep Learning. Kreatif.
Nugraha, F. A., Harani, N. H., & Habibi, R. (2020b). Analisis Sentimen Terhadap Pembatasan Sosial Menggunakan Deep Learning. Kreatif.
Prabowo, Y. D., Marselino, T. L., & Suryawiguna, M. (2019). Pembentukan vector space model bahasa indonesia menggunakan metode word to vector. Jurnal Buana Informatika, 10(1), 29–40.
Pradana, Y. A., Cholissodin, I., & Kurnianingtyas, D. (2023). Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(5), 2389–2397.
Rahman, M. Z., Sari, Y. A., & Yudistira, N. (2021). Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(11), 5120–5127.
SILVI, F. (2023). IMPLEMENTASI METODE HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)–LONG SHORT TERM MEMORY (LSTM) DALAM PERAMALAN HARGA SAHAM BANK BRI.
Widayat, W. (2021). Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning. Jurnal Media Informatika Budidarma, 5(3), 1018–1026.
Andriani, F. (2018). Fenomena Social Climber Melalui Twitwar. Jurnal Pustaka Komunikasi, 1(2), 349–360.
Brownlee J. (2021). Machine Learning Mastery. Https://Machinelearningmastery.Com/Adam-Optimization-Algorithm-for-Deep-Learning/.
Damar, M. (2021). Metaverse shape of your life for future: A bibliometric snapshot. Journal of Metaverse, 1(1), 1–8.
Díaz, J., Saldaña, C., & Avila, C. (2020). Virtual world as a resource for hybrid education. International Journal of Emerging Technologies in Learning (IJET), 15(15), 94–109.
Haddi, E., Liu, X., & Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26–32.
Ibrohim, M. O., Sazany, E., & Budi, I. (2019). Identify abusive and offensive language in indonesian twitter using deep learning approach. Journal of Physics: Conference Series, 1196(1), 012041.
Isnain, A. R., Sihabuddin, A., & Suyanto, Y. (2020). Bidirectional long short term memory method and Word2vec extraction approach for hate speech detection. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 14(2), 169–178.
Li, Y., & Xiong, D. (2022). The metaverse phenomenon in the teaching of digital media art major. 2021 Conference on Art and Design: Inheritance and Innovation (ADII 2021), 348–353.
Narayanan, R., Liu, B., & Choudhary, A. (2009). Sentiment analysis of conditional sentences. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 180–189.
Nugraha, F. A., Harani, N. H., & Habibi, R. (2020a). Analisis Sentimen Terhadap Pembatasan Sosial Menggunakan Deep Learning. Kreatif.
Nugraha, F. A., Harani, N. H., & Habibi, R. (2020b). Analisis Sentimen Terhadap Pembatasan Sosial Menggunakan Deep Learning. Kreatif.
Prabowo, Y. D., Marselino, T. L., & Suryawiguna, M. (2019). Pembentukan vector space model bahasa indonesia menggunakan metode word to vector. Jurnal Buana Informatika, 10(1), 29–40.
Pradana, Y. A., Cholissodin, I., & Kurnianingtyas, D. (2023). Analisis Sentimen Pemindahan Ibu Kota Indonesia pada Media Sosial Twitter menggunakan Metode LSTM dan Word2Vec. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(5), 2389–2397.
Rahman, M. Z., Sari, Y. A., & Yudistira, N. (2021). Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(11), 5120–5127.
SILVI, F. (2023). IMPLEMENTASI METODE HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)–LONG SHORT TERM MEMORY (LSTM) DALAM PERAMALAN HARGA SAHAM BANK BRI.
Widayat, W. (2021). Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning. Jurnal Media Informatika Budidarma, 5(3), 1018–1026.
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