Sentimen Analisis Mengenai Polusi Udara Menggunakan Algoritma Support Vector Machine dan Random Forest
DOI:
https://doi.org/10.22441/fifo.2023.v15i2.001Keywords:
random forest, svm, twitter, sentiment analyst, pollutionAbstract
Air pollution is the contamination of indoor or outdoor environments with chemical, physical, or biological substances that change the natural properties of the atmosphere. Domestic incinerators, cars, motorbikes, combustion products from factory processing, waste burning, and forest fires are common sources of air pollution. In Indonesia, there is no doubt that air pollution occurs because of the many forest fires in Indonesia. As a result of this case, many people's opinions differ. Various sentiments occur in cyberspace, one of which is Twitter. Twitter is the social media that accommodates the most various kinds of positive, negative and neutral opinions. Therefore, researchers want to solve the problem by implementing the SVM and Random Forest algorithms. The dataset was obtained from scrapping results using tweet harvest. The data obtained was 5545 tweets. By dividing the dataset model by 80% and 20%, the results showed that the accuracy of the SVM algorithm was better than the Random Forest algorithm. The accuracy of the SVM algorithm is 83% while the Random Forest algorithm is 81%.Downloads
References
BBC Indonesia, “Indonesia masuk ‘enam negara paling berkontribusi terhadap polusi udara global’, warga akan gugat pemerintah dan industri,” https://www.bbc.com/indonesia/articles/c72enp76622o.
CNBC Indonesia, “Bukan PLTU, Ternyata Ini Penyebab Utama Polusi di Jakarta,” https://www.cnbcindonesia.com/news/20230903164954-4-468635/bukan-pltu-ternyata-ini-penyebab-utama-polusi-di-jakarta#:~:text=Siti%20menjelaskan%20penyebab%20pencemaran%20kualitas,juta%20di%20antaranya%20sepeda%20motor.
CNBC Indonesia, “Polusi Makin Parah! Rakyat Menderita, Solusi Malah Amburadul,” https://www.cnbcindonesia.com/research/20230828123040-128-466679/polusi-makin-parah-rakyat-menderita-solusi-malah-amburadul.
N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” vol. 6, no. 3, 2021, [Online]. Available: http://situs.com
A. P. Giovani, A. Ardiansyah, T. Haryanti, L. Kurniawati, and W. Gata, “ANALISIS SENTIMEN APLIKASI RUANG GURU DI TWITTER MENGGUNAKAN ALGORITMA KLASIFIKASI,” Jurnal Teknoinfo, vol. 14, no. 2, p. 115, Jul. 2020, doi: 10.33365/jti.v14i2.679.
G. A. Buntoro, “Analisis Sentimen Calon Gubernur DKI Jakarta 2017 Di Twitter,” 2017. [Online]. Available: https://t.co/jrvaMsgBdH
A. Fathan Hidayatullah, A. Sn, J. Teknik, I. Fakultas, and T. Industri, “ISSN: 1979-2328 UPN "Veteran,” 2014. [Online]. Available: http://www.situs.com
M. Rangga, A. Nasution, and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,” JURNAL INFORMATIKA, vol. 6, no. 2, pp. 212–218, 2019, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/ji
M. I. Fikri, T. S. Sabrila, Y. Azhar, and U. M. Malang, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter”.
A. Novantirani, M. S. Kania Sabariah, and V. Effendy, “Analisis Sentimen pada Twitter untuk Mengenai Penggunaan Transportasi Umum Darat Dalam Kota dengan Metode Support Vector Machine.”
R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 4, no. 3, p. 650, Jul. 2020, doi: 10.30865/mib.v4i3.2181.
D. Darwis, E. Shintya Pratiwi, A. Ferico, and O. Pasaribu, “PENERAPAN ALGORITMA SVM UNTUK ANALISIS SENTIMEN PADA DATA TWITTER KOMISI PEMBERANTASAN KORUPSI REPUBLIK INDONESIA,” 2020.
N. Dwi Putranti and E. Winarko, “Analisis Sentimen Twitter untuk Teks Berbahasa Indonesia dengan Maximum Entropy dan Support Vector Machine,” IJCCS, vol. 8, no. 1, pp. 91–100, 2014.
Y. Tao, F. Zhang, C. Shi, and Y. Chen, “Social media data-based sentiment analysis of tourists’ air quality perceptions,” Sustainability (Switzerland), vol. 11, no. 18, Sep. 2019, doi: 10.3390/su11185070.
M. R. Huq, A. Ali, and A. Rahman, “Sentiment Analysis on Twitter Data using KNN and SVM,” 2017. [Online]. Available: www.ijacsa.thesai.org
AWC, “What is Sentiment Analysis?,” https://aws.amazon.com/what-is/sentiment-analysis/#:~:text=Sentiment%20analysis%20is%20the%20process,social%20media%20comments%2C%20and%20reviews.
Rohith Gandhi, “Support Vector Machine — Introduction to Machine Learning Algorithms,” https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47.
Sruthi E R and Analytics Vidyha, “Understand Random Forest Algorithms With Examples (Updated 2023),” https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/.
Tokenex, “What is Tokenization?,” Tokenex. Accessed: Oct. 24, 2023. [Online]. Available: https://www.tokenex.com/blog/what-is-tokenization/
Alexander S Gillis, “Definition Lemmatization,” 2023. Accessed: Oct. 24, 2023. [Online]. Available: https://www.techtarget.com/searchenterpriseai/definition/lemmatization
Downloads
Published
How to Cite
Issue
Section
License
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that this paper has not been published in the same form elsewhere.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above-mentioned paper as a whole to UMB. The copyright transfer covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
Our Articles are licensed under CC BY-NC
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
