Pemetaan Sentimen Pengguna Media Sosial dalam Evaluasi Quality of Experience Kinerja Layanan Video Streaming

Penulis

DOI:

https://doi.org/10.23887/jstundiksha.v13i1.61570

Kata Kunci:

QoE, big data, Sentiment Analysis, Video Streaming, User Perception

Abstrak

Teknologi pengolah Big Data saat ini mengalami kemajuan dalam berbagai hal, mulai dari teknologi pendukung hingga area implementasi pemanfaatan Big Data itu sendiri. Salah satu sumber Big Data yang terus bertambah adalah data pada media sosial. Jumlah pengguna media sosial erat kaitannya dengan jumlah pengguna layanan video streaming di Indonesia yang mengalami peningkatan sekitar 25 persen pada tahun 2022, dan mencapai 83 juta jumlah pengguna. Untuk menjamin kualitas komunikasi data yang baik, diperlukan pengukuran QoE yang baik, efisien dan akurat. Jumlah pengguna yang sangat banyak juga dapat memberikan peluang terkait jenis interaksi pengguna dalam media sosial dan sentimen pengguna dalam memberikan umpan balik secara natural terkait kepuasan layanan yang diterima. Dalam penelitian telah dipetakan persepsi pengguna dalam pengukuran QoE berdasarkan komentar pengguna layanan pada media sosial. Hasil dari sentimen pengguna layanan yang telah dipetakan menunjukan bahwa aspek sistem dan konten penyedia layanan dengan jumlah 59,43% pada layanan video streaming. Hal tersebut menjadi faktor yang paling banyak disampaikan pengguna dalam media sosial.

Referensi

Akhtar, Z., & Falk, T. H. (2017). Audio-Visual Multimedia Quality Assessment: A Comprehensive Survey. IEEE Access, 5, 21090–21117. https://doi.org/10.1109/ACCESS.2017.2750918.

Akhtar, Z., Siddique, K., Rattani, A., Lutfi, S. L., & Falk, T. H. (2019). Why is multimedia quality of experience assessment a challenging problem? IEEE Access, 7, 117897–117915. https://doi.org/10.1109/ACCESS.2019.2936470.

Alamsyah, A., & Indraswari, A. A. (2019). Social network and sentiment analysis for social customer relationship management in Indonesia banking sector. Advanced Science Letters, 23(4), 3808–3812. https://doi.org/10.1166/ASL.2017.9279.

Andruloniw, P., Kowalik, K., & Zwierzykowski, P. (2019). Unsupervised Learning Data-Driven Continuous QoE Assessment in Adaptive Streaming-Based Television System. Applied Sciences (Switzerland, 12(16), 8288. https://doi.org/10.3390/APP12168288/S1.

Angelia, D. (2022). Platform Video Streaming Paling Digemari Masyarakat Indonesia 2022 - GoodStats. Goodstats.Id. https://doi.org/https://goodstats.id/article.

Barman, N., & Martini, M. G. (2019). QoE Modeling for HTTP Adaptive Video Streaming–A Survey and Open Challenges. IEEE Access, 7, 30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778.

Bishop, C. M. (2020). Pattern Recognition and Machine Learning. https://link.springer.com/book/9780387310732.

Bouraqia, K., Sabir, E., Sadik, M., & Ladid, L. (2020). Quality of Experience for Streaming Services: Measurements, Challenges and Insights. IEEE Access, 8, 13341–13361. https://doi.org/10.1109/ACCESS.2020.2965099.

Bratawisnu, M. K., & Alamsyah, A. (2020). Social Network Analysis Untuk Analisa Interaksi User Dimedia Sosial Mengenai Bisnis E-Commerce (Studi Kasus: Lazada, Tokopedia Dan Elevenia. Almana : Jurnal Manajemen Dan Bisnis, 2(2), 107–115. https://doi.org/10.36555/ALMANA.V2I2.143.

Chen, K. T., Wu, C. C., Chang, Y. C., & Lei, C. L. (2020). A crowdsourceable QoE evaluation framework for multimedia content. Proceedings of the 2009 ACM Multimedia Conference, with Co-Located Workshops and Symposiums, 491–500. https://doi.org/10.1145/1631272.1631339.

Cieplinska, N., Janowski, L., Moor, K., & Wierzchon, M. (2022). Long-Term Video QoE Assessment Studies: A Systematic Review. IEEE Access, 10, 133883–133897. https://doi.org/10.1109/ACCESS.2022.3231747.

Cisco Predicts | Fierce Video. (2020). Aplikasi Video on Demand Paling Favorit di Indonesia. https://databoks.katadata.co.id/datapublish/2022/07/29.

Fizza, K., Banerjee, A., Mitra, K., Jayaraman, P. P., Ranjan, R., Patel, P., & Georgakopoulos, D. (2021). QoE in IoT: a vision, survey and future directions. Discover Internet of Things, 1(1), 1–14. https://doi.org/10.1007/S43926-021-00006-7.

Gramaglia, M., Digon, I., Friderikos, V., Hugo, D., Mannweiler, C., Puente, M. A., Samdanis, K., & Sayadi, B. (2019). Flexible connectivity and QoE/QoS management for 5G Networks. The 5G NORMA View. 2016 IEEE International Conference on Communications Workshops, 373–379. https://doi.org/10.1109/ICCW.2016.7503816.

Habachi, O., Hu, Y., Schaar, M., Hayel, Y., & Wu, F. (2019). MOS-based congestion control for conversational services in wireless environments. IEEE Journal on Selected Areas in Communications, 30(7), 1225–1236. https://doi.org/10.1109/JSAC.2012.120808.

Hossfeld, T., Keimel, C., Hirth, M., Gardlo, B., Habigt, J., Diepold, K., & Tran-Gia, P. (2019). Best practices for qoe crowdtesting: Qoe assessment with crowdsourcing. IEEE Transactions on Multimedia, 16(2), 541–558. https://doi.org/10.1109/TMM.2013.2291663.

International telemucation. (2020). 100+ Social Media Statistics You Need To Know In 2023 [All Networks. https://statusbrew.com/insights/social-media-statistics.

Khalil, A., Mbarek, N., & Togni, O. (2019). IoT Service QoS Guarantee Using QBAIoT Wireless Access Method. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11005 LNCS (pp. 157–173). https://doi.org/10.1007/978-3-030-03101-5_15/COVER.

Kim, W., Ahn, S., Nguyen, A. D., Kim, J., Kim, J., Oh, H., & Lee, S. (2020). Modern trends on quality of experience assessment and future work. APSIPA Transactions on Signal and Information Processing, 8. https://doi.org/10.1017/ATSIP.2019.16.

Kim, W., Kim, J., & Lee, S. (2019). Quality of Experience using Deep Convolutional Neural Networks and future trends. APSIPA Transactions on Signal and Information Processing.

Kougioumtzidis, G., Poulkov, V., Zaharis, Z. D., & Lazaridis, P. I. (2022). A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction. IEEE Access, 10, 19507–19538. https://doi.org/10.1109/ACCESS.2022.3149592.

Live Video Usage Will Increase 15-Fold. (2021). Live video usage will increase 15-fold by 2021, Cisco predicts | Fierce Video. https://www.fiercevideo.com/cable/live-video-usage-will-increase-15-fold-by-2021-cisco-predicts.

Marhantara, Y. G., & Widodo, T. (2021). Pengaruh Social Media Marketing Twitter Dalam Upaya Meningkatkan Loyalitas Penggunaan Customer Streaming Vidio.com (Studi Pada Kota Bandung. EProceedings of Management, 8(5). https://openlibrarypublications.telkomuniversity.ac.id/index.php/management/article.

Mizoguchi, T., & Ito, Y. (2020). Effect of QoS degradation caused by 6to4 and IPsec on QoE for Web services. IEEE 3rd Global Conference on Consumer Electronics, GCCE, 5–9. https://doi.org/10.1109/GCCE.2014.7031121.

Netflix. (2020). Social Network Analysis for Foundations: Six Ideas to Scale Impact - Visible Network Labs. https://visiblenetworklabs.com/2023/01/06/social-network-analysis-for-foundations.

Permana, F. C., Wicaksono, Z. M., Kurniawan, C., Abdullah, A. S., & Ruchjana, B. N. (2021). Perception analysis of the Indonesian society on twitter social media on the increase in BPJS kesehatan contribution in the Covid 19 pandemic era. Journal of Physics: Conference Series, 1722(1), 12022. https://doi.org/10.1088/1742-6596/1722/1/012022.

Ribeiro, F., Florêncio, D., Zhang, C., & Seltzer, M. (2019). CROWDMOS: An approach for crowdsourcing mean opinion score studies. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2416–2419. https://doi.org/10.1109/ICASSP.2011.5946971.

Ríos, S. A., Aguilera, F., Nuñez-Gonzalez, J. D., & Graña, M. (2019). Semantically enhanced network analysis for influencer identification in online social networks. Neurocomputing, 326–327, 71–81. https://doi.org/10.1016/J.NEUCOM.2017.01.123.

Said, O. (2022). Design and performance evaluation of QoE/QoS-oriented scheme for reliable data transmission in Internet of Things environments. Computer Communications, 189, 158–174. https://doi.org/10.1016/J.COMCOM.2022.03.020.

Sapountzi, A., & Psannis, K. E. (2019). Social networking data analysis tools & challenges. Future Generation Computer Systems, 86, 893–913. https://doi.org/10.1016/J.FUTURE.2016.10.019.

Seufert, M., Wassermann, S., & Casas, P. (2019). Considering User Behavior in the Quality of Experience Cycle: Towards Proactive QoE-Aware Traffic Management. IEEE Communications Letters, 23(7), 1145–1148. https://doi.org/10.1109/LCOMM.2019.2914038.

Song, E., Pan, T., Fu, Q., Zhang, R., Jia, C., Cao, W., & Huang, T. (2020). Threshold-oblivious on-line web QoE assessment using neural network-based regression model. IET Communications, 14(12), 2018–2026. https://doi.org/10.1049/IET-COM.2019.1229.

Sugiyono. (2019). Metode Penelitian Kuantitatif (19th ed.). Alfabeta.

Toet, A., Mioch, T., Gunkel, S. N. B., Niamut, O., & Erp, J. B. F. (2021). Towards a multiscale QoE assessment of mediated social communication. Quality and User Experience, 7(1), 1–22. https://doi.org/10.1007/S41233-022-00051-2.

Yang, M., Wang, S., Calheiros, R. N., & Yang, F. (2020). Survey on QoE assessment approach for network service. IEEE Access, 6, 48374–48390. https://doi.org/10.1109/ACCESS.2018.2867253.

Diterbitkan

2024-04-04

Cara Mengutip

Fahmi Candra Permana, & Yoanes Bandung. (2024). Pemetaan Sentimen Pengguna Media Sosial dalam Evaluasi Quality of Experience Kinerja Layanan Video Streaming. JST (Jurnal Sains Dan Teknologi), 13(1), 135–146. https://doi.org/10.23887/jstundiksha.v13i1.61570

Terbitan

Bagian

Articles