Metode dan Algoritma Dalam Sentimen Analisis: Systematic Literature Review

Erba Lutfina; Wiwin Andriana; Sanina Quamila Putri Wiratmaja; Ervina Febrianti

doi:10.53416/stmj.v4i2.274

Authors

Erba Lutfina Universitas Nasional Karangturi
Wiwin Andriana Universitas Dian Nuswantoro
Sanina Quamila Putri Wiratmaja Universitas Dian Nuswantoro
Ervina Febrianti Universitas Dian Nuswantoro

DOI:

https://doi.org/10.53416/stmj.v4i2.274

Keywords:

Analisis data, analisis sentiment, sentiment analysis, Systematic Review, Metode SLR

Abstract

This study highlights the advantages of sentiment analysis using algorithms in understanding public opinion, especially in the context of the increasing complexity of digital content. To investigate and present the latest developments in sentiment analysis, this study uses the Systematic Literature Review (SLR) method to identify and evaluate previously developed sentiment analysis methods. The research steps involve identifying significant sentiment analysis methods, assessing advantages and disadvantages, and critically reviewing recent advances. By applying algorithms in this process, it is expected to be able to analyze and describe the development of sentiment analysis research comprehensively. Through the application of the SLR method, this study is expected to provide in-depth insights into trends, challenges, and opportunities for future research in sentiment analysis, create a better understanding of effective sentiment analysis methods, and detail the expected results that can be expected in the development of sentiment analysis.

Author Biography

Erba Lutfina, Universitas Nasional Karangturi

References

Ainul Wildan, R. S., Adam Rajagede, R., & Rahmadi, R. (2021). Analisis Sentimen Politik Berdasarkan Big Data dari Media Sosial Youtube: Sebuah Tinjauan Literatur. Prosiding Automata, 2(1).

Aliandu, P. (2015). Sentiment Analysis to Determine Accommodation, Shopping and Culinary Location on Foursquare in Kupang City. Procedia Computer Science, 72, 300–305. https://doi.org/10.1016/j.procs.2015.12.144

Andriana, H., Shofia Hilabi, S., & Hananto, A. (2023). Penerapan Metode K-Nearest Neighbor pada Sentimen Analisis Pengguna Twitter terhadap KTT G20 di Indonesia. JURIKOM (Jurnal Riset Komputer), 10(1), 60–67. https://doi.org/10.30865/jurikom.v10i1.5427

Apriliani, D., Susanto, A., Hidayattullah, M. F., & Sasmito, G. W. (2023). Sentimen Analisis Pandangan Masyarakat Terhadap Vaksinasi Covid 19 Menggunakan K-Nearest Neighbors. Jurnal Informatika: Jurnal Pengembangan IT, 8(1), 34–37. https://doi.org/10.30591/jpit.v8i1.4759

Ash, S., & Surya, A. (2022). Analisis Sentimen Masyarakat Terhadap Kebijakan Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Metode Logistic Regression. Jurnal CoSciTech (Computer Science and Information Technology), 99–106.

Athira Luqyana, W., Cholissodin, I., & Perdana, R. S. (2018). Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(11), 4704–4713. http://j-ptiik.ub.ac.id

Cholifah, C., Hanny Hikmayanti Handayani, & Ayu Ratna Juwita. (2023). Analisis Sentimen Twitter Terhadap Uu Omnibus Law Menggunakan Metode Support Vector Machine (Svm) Dan Naïve Bayes Classifier (Nbc). Jurnal Informatika Teknologi Dan Sains, 4(4), 483–488. https://doi.org/10.51401/jinteks.v4i4.2191

Deviyanto, A., & Wahyudi, M. D. R. (2018). Penerapan Analisis Sentimen Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor. JISKA (Jurnal Informatika Sunan Kalijaga), 3(1), 1. https://doi.org/10.14421/jiska.2018.31-01

Dussoye, H., & Cadersaib, Z. (2018). Sentiment analytics framework integrating Twitter and Odoo ERP. 2017 International Conference on Infocom Technologies and Unmanned Systems: Trends and Future Directions, ICTUS 2017, 2018-Janua, 145–151. https://doi.org/10.1109/ICTUS.2017.8285994

Faradhillah, N. Y. A., Kusumawardani, R. P., & Hafidz, I. (2016). Eksperimen Sistem Klasifikasi Analisa Sentimen Twitter pada Akun Resmi Pemerintah Kota Surabaya Berbasis Pembelajaran Mesin (Experiments on Sentiment Classification System for Tweets of the Official Account of the City Government of Surabaya based on Mach. Prosiding Seminar Nasional Sistem Informasi Indonesia 2016, 15–24. http://is.its.ac.id/pubs/oajis/index.php/home/detail/1645/EKSPERIMEN-SISTEM-KLASIFIKASI-ANALISA-SENTIMEN-TWITTER-PADA-AKUN-RESMI-PEMERINTAH-KOTA-SURABAYA-BERBASIS-PEMBELAJARAN-MESIN

Febriyani, E., & Februariyanti, H. (2023). Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Algoritma Naive Bayes Classifier Di Twitter. Jurnal Tekno Kompak, 17(1), 25. https://doi.org/10.33365/jtk.v17i1.2061

Fitri, V. A., Andreswari, R., & Hasibuan, M. A. (2019). Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm. Procedia Computer Science, 161, 765–772. https://doi.org/10.1016/j.procs.2019.11.181

Hartmann, J., Heitmann, M., Siebert, C., & Schamp, C. (2023). More than a Feeling: Accuracy and Application of Sentiment Analysis. International Journal of Research in Marketing, 40(1), 75–87. https://doi.org/10.1016/j.ijresmar.2022.05.005

Hasanah, S., Purwasih, I., & Santoso, I. (2023). Analisis Sentimen Terhadap Masyarakat Adanya Uang Kertas Baru Menggunakan Algoritma K-Nearest Neighbor(Knn). IKRA-ITH Informatika …, 7(2), 105–114. http://journals.upi-yai.ac.id/index.php/ikraith-informatika/article/view/2813%0Ahttps://journals.upi-yai.ac.id/index.php/ikraith-informatika/article/download/2813/2065

Idris, I. S. K., Mustofa, Y. A., & Salihi, I. A. (2023). Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM). Jambura Journal of Electrical and Electronics Engineering, 5(1), 32–35. https://doi.org/10.37905/jjeee.v5i1.16830

Ihsan, M., Benny Sukma Negara, & Surya Agustian. (2022). LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 13(1), 79–89. https://doi.org/10.31849/digitalzone.v13i1.9950

Karim, A. (2020). Analisis Sentimen Pada Komentar Sosial Media Instagram Layanan Kesehatan BPJS Menggunkanan Naive Bayes Classifier. Repository Unisula, 5(3), 248–253.

Khairudin, M., Sukendar, A., & Somantri, A. (2023). Analisis Sentimen Film Di Twitter Menggunakan Metode Support Vector Machine. Jurnal Sains Dan Sistem Teknologi Informasi, 5(1), 97–102. https://doi.org/10.59811/sandi.v5i1.47

Kurniawan, I., Lia Hananto, A., Shofia Hilabi, S., Hananto, A., Priyatna, B., & Yuniar Rahman, A. (2023). Perbandingan Algoritma Naive Bayes Dan SVM Dalam Sentimen Analisis Marketplace Pada Twitter. Jurnal Teknik Informatika Dan Sistem Informasi, 10(1), 731–740. http://jurnal.mdp.ac.id

Lutfina, E., Nugroho, A., Abdillah, M. Z., Informasi, S., Sains, F., Karangturi, U. N., & Patah, J. R. (2022). SYSTEMATIC LITERATURE REVIEW UNTUK IDENTIFIKASI METODE FRAGMENTASI PADA PERANCANGAN SISTEM DATABASE TERDISTRIBUSI. Science, Technology and Management Journal Http://Journal.Unkartur.Ac.Id/Index.Php/Stmj SYSTEMATIC, 2(2), 56–61.

Lutfina, E., Setiawan, R. O. C., Nugroho, A., & Abdillah, M. Z. (2023). PERANCANGAN APLIKASI PEMBELAJARAN DENGAN KONSEP GAMIFIKASI Systematic Literature Review. METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi, 7(1), 78–87. https://doi.org/10.46880/jmika.vol7no1.pp78-87

Muslimin, M., & Lusiana, V. (2023). Analisis Sentimen Terhadap Kenaikan Harga Bahan Pokok Menggunakan Metode Naive Bayes Classifier. Jurnal Media Informatika Budidarma, 7(3), 1200–1209. https://doi.org/10.30865/mib.v7i3.6418

Pramesti, L. A., & Pratiwi, N. (2023). Analisis Sentimen Twitter Terhadap Program MBKM Menggunakan Decision Tree dan Support Vector Machine. Journal of Information System Research (JOSH), 4(4), 1145–1154. https://doi.org/10.47065/josh.v4i4.3807

Riany, J., Fajar, M., & Lukman, M. P. (2016). Penerapan Deep Sentiment Analysis pada Angket Penilaian Terbuka Menggunakan K-Nearest Neighbor. Sisfo, 06(01), 147–156. https://doi.org/10.24089/j.sisfo.2016.09.011

Rifaldi, M. I., Ramadhan, Y. R., & Jaelani, I. (2023). Analisis Sentimen Terhadap Aplikasi Chatgpt Pada Twitter Menggunakan Algoritma Naïve Bayes. Jurnal Sains Komputer & Informatika (J-SAKTI, 7(2), 802–814. http://tunasbangsa.ac.id/ejurnal/index.php/jsakti/article/viewFile/687/662

Rohman, M. A., Suhartono, & Chamidy, T. (2023). Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile Bidirectional GRU with Attention Mechanism on Sentiment Analysis of PLN Mobile. Techno.Com, 22(2), 358–372.

Sanjaya, F., Lutfina, E., Nugroho, A., & Abdilah, M. (2023). Systematic Literature Review Untuk Perancangan Sistem Informasi Stok Opname Gudang Berbasis Web. Science, Technology and Management Journal, 3(1), 182–192. http://journal.unkartur.ac.id/index.php/stmj

Saputra, I., AJI PAMBUDI, R. S., DARONO, H. E., AMSURY, F., FAHDIA, M. R., RAMADHAN, B., & ARDIANSYAH, A. (2021). Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning. Faktor Exacta, 13(4), 200. https://doi.org/10.30998/faktorexacta.v13i4.7074

Saraswati, M., & Riminarsih, D. (2020). Analisis Sentimen Terhadap Pelayanan Krl Commuterline Berdasarkan Data Twitter Menggunakan Algortima Bernoulli Naive Bayes. Jurnal Ilmiah Informatika Komputer, 25(3), 225–238. https://doi.org/10.35760/ik.2020.v25i3.3256

Singgalen, Y. A. (2023a). Analisis Sentimen dan Sistem Pendukung Keputusan Menginap di Hotel Menggunakan Metode CRISP-DM dan SAW. Journal of Information System Research (JOSH), 4(4), 1343–1353. https://doi.org/10.47065/josh.v4i4.3917

Singgalen, Y. A. (2023b). Analisis Sentimen Pengunjung Pulau Komodo dan Pulau Rinca di Website Tripadvisor Berbasis CRISP-DM. Journal of Information System Research (JOSH), 4(2), 614–625. https://doi.org/10.47065/josh.v4i2.2999

Supriyanto, J., Alita, D., & Isnain, A. R. (2023). Penerapan Algoritma K-Nearest Neighbor (K-NN) Untuk Analisis Sentimen Publik Terhadap Pembelajaran Daring. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 4(1), 74–80. https://doi.org/10.33365/jatika.v4i1.2468

Wahyuni, L., Triandi, B., Zarlis, M., & Nasution, Z. (2023). Pendekatan Filsafat Ilmu Terhadap Perkembangan Deep Learning dalam Perspektif Aksiologi. Computer Science Research and Its Development Journal, 15(1), 62–72. http://csridjournal.potensi-utama.ac.id/index.php/CSRIDjournal/article/view/39%0Ahttps://csridjournal.potensi-utama.ac.id/index.php/CSRIDjournal/article/download/39/16

Yohana, P., Agustian, S., & Gusti, S. K. (2022). Klasifikasi Sentimen Masyarakat terhadap Kebijakan Vaksin Covid-19 pada Twitter dengan Imbalance Classes Menggunakan Naive Bayes. Seminar Nasional Teknologi …, 69–80. http://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/view/19012%0Ahttp://ejournal.uin-suska.ac.id/index.php/SNTIKI/article/viewFile/19012/8336