Emotion Recognition Tentang Ditutupnya TikTok Shop Menggunakan Metode Naive Bayes
DOI:
https://doi.org/10.62951/bridge.v2i2.53Keywords:
Naive Bayes, Emotion Recognition, the closure of TikTokshop, TF-IDFAbstract
The policy of shutting down TikTok Shop has sparked both pros and cons. On one side, it eliminates jobs for content creators whose income relies on TikTok Shop, while on the other side, it saves UMKM from predatory pricing wars that harm them. Utilizing the Naive Bayes algorithm, a classification method capable of predicting the likelihood of a class and making decisions based on learning data, the Emotion Recognition research on YouTube comments related to the closure of TikTok Shop is conducted. Data will be classified into five classes: happy, angry, sad, afraid, and surprised. The objective of this research is to find the best emotional model using the Naive Bayes method. The results of user testing with Naive Bayes and Tf-Idf show that the precision values for sad, happy, afraid, and surprised emotions are high, while for anger, the percentage is 59%. The percentages for afraid, happy, sad, and surprised emotions are 91%, 87%, 84%, and 79%, respectively. The overall accuracy is 82%.
Downloads
References
B Uarbat, B. E., and R. Cretulscu. 2003. “AISB 2008 Convention Communication , Interaction and Social Intelligence.” Technology 25–35.
Bestari, Novina Putri. 2023. “TikTok Monopoli, Dilarang Sekaligus Medsos-Ecommerce.” CNBC INDONESIA.
Candra Ardiansyah, Indriati, Marji. 2020. “Klasifikasi Emosi Pada Komentar Youtube Menggunakan Metode Modified K-Nearest Neighbor Dengan BM25 Dan Seleksi Fitur Chi-Square.” Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer 4(4):1027–32.
Dhiya Abirawa, Irfan, Andrew Brian Osmond, and Casi Setianingsih. 2018. “Klasifikasi Emosi Pada Lirik Lagu Menggunakan Metode Support Vector Machine Emotion Classification in a Song Lyrics Using Support Vector Machine Method 1.” 5(3):6203–9.
Ekman, Paul, and Daniel Cordaro. 2011. “What Is Meant by Calling Emotions Basic.” Emotion Review 3(4):364–70. doi: 10.1177/1754073911410740.
Fadli, Nur. 2023. “Klasifikasi Emosi Kompleks Yang Negatif Pada Anak Menggunakan Metode Naïve Bayes Classification of Negative Complex Emotions in Children Using Naïve Bayes Method.” 10(1):460–66.
Fanesya, Fera, Randy Cahya Wihandika, and Indriati. 2019. “Deteksi Emosi Pada Twitter Menggunakan Metode Naive Bayes Dan Kombinasi Fitur.” Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer 3(7):3.
Handayani, Riska Dwi, Kusrini Kusrini, and Hanif Al Fatta. 2020. “Perbandingan Fitur Ekstraksi Untuk Klasifikasi Emosi Pada Sosial Media.” Jurnal Ilmiah SINUS 18(2):21. doi: 10.30646/sinus.v18i2.457.
KEMP, SIMON. 2023. “DIGITAL 2023: INDONESIA.” Datareportal.
Komorowski, Matthieu, Dominic C. Marshall, Justin D. Salciccioli, and Yves Crutain. 2016. “Secondary Analysis of Electronic Health Records.” Secondary Analysis of Electronic Health Records (September):1–427. doi: 10.1007/978-3-319-43742-2.
Nisa Sofia Amriza, Rona, and Didi Supriyadi. 2021. “Komparasi Metode Machine Learning Dan Deep Learning Untuk Deteksi Emosi Pada Text Di Sosial Media.” Jurnal JUPITER 13(2):130–39.
Nurfauzan, Azward, and Warih Maharani. 2021. “Klasifikasi Emosi Pada Pengguna Twitter Menggunakan Metode Klasifikasi Decision Tree.” Universitas Telkom.
Puspitarini, Dinda Sekar, and Reni Nuraeni. 2019a. “Pemanfaatan Media Sosial Sebagai Media Promosi.” Jurnal Common 3(1):71–80. doi: 10.34010/common.v3i1.1950.
Puspitarini, Dinda Sekar, and Reni Nuraeni. 2019b. PEMANFAATAN MEDIA SOSIAL SEBAGAI MEDIA PROMOSI (Studi Deskriptif Pada Happy Go Lucky House). Vol. 3.
Riani, Adella Putri, Nina Sulistyowati, Taufik Ridwan, and Apriade Voutama. 2023. “Klasifikasi Emosi Publik Terhadap Larangan Penggunaan Obat Sirup Menggunakan Algoritma Naive Bayes.” METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi 7(2):325–39. doi: 10.46880/jmika.vol7no2.pp325-339.
Sudianto, Sudianto. 2022a. “Analisis Kinerja Algoritma Machine Learning Untuk Klasifikasi Emosi.” Building of Informatics, Technology and Science (BITS) 4(2):1027–34. doi: 10.47065/bits.v4i2.2261.
Sudianto, Sudianto. 2022b. “Analisis Kinerja Algoritma Machine Learning Untuk Klasifikasi Emosi.” Building of Informatics, Technology and Science (BITS) 4(2):1027–34. doi: 10.47065/bits.v4i2.2261.
Yanwardhana, Emir. 2023. “Menkominfo: Social Commerce Tidak Diatur, Bisa Monopoli.” CNBC INDONESIA.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Bridge : Jurnal publikasi Sistem Informasi dan Telekomunikasi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
