Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes

 Sania Wulandari (Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, —)
 (*)Firman Noor Hasan Mail (Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: February 29, 2024; Published: April 23, 2024

Abstract

Thrifting is an increasingly popular second-hand shopping activity in Indonesia, especially among millennials and generation Z as a cost-saving shopping alternative. Thrifting activities have a clear positive impact on the Indonesian people in protecting the environment by reducing the purchase of new goods. However, thrifting is considered illegal and can harm the domestic textile industry. So sentiment analysis needs to be done to find out how people respond to thrifting activities. This study aims to calculate the number of positive and negative comments from Twitter users, and find out how accurately the Naïve Bayes algorithm is used in the classification. The data used is taken from Twitter social media as many as 900 tweets, then processed through several advanced stages such as pre-processing which consists of cleansing, tokenize, and filter stopwords. Then at the labeling stage the data is divided into training data and test data with a ratio of 60:40. After being classified using the Naïve Bayes algorithm, the results obtained tend to be positive with a total of 368 positive comments and 181 negative sentiments. After going through the evaluation stage, the accuracy value is 95.92%, the precision value is 95.76%, and the recall value is 97.41%. The evaluation results show that the Naïve Bayes algorithm is proven to have a high level of accuracy used in classification.

Keywords


Thrifting; Sentiment Analysis; Naïve Bayes Algorithm; Twitter; Social Media

Full Text:

PDF


Article Metrics

Abstract view : 592 times
PDF - 270 times

References

S. Nadhila, M. Muzhirah, H. Sajali, and M. Andinata, “Eksistensi Diri Remaja Dalam Penggunaan Pakaian Bekas (Studi Kasus Pada Konsumen Thrifting Pajak Melati Medan),” Innov. J. Soc. Sci. Res., vol. 3, no. 3, pp. 2436–2446, 2023, doi: 10.31004/innovative.v3i3.2403.

M. Hayati and N. Susilawati, “Thrifting sebagai presentasi diri Mahasiswa di Pasar Putih Bukittinggi,” J. Perspekt., vol. 4, no. 3, p. 359, Sep. 2021, doi: 10.24036/perspektif.v4i3.460.

D. Rahmayanti, “Pengaruh Fashion Involvement , Promosi , Religiusitas terhadap Impulse Buying dengan Shopping Emotion sebagai Variabel Intervening pada Pakaian Thrift di Salatiga,” vol. 3, no. 1, pp. 40–51, 2023, doi: 10.19105/mabny.v3i01.8988.

N. L. Adji and D. Claretta, “Fenomena thrift shop dikalangan remaja: studi fenomenologi tentang thrift shop di kalangan remaja Surabaya,” Dawatuna J. Commun. ion Islam. Broadcast., vol. 3, no. 1, pp. 36–44, 2023, doi: 10.47476/dawatuna.v3i1.2201.

M. I. Petiwi, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Gofood Berdasarkan Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine,” J. Media Inform. Budidarma, vol. 6, no. 1, p. 542, 2022, doi: 10.30865/mib.v6i1.3530.

I. P. Rahayu, A. Fauzi, and J. Indra, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine,” J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 296, 2022, doi: 10.30865/json.v4i2.5381.

S. Juanita, “Analisis Sentimen Persepsi Masyarakat Terhadap Pemilu 2019 Pada Media Sosial Twitter Menggunakan Naive Bayes,” J. Media Inform. Budidarma, vol. 4, no. 3, p. 552, 2020, doi: 10.30865/mib.v4i3.2140.

M. Oktaviana, Z. A. Achmad, H. Arviani, and K. Kusnarto, “Budaya komunikasi virtual di Twitter dan Tiktok: Perluasan makna kata estetik,” Satwika Kaji. Ilmu Budaya dan Perubahan Sos., vol. 5, no. 2, pp. 173–186, 2021, doi: 10.22219/satwika.v5i2.17560.

Y. A. V. Gunawan, N. A. S. ER, I. B. M. Mahendra, I. M. Widiartha, I. G. N. A. C. Putra, and I. G. A. G. A. Kadyanan, “Analisis Sentimen Ulasan Aplikasi Transportasi Online Menggunakan Multinomial Naïve Bayes dan Query Expansion Ranking,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 11, no. 1, p. 121, 2022, doi: 10.24843/jlk.2022.v11.i01.p13.

E. Fitri, “Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine,” J. Transform., vol. 18, no. 1, p. 71, 2020, doi: 10.26623/transformatika.v18i1.2317.

D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier,” J. Inform. dan Tek. Elektro Terap., vol. 10, no. 1, pp. 34–40, 2022, doi: 10.23960/jitet.v10i1.2262.

Y. A. Singgalen, “Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier,” Build. Informatics, Technol. Sci., vol. 4, no. 3, 2022, doi: 10.47065/bits.v4i3.2486.

F. Sidik, I. Suhada, A. H. Anwar, and F. N. Hasan, “Analisis Sentimen Terhadap Pembelajaran Daring Dengan Algoritma Naive Bayes Classifier,” J. Linguist. Komputasional, vol. 5, no. 1, p. 34, 2022, doi: 10.26418/jlk.v5i1.79.

B. Gunawan, H. S. Pratiwi, and E. E. Pratama, “Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 4, no. 2, p. 113, 2018, doi: 10.26418/jp.v4i2.27526.

Alfandi Safira and F. N. Hasan, “Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier,” Zo. J. Sist. Inf., vol. 5, no. 1, pp. 59–70, 2023, doi: 10.31849/zn.v5i1.12856.

S. Lestari and S. Saepudin, “Analisis Sentimen Vaksin Sinovac Pada Twitter Menggunakan Algoritma Naive Bayes,” SISMATIK (Seminar Nas. Sist. Inf. dan Manaj. Inform., pp. 163–170, 2021.

S. M. Salsabila, A. Alim Murtopo, and N. Fadhilah, “Analisis Sentimen Pelanggan Tokopedia Menggunakan Metode Naïve Bayes Classifier,” J. Minfo Polgan, vol. 11, no. 2, pp. 30–35, 2022, doi: 10.33395/jmp.v11i2.11640.

Vynska Amalia Permadi, “Analisis Sentimen Menggunakan Algoritma Naive Bayes Terhadap Review Restoran di Singapura,” J. Buana Inform., vol. 11, pp. 141–151, 2020, doi: 10.24002/jbi.v11i2.3769.

S. N. J. Fitriyyah, N. Safriadi, and E. E. Pratama, “Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 5, no. 3, p. 279, 2019, doi: 10.26418/jp.v5i3.34368.

A. Rozaqi, A. Triayudi, and R. T. Aldisa, “Analisis Sentimen Vaksinasi Booster Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes dan K-NN,” J. Sist. Komput. dan Inform., vol. 4, no. 1, p. 184, 2022, doi: 10.30865/json.v4i1.4907.

A. Wibowo, Firman Noor Hasan, Rika Nurhayati, and Arief Wibowo, “Analisis Sentimen Opini Masyarakat Terhadap Keefektifan Pembelajaran Daring Selama Pandemi COVID-19 Menggunakan Naïve Bayes Classifier,” J. Asiimetrik J. Ilm. Rekayasa Inov., vol. 4, pp. 239–248, 2022, doi: 10.35814/asiimetrik.v4i1.3577.

A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.

A. Muzaki and A. Witanti, “Sentiment Analysis of the Community in the Twitter To the 2020 Election in Pandemic Covid-19 By Method Naive Bayes Classifier,” J. Tek. Inform., vol. 2, no. 2, pp. 101–107, 2021, doi: 10.20884/1.jutif.2021.2.2.51.

N. M. A. J. Astari, Dewa Gede Hendra Divayana, and Gede Indrawan, “Analisis Sentimen Dokumen Twitter Mengenai Dampak Virus Corona Menggunakan Metode Naive Bayes Classifier,” J. Sist. dan Inform., vol. 15, no. 1, pp. 27–29, 2020, doi: 10.30864/jsi.v15i1.332.

U. Gunadarma, U. Gunadarma, K. Positif, K. Negatif, and N. Bayes, “Analisis Sentimen Terhadap Universitas Gunadarma Berdasarkan Opini Pengguna Twitter Menggunakan Metode Naive Bayes Classifier,” J. Ilm. Komputasi, vol. 19, no. 4, pp. 507–521, 2020, doi: 10.32409/jikstik.19.4.354.

A. Saputra and F. Noor Hasan, “Analisis Sentimen Terhadap Aplikasi Coffee Meets Bagel Dengan Algoritma Naïve Bayes Classifier,” SIBATIK J. J. Ilm. Bid. Sos. Ekon. Budaya, Teknol. dan Pendidik., vol. 2, no. 2, pp. 465–474, 2023, doi: 10.54443/sibatik.v2i2.579.

A. Kusuma and A. Nugroho, “Analisa Sentimen Pada Twitter Terhadap Kenaikan Tarif Dasar Listrik Dengan Metode Naïve Bayes,” J. Ilm. Teknol. Inf. Asia, vol. 15, no. 2, p. 137, 2021, doi: 10.32815/jitika.v15i2.557.

Y. I. Kurniawan and T. I. Barokah, “Klasifikasi Penentuan Pengajuan Kartu Kredit Menggunakan K-Nearest Neighbor,” J. Ilm. Matrik, vol. 22, no. 1, pp. 73–82, 2020, doi: 10.33557/jurnalmatrik.v22i1.843.

M. Furqan, S. Sriani, and S. M. Sari, “Analisis Sentimen Menggunakan K-Nearest Neighbor Terhadap New Normal Masa Covid-19 Di Indonesia,” Techno.Com, vol. 21, no. 1, pp. 51–60, 2022, doi: 10.33633/tc.v21i1.5446.

N. Herlinawati, Y. Yuliani, S. Faizah, W. Gata, and S. Samudi, “Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, p. 293, 2020, doi: 10.24114/cess.v5i2.18186.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 JURNAL MEDIA INFORMATIKA BUDIDARMA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.



JURNAL MEDIA INFORMATIKA BUDIDARMA
STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998
Email: mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.