Analisis Sentimen Tentang Penggunaan Galon Bebas BPA di Indonesia Menggunakan Algoritma Support Vector Machine

 Muhammad Iqbal Tri Atmojo (Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia)
 (*)Estu Sinduningrum Mail (Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: December 8, 2023; Published: December 29, 2023

Abstract

This research employs the Support Vector Machine algorithm to classify sentiment in comments on the X, Youtube, and Tiktok platforms regarding the use of BPA-free water gallons in Indonesia. From a total of 1200 data points, post-labeling, 772 data points were obtained, with 552 classified as positive and 220 as negative. The experimental results reveal that SVM achieves an accuracy of 96.15%, while Naïve Bayes achieves an accuracy of 84.55%. These findings indicate that SVM is effective in classifying sentiment with a high accuracy rate, providing valuable insights for manufacturers, government entities, and consumers regarding the use of BPA in water gallons in Indonesia. This study contributes to a better understanding of the role of social media in shaping public opinion and policies related to environmental and health issues.

Keywords


Support Vector Machine; BPA; Naïve Bayes; Sentiment Analysis; Crossvalidation Data

Full Text:

PDF


Article Metrics

Abstract view : 515 times
PDF - 182 times

References

R. Budi Syahputra Siregar, L. Rohani, and R. Devianty, “Analisis Penggunaan Media Sosial Instagram Terhadap Komunikasi Pembangunan Di Kota Medan,” SIBATIK J. J. Ilm. Bid. Sos. Ekon. Budaya, Teknol. dan Pendidik., vol. 2, no. 3, pp. 1047–1054, 2023, doi: 10.54443/sibatik.v2i3.720.

E. H. Sciences and NIH, “National Toxicology Program, US Department of Health and Human Services,” Natl. Toxicol. Progr., no. 08, pp. 11–13, 2015, [Online]. Available: http://ntp.niehs.nih.gov/about/%0Ahttps://ntp.niehs.nih.gov/ntp/ohat/bisphenol/bisphenol.pdf

R. T. Zoeller et al., “European Medicines Agency Conflicts With the European Food Safety Authority (EFSA) on Bisphenol A Regulation,” J. Endocr. Soc., vol. 7, no. 9, pp. 1–4, 2023, doi: 10.1210/jendso/bvad107.

A. P. Ayudhitama and U. Pujianto, “Analisa 4 Algoritma Dalam Klasifikasi Liver Menggunakan Rapidminer,” J. Inform. Polinema, vol. 6, no. 2, pp. 1–9, 2020, doi: 10.33795/jip.v6i2.274.

J. A. Saputra and S. A. Aklani, “Analisis Komparasi Algoritma K-Nearest Neighbor Dan Support Vector Machine Dengan Pendekatan Multi Dataset,” J. Ilm. Betrik, pp. 415–421, 2022, [Online]. Available: https://ejournal.pppmitpa.or.id/index.php/betrik/article/view/50%0Ahttps://ejournal.pppmitpa.or.id/index.php/betrik/article/download/50/32

F. Syah, H. Fajrin, A. N. Afif, M. R. Saeputra, D. Mirranty, and D. D. Saputra, “Analisa Sentimen Terhadap Twitter IndihomeCare Menggunakan Perbandingan Algoritma Smote, Support Vector Machine, AdaBoost dan Particle Swarm Optimization,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 7, no. 1, pp. 53–58, 2023, doi: 10.35870/jtik.v7i1.686.

R. Fahlapi et al., “Analisa Sentimen Vaksinasi Covid-19 Dengan Metode Support Vector Machine Dan Naïve Bayes Berbasis Teknik Smote,” J. Inform. Kaputama, vol. 6, no. 1, pp. 57–63, 2022, doi: 10.59697/jik.v6i1.136.

M. Rafly, A. Fattah, and M. Kamayani, “Analisis Sentimen Ulasan Pelanggan Online Ubi Madu Cilembu Abah Nana Menggunakan Algoritma Naïve Bayes,” J. Sist. Komput. dan Inform., vol. 5, no. September, pp. 11–21, 2023, doi: 10.30865/json.v5i1.6646.

M. Dogucu and M. Çetinkaya-Rundel, “Web Scraping in the Statistics and Data Science Curriculum: Challenges and Opportunities,” J. Stat. Educ., vol. 0, no. 0, pp. 1–24, 2020, doi: 10.1080/10691898.2020.1787116.

D. T. Hermanto, A. Setyanto, and E. T. Luthfi, “Algoritma LSTM-CNN untuk Binary Klasifikasi dengan Word2vec pada Media Online,” Creat. Inf. Technol. J., vol. 8, no. 1, p. 64, 2021, doi: 10.24076/citec.2021v8i1.264.

P. Dengan, M. Saw, M. I. Thohir, S. I. Mulyana, and F. Sembiring, “Analisis Sentimen Aplikasi Dompet Digital Pada Google,” vol. 5, no. 2, pp. 202–213, 2023.

Jimmy, E. H. Hermaliani, and L. Kurniawati, “Analisis Klasifikasi Sentimen Pengguna Media Sosial Twitter Terhadap Penundaan Pemilu Presiden Tahun 2024,” J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 2, pp. 570–579, 2023, doi: 10.35870/jimik.v4i2.243.

D. Musfiroh, U. Khaira, P. E. P. Utomo, and T. Suratno, “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 24–33, 2021, doi: 10.57152/malcom.v1i1.20.

A. Andreyestha and Q. N. Azizah, “Analisa Sentimen Kicauan Twitter Tokopedia Dengan Optimalisasi Data Tidak Seimbang Menggunakan Algoritma SMOTE,” Infotek J. Inform. dan Teknol., vol. 5, no. 1, pp. 108–116, 2022, doi: 10.29408/jit.v5i1.4581.

M. I. Amal, E. S. Rahmasita, E. Suryaputra, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Isu Kebocoran Data Kartu Identitas Ponsel di Twitter,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 3, pp. 645–660, 2022, doi: 10.28932/jutisi.v8i3.5483.

R. S. Amardita, A. Adiwijaya, and M. D. Purbolaksono, “Analisis Sentimen terhadap Ulasan Paris Van Java Resort Lifestyle Place di Kota Bandung Menggunakan Algoritma KNN,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 1, p. 62, 2022, doi: 10.30865/jurikom.v9i1.3793.

N. Agustina, D. H. Citra, W. Purnama, C. Nisa, and A. R. Kurnia, “Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Ulasan Shopee pada Google Play Store,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 2, no. 1, pp. 47–54, 2022, doi: 10.57152/malcom.v2i1.195.

H. C. Husada and A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Teknika, vol. 10, no. 1, pp. 18–26, 2021, doi: 10.34148/teknika.v10i1.311.

M. Kantardzic, DATA MINING Conepts, Models, Methods, and Algorithms, 3rd ed. 2020.

M. Fansyuri, “Analisa algoritma klasifikasi k-nearest neighbor dalam menentukan nilai akurasi terhadap kepuasan pelanggan (study kasus pt. Trigatra komunikatama),” Humanika J. Ilmu Sos. Pendidikan, dan Hum., vol. 3, no. 1, pp. 29–33, 2020.

I. H.Witten, E. Frank, M. A.Hall, and C. J.Pal, Data Mining Practical Machine Learning Tools and Techniques., 4th ed. 2016.

N. L. P. M. Putu, Ahmad Zuli Amrullah, and Ismarmiaty, “Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 123–131, 2021, doi: 10.29207/resti.v5i1.2587.

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.

I. F. PUTRA, “Preprocessing the Indonesian Hate & Abusive Text,” Kaggle, 2019. kaggle.com/code/ilhamfp31/preprocessing-the-indonesian-hate-abusive-text

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Tentang Penggunaan Galon Bebas BPA di Indonesia Menggunakan Algoritma Support Vector Machine

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Muhammad Iqbal Tri Atmojo, Estu Sinduningrum

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

Jurnal Sistem Komputer dan Informatika (JSON)
Dikelola oleh STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : jurnal.json@gmail.com


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