Komparasi Algoritma Klasifikasi Dan Penerapan Ner pada Analisis Sentimen Bencana Alam Banjir
DOI:
https://doi.org/10.22441/incomtech.v14i2.22962Keywords:
Naive bayes, Random forest, Linier regression, Support vector machine, Decission tree, KNN, Named Entity Recognition (NER),Abstract
Bencana alam akhir-akhir ini terjadi di Indonesia. Layanan jejaring sosial twitter memberi ruang bagi masyarakat untuk menanggapi terkait bencana yang disebabkan oleh fakor alam, namun tanggapan yang diberikan oleh masyarakat belum diklasifikasikan. Pada penelitian ini, analisis sentimen dilakukan terhadap tweet yang mengklasifikasikannya ke dalam kategori katastropik dan non-katastropik menggunakan metode klasifikasi komparatif dan pemodelan lain yang disebut Entity Recognition Modeling (NER). Penelitian ini menggunakan 6 algoritma klasifikasi, yaitu multinominal naive bayes, random forest, linier regression, support vector machine, decission tree dan KNN. Untuk data training dan data testing diambil dengan metode random sampling dengan presentase data training 80% dan data testing 20%. Pemodelan NER dilakukan dengan Spacy untuk mendapatkan LOCATION, ORGANIZATION, PERSON, QUANTITY, TIME. Setelah dilakukan pemodelan NER dengan spacy, dilanjutkan pengukuran accuracy, precision, recall, f1-score. Support. Perhitungan precission, recall, dan f-measure mendapatkan nilai terbaik yaitu 80%, 100%, dan 89% untuk metode NER Sedangkan untuk hasil prediksi bencana dan non bencana didapatkan hasil yaitu 81,60%, 82% dan 82%.
Downloads
References
I. Fitriyaningsih dan Y. Basani, “Prediksi Kejadian Banjir dengan Ensemble Machine Learning MenggunakanBP-NN dan SVM,” J. Teknol. dan Sist. Komput., vol. 7, no. 3, 2019.
F. Nonggala dan C. Fatichah, “Klasifikasi jenis kejadian menggunakan kombinasi NeuroNER dan Recurrent Convolutional Neural Network pada data Twitter,” vol. 4, no. 2, hal. 81–90, 2018.
M. F. Rifai, H. Jatnika, dan B. Valentino, “Penerapan Algoritma Naïve Bayes Pada Sistem Prediksi Tingkat Kelulusan Peserta Sertifikasi Microsoft Office Specialist (MOS),” PETIR, vol. 12, no. 2, 2019, doi: 10.33322/petir.v12i2.471.
R. M. Yanti, I. Santoso, dan L. H. Suadaa, “Application of Named Entity Recognition via Twitter on SpaCy in Indonesian (Case Study : Power Failure in the Special Region of Yogyakarta),” Indones. J. Inf. Syst., 2021, doi: 10.24002/ijis.v4i1.4677.
Y. Apridiansyah, N. D. M. Veronika, dan E. D. Putra, “Prediksi Kelulusan Mahasiswa Fakultas Teknik Informatika Universitas Muhammadiyah Bengkulu Menggunakan Metode Naive Bayes,” JSAI (Journal Sci. Appl. Informatics), vol. 4, no. 2, 2021, doi: 10.36085/jsai.v4i2.1701.
A. M. Maksun, Y. A. Sari, dan B. Rahayudi, “Analisis Sentimen pada Twitter Bencana Alam di Kalimantan Selatan menggunakan Metode Naïve Bayes,” vol. 5, no. 12, hal. 5614–5621, 2021.
I. Fahrur Rozi, A. Taufika Firdausi, dan K. Islamiyah, “Analisis Sentimen Pada Twitter Mengenai Pasca Bencana Menggunakan Metode Naïve Bayes Dengan Fitur N-Gram,” J. Inform. Polinema, vol. 6, no. 2, hal. 33–39, 2020, doi: 10.33795/jip.v6i2.316.
V. Chandani dan R. S. Wahono, “Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film,” J. Intell. Syst., vol. 1, no. 1, 2015.
J. Han, M. Kamber, dan J. Pei, Data Mining: Concepts and Techniques. 2012.
D. A. Lestari dan D. Mahdiana, “Penerapan Algoritma K-Nearest Neighbor pada Twitter untuk Analisis Sentimen Masyarakat Terhadap Larangan Mudik 2021,” Inform. J. Ilmu Komput., vol. 17, no. 2, 2021, doi: 10.52958/iftk.v17i2.3629.
R. Jayadi, H. M. Firmantyo, M. T. J. Dzaka, M. F. Suaidy, dan A. M. Putra, “Employee performance prediction using naïve bayes,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 8, no. 6, 2019, doi: 10.30534/ijatcse/2019/59862019.
R. M. Yanti, I. Santoso, dan L. H. Suadaa, “Application of Named Entity Recognition via Twitter on SpaCy in Indonesian ( Case Study : Power Failure in the Special Region of Yogyakarta ),” vol. 4, no. 1, hal. 76–86, 2021.
R. R. Rizqiyah, L. Muflikhah, dan M. A. Fauzi, “Pengenalan Entitas Bernama untuk Identifikasi Transaksi Akuntansi Pengenalan Entitas Bernama untuk Identifikasi Transaksi Akuntansi Menggunakan Hidden Markov Model,” no. July, 2018.
P. Attewell dan D. Monaghan, “14. Association Rules,” Data Min. Soc. Sci., vol. 8, no. 5, hal. 227–234, 2019, doi: 10.1525/9780520960596-015.
W. F. Oyewusi, O. Adekanmbi, I. Okoh, dan V. Onuigwe, “NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages.,” 2021.
W. Liao dan S. Veeramachaneni, “A Simple Semi-supervised Algorithm For Named Entity Recognition,” no. June, hal. 58–65, 2009.
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:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above mentioned paper in whole to UMB. The copyright transfer covers the exclusive 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
1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors may reproduce or authorize others to reproduce the Work or derivative works for the authors personal use or for company use, provided that the source and the UMB copyright notice are indicated, the copies are not used in any way that implies UMB endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. 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.
