Analisis Sentimen Komentar Youtube Tentang Resesi Global 2023 Menggunakan LSTM
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Jan 10, 2024
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The COVID-19 pandemic that occurred in 2020 caused the economy to decline due to declining economic activity, making companies decide to lay off some workers so that the unemployment rate increased. This makes economic activists predict that there will be a global recession in 2023, Youtube as a video-sharing platform is one of the places to discuss through the comment’s column. The increasing number of YouTube users is one of the references for sentiment analysis using data taken from video comments. Long Short-Term Memory (LSTM) is used to perform sentiment analysis, with 500 data divided into training data and test data, resulting in the highest accuracy of 90% training data and 76% test data. This result is obtained from the configuration of the LSTM architecture with dense layers using sigmoid activation and 50 epochs.
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Hendrawan, A., & Sela, E. I. (2024). Analisis Sentimen Komentar Youtube Tentang Resesi Global 2023 Menggunakan LSTM. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(1), 587-593. https://doi.org/10.35870/jimik.v5i1.526
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Nugraha, F. A., Harani, N. H., & Habibi, R. (2020). Analisis Sentimen Terhadap Pembatasan Sosial Menggunakan Deep Learning. Kreatif.
Novitasari, I., Kurniawan, T. B., & Dewi, D. A. (2022). Analisis Sentimen Masyarakat Terhadap Tweet Ruang Guru Menggunakan Algoritma Naive Bayes Classifier (NBC). Jurnal Mantik, 6(3), 3308-3318.
Lestandy, M., Abdurrahim, A., & Syafa’ah, L. (2021). Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 802-808. DOI: https://doi.org/10.29207/resti.v5i4.3308.
Kumar, A., Sangwan, S. R., Arora, A., Nayyar, A., & Abdel-Basset, M. (2019). Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE access, 7, 23319-23328. DOI: 10.1109/ACCESS.2019.2899260.
Fatmasari, R., Ayu, V. M., Anto, H., & Yulianto, L. D. (2022). Analisis Sentimen Dalam Pengkategorian Komentar Youtube Terhadap Layanan Akademik dan Non-Akademik Universitas Terbuka Untuk Prediksi Kepuasan. Building of Informatics, Technology and Science (BITS), 4(2), 395-404. DOI: https://doi.org/10.47065/bits.v4i2.1738.
Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan metode naïve bayes dan support vector machine pada analisis sentimen twitter. SMATIKA Jurnal: STIKI Informatika Jurnal, 10(02), 71-76. DOI : https://doi.org/10.32664/smatika.v10i02.455.
Azhar, R., Surahman, A., & Juliane, C. (2022). Analisis Sentimen Terhadap Cryptocurrency Berbasis Python TextBlob Menggunakan Algoritma Naïve Bayes. J-SAKTI (Jurnal Sains Komputer dan Informatika), 6(1), 267-281. DOI: http://dx.doi.org/10.30645/j-sakti.v6i1.443.
Parlika, R., Pradika, S. I., Hakim, A. M., & NM, K. R. (2020). Analisis Sentimen Twitter Terhadap Bitcoin dan Cryptocurrency Berbasis Python TextBlob. Jurnal Ilmiah Teknologi Informasi dan Robotika, 2(2), 33-37.
Setiawati, I., & Sela, E. I. (2022). Classification of Facial Expression Using Principal Component Analysis (PCA) Method and Support Vector Machine (SVM). International Journal of Computer and Information Technology (2279-0764), 11(1).
Khumaidi, A., Raafi'udin, R., & Solihin, I. P. (2020). Pengujian Algoritma Long Short Term Memory untuk Prediksi Kualitas Udara dan Suhu Kota Bandung. Jurnal Telematika, 15(1), 13-18. DOI: https://doi.org/10.61769/telematika.v15i1.340.