Sentiment Analysis Of Development Jakarta-Bandung High-Speed Train Using Twitter Social Media With BNN Method
DOI:
https://doi.org/10.30865/jurikom.v9i3.4135Keywords:
Jakarta-Bandung High-Speed Train, Sentiment Analysis, Twitter, TF-IDF, Backpropagation Neural NetworkAbstract
The Jakarta-Bandung high-speed train is one of the infrastructure development projects currently being carried out by the Indonesian government. The project is a large project that requires a long processing time and very large costs. Therefore, infrastructure development has reaped a lot of public opinions, both positive and negative. The purpose of writing this Final Project is to analyze sentiment on public opinion about the construction of the Jakarta-Bandung high-speed train. With data sourced from Twitter social media, the data will be analyzed in three classes, namely positive, negative, and neutral classes where the weighting will use the TF-IDF. The classification method used in this study is the Backpropagation Neural Network method. The best results were obtained in this study using a hyper tuning scenario with an accuracy of 74.56%.
References
“High-Speed Tracks | High Speed Rail Alliance.†[Online]. Available: https://www.hsrail.org/high-speed-tracks
“Jakarta-Bandung High-Speed Rail - Railway Technology.†[Online]. Available: https://www.railway-
technology.com/projects/jakarta-to-bandung-high-speed-rail/
“Kereta Cepat Jakarta Bandung - Kereta Cepat Indonesia China.†[Online]. Available: https://kcic.co.id/proyek/kereta-progres-pengembangan/
T. Ahmad, “Dampak Negara Danai Kereta Cepat : Subsidi Naik , Tiket Mahal,†pp. 1–7, 2021.
O. R. Kasali, “Menyoal Ribut-ribut Kereta Cepat Jakarta- Bandung,†pp. 1–10, 2021.
B. Liu, “Introduction,†in Sentiment Analysis, Cambridge University Press, 2020, pp. 1–17. doi: 10.1017/9781108639286.002.
H. Jiang, P. Lin, and M. Qiang, “Public-Opinion Sentiment Analysis for Large Hydro Projects,†J. Constr. Eng. Manag., vol. 142, no. 2, p. 05015013, 2016, doi: 10.1061/(asce)co.1943-7862.0001039.
Z. Reyhana, K. Fithriasari, M. Atok, and N. Iriawan, “Linking Twitter Sentiment Knowledge with Infrastructure Development,†Matematika, vol. 34, no. 3, pp. 91–102, 2018, doi: 10.11113/matematika.v34.n3.1142.
H. Tsaniya, R. Rosadi, and A. S. Abdullah, “Sentiment analysis towards Jokowi`s government using twitter data with convolutional neural network method,†J. Phys. Conf. Ser., vol. 1722, no. 1, 2021, doi: 10.1088/1742-6596/1722/1/012017.
T. Hendrawati and C. P. Yanti, “Analysis of Twitter Users Sentiment against the Covid-19 Outbreak Using the Backpropagation Method with Adam Optimization,†J. Electr. Electron. Informatics, vol. 5, no. 1, p. 1, 2021, doi: 10.24843/jeei.2021.v05.i01.p01.
M. D. Sapanta, J. T. Elektro, F. T. Industri, and U. I. Indonesia, “Backpropagation Pada Studi Peramalan Beban Menggunakan Metode Artificial Neural Network,†2018.
G. Z. Nabiilah, S. Al Faraby, and M. Dwifebri Purbolaksono, “Classification of Hadith Topic of Indonesian Translation Using K-Nearest Neighbor and Chi-Square,†Int. J. Inf. Commun. Technol., vol. 7, no. 2, pp. 11–22, 2021, doi: 10.21108/ijoict.v7i2.573.
VINDI DWI ANTONIO, “Analisis Kinerja Ekstrasi Fitur Tf-Idf (Term Frequency – Inverse Document Frequency) Untuk Algoritma Klasifikasi Stochastic Gradient Descent Pada Analisis Sentimen Teks Indonesia,†pp. 7–37, 2021.
V. Amrizal, “Penerapan Metode Term Frequency Inverse Document Frequency (Tf-Idf) Dan Cosine Similarity Pada Sistem Temu Kembali Informasi Untuk Mengetahui Syarah Hadits Berbasis Web (Studi Kasus: Hadits Shahih Bukhari-Muslim),†J. Tek. Inform., vol. 11, no. 2, pp. 149–164, 2018, doi: 10.15408/jti.v11i2.8623.
S. P. Siregar and A. Wanto, “Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting),†IJISTECH (International J. Inf. Syst. Technol., vol. 1, no. 1, p. 34, 2017, doi: 10.30645/ijistech.v1i1.4.
J. Brownlee, “How to Code a Neural Network with Backpropagation In Python (from scratch),†Machine Learning Mastery. p. 1, 2016. [Online]. Available: https://machinelearningmastery.com/implement-backpropagation-algorithm-scratch-python/
E. Eka Patriya, “Implementasi Support Vector Machine Pada Prediksi Harga Saham Gabungan (Ihsg),†J. Ilm. Teknol. dan Rekayasa, vol. 25, no. 1, pp. 24–38, 2020, doi: 10.35760/tr.2020.v25i1.2571.
F. A. Hizham, Y. Nurdiansyah, and D. M. Firmansyah, “Implementasi Metode Backpropagation Neural Network (BNN) dalam Sistem Klasifikasi Ketepatan Waktu Kelulusan Mahasiswa (Studi Kasus: Program Studi Sistem Informasi Universitas Jember),†Berk. Sainstek, vol. 6, no. 2, p. 97, 2018, doi: 10.19184/bst.v6i2.9254.
