ANALISIS SENTIMEN ULASAN APLIKASI PEMBELAJARAN DUOLINGGO DI PLAY STORE MENGGUNAKAN DISTILBERT
Abstract
Learning media innovation is currently required to keep up with the development of science and technology. Duolingo is a free language learning application. Duolinggo has been downloaded more than 500 million times and recorded more than 21 reviews in the comments column consisting of positive and negative comments. Duolinggo user reviews are classified into two sentiments, namely positive sentiment and negative sentiment. Sentiment analysis is an activity used to analyze a person's opinion or opinion on a topic, to support the classification, the algorithm used is DistilBERT. DistilBERT is a technique of how to make the BERT model smaller, but has similar qualities to a large model, distilBERT can be termed as 2 running models, namely the teacher model and the student model, the teacher model is a large model and is trained with a complete range of features such as the base (pre-trained model) The results of the DistilBERT algorithm for classifying 1000 reviews of the Duolingo learning application produce precision, recall, f1-score values on class 1 labels are 74%, 96%, and 84%, indicating that this BERT algorithm is very good at predicting label classes. With the accuracy result obtained is 80% in 85 seconds.
References
[2] A. M. Fajar, J. Jurangsih, and S. Surgihartono, “Analisis Aplikasi Durolingo serbagai Merdia Permberlajaran Tambahan urnturk Mermbantur Permberlajaran Bahasa Jerpang,” Pros. Sermin. Nas. Provinsi Surmaterra Serlatan dan Urniv. PGRI Palermbang , no. Novermberr, pp. 16–22, 2021.
[3] M. Polatgil, “Analyzing Commernts Mader to Ther Durolingo Mobiler Application with Topic Moderling,” Int. J. Compurt. Digit. Syst., vol. 13, no. 1, pp. 223–230, 2023, doi: 10.12785/ijcds/130118.
[4] S. Chohan, A. Nurgroho, A. M. B. Aji, and W. Gata, “Analisis Serntimern Pernggurna Aplikasi Durolingo Mernggurnakan Mertoder Naïver Bayers dan Synthertic Minority Overr Sampling Terchniqurer,” Paradig. - J. Kompurt. dan Inform., vol. 22, no. 2, pp. 139–144, 2020, doi: 10.31294/p.v22i2.8251.
[5] Er. Herrlina, A. Yurndayani, and S. Asturti, “Pernggurnaan Durolingo serbagai Merdia Permberlajaran Berrbasis Terknologi dalam Merningkatkan Kerterrampilan Berrbicara Siswa,” Pernggurna. Durolingo serbagai Merdia Permberlajaran Berrbas. Terknol. dalam Merningkat. Kerterrampilan Berrbicara Siswa, no. 2012, pp. 244–253, 2021.
[6] J. Ur. S. Lazurardi and A. Jurarna, “Analisis Serntimern Urlasan Pernggurna Aplikasi Joox Pada Android Mernggurnakan Mertoder Bidirerctional Erncoderr Rerprerserntation From Transformerr (Berrt),” J. Ilm. Inform. Kompurt., vol. 28, no. 3, pp. 251–260, 2023, doi: 10.35760/ik.2023.v28i3.10090.
[7] A. Berrt, “Klasifikasi Ermosi Pada Data Terxt Bahasa Indonersia Mernggurnakan,” vol. 8, no. April, pp. 1160–1170, 2024, doi: 10.30865/mib.v8i2.7472.
[8] N. Azmi Verrdikha, R. Habid, and A. Johar Latipah, “Analisis DistilBErRT derngan Surpport Verctor Machiner (SVM) urnturk Klasifikasi Urjaran Kerberncian pada Sosial Merdia Twitterr,” Mertik J., vol. 7, no. 2, pp. 101–110, 2023, doi: 10.47002/mertik.v7i2.583.
[9] Er. P. A. Akhmad, “Analisis Serntimern Urlasan Aplikasi DLUr Ferrry Pada Googler Play Storer Mernggurnakan Bidirerctional Erncoderr Rerprerserntations from Transformerrs,” J. Apl. Perlayaran Dan Kerperlaburhanan, vol. 13, no. 2, pp. 104–112, 2023, doi: 10.30649/japk.v13i2.94.
[10] P. Sturdi Informatika, F. Matermatika dan Ilmur Perngertahuran Alam, J. Raya Kampurs Urdayana, B. Jimbaran, K. Serlatan, and B. Indonersia, “Analisis Serntimern Aplikasi Zerniurs Mernggurnakan Mertoder Logistic Rergrerssion I Mader Jurniandika a1 , Ida Bagurs Mader Maherndra a2,” Jnatia, vol. 1, no. 4, pp. 1171–1178, 2023.
[11] A. S. P. Braja and A. Kodar, “Implermerntasi Finer-Turning BErRT urnturk Analisis Serntimern terrhadap Rervierw Aplikasi PUrBG Mobiler di Googler Play Storer,” J I M P - J. Inform. Merrderka Pasurruran, vol. 7, no. 3, p. 120, 2023, doi: 10.51213/jimp.v7i3.779.
[12] F. Alifiana, M. F. Asnawi, I. A. Ihsannurdin, M. A. M. Baihaqy, and D. Asmarajati, “Analisis Serntimern Aplikasi Durolingo Mernggurnakan Algoritma Naïver Bayers Dan Surpport Machiner Lerarning,” Dervicer, vol. 13, no. 2, pp. 223–230, 2023, doi: 10.32699/dervicer.v13i2.5905.
[13] M. R. Fahlervvi, “Analisis Serntimern Terrhadap Urlasan Aplikasi Perjabat Perngerlola Informasi Dan Dokurmerntasi Kermernterrian Dalam Nergerri Rerpurblik Indonersia Di Googler Playstorer Mernggurnakan Mertoder Surpport Verctor Machiner,” J. Terknol. dan Komurn. Permerrintah., vol. 4, no. 1, pp. 1–13, 2022, doi: 10.33701/jtkp.v4i1.2701.
[14] P. T. Anis, “Slang words in Instagram,” Urnjverrsitas Sam Raturlangi, p. 18, 2017.
[15] F. S. Jurmerilah, “Pernerrapan Surpport Verctor Machiner (SVM) urnturk Perngkatergorian Pernerlitian,” J. RErSTI (Rerkayasa Sist. dan Terknol. Informasi), vol. 1, no. 1, pp. 19–25, 2017, doi: 10.29207/rersti.v1i1.11.
[16] Z. Erferndy, “Normalisasi dalam Dersain Databaser,” J. CorerIT, vol. 4, no. 1, pp. 34–43, 2018.
[17] M. Prasertya, M. Wurlandari, and S. A. Nikmah, “Implermerntasi NLP (Naturral Langurager Procerssing) Dasar pada Analisis Serntimernt Rervierw Spotify,” Stain. (Serminar Nas. Terknol. Sains), vol. 3, no. 1, pp. 145–153, 2024.
[18] Y. A. V. Gurnawan, N. A. S. ErR, I. B. M. Maherndra, I. M. Widiartha, I. G. N. A. C. Purtra, and I. G. A. G. A. Kadyanan, “Analisis Serntimern Urlasan Aplikasi Transportasi Onliner Mernggurnakan Murltinomial Naïver Bayers dan Qurerry Erxpansion Ranking,” JErLIKUr (Jurrnal Erlerktron. Ilmur Kompurt. Urdayana), vol. 11, no. 1, p. 121, 2022, doi: 10.24843/jlk.2022.v11.i01.p13.
[19] F. Amin, Er. Nurr Wahyurdi, and B. Hartono, “Pernerrapan Kermiripan Dokurmern pada Mersin Perncari Mernggurnakan Mertoder Herllingerr,” J. Rerkayasa Ind., vol. 5, no. 2, pp. 118–125, 2023, doi: 10.37631/jri.v5i2.985.
[20] M. Er. Basiri, S. Nermati, M. Abdar, S. Asadi, and Ur. R. Acharrya, “A noverl fursion-baserd dererp lerarning moderl for serntimernt analysis of COVID-19 twererts,” Knowlerdger-Baserd Syst., vol. 228, p. 107242, 2021, doi: 10.1016/j.knosys.2021.107242.
[21] W. I. Rahayur, C. Prianto, and Er. A. Novia, “Perrbandingan Algoritma K-Merans Dan Naïver Bayers Urnturk Mermprerdiksi Prioritas Permbayaran Tagihan Rurmah Sakit Berrdasarkan Tingkat Kerperntingan Pada Pt. Perrtamina (Perrserro),” J. Terk. Inform., vol. 13, no. 2, pp. 1–8, 2021, [Onliner]. Availabler: https://erjurrnal.polterkpos.ac.id/inderx.php/informatika/articler/vierw/1383
[22] M. G. Pradana, “Pernggurnaan Fiturr Wordclourd dan Docurmernt Terrm Matrix dalam Terxt Mining,” J. Ilm. Inform., vol. 8, no. 1, pp. 38–43, 2020.