Perbandingan Klasifikasi Single-Label dan Multi-Label Ulasan Pengguna Lapangan Futsal di Semarang Menggunakan SVM
DOI:
https://doi.org/10.22441/incomtech.v15i2.33905Keywords:
Klasifikasi Multi-Label, Single-Label, Ulasan Pengguna, Lapangan Futsal, Support Vector MachineAbstract
Futsal merupakan cabang olahraga yang semakin populer di seluruh Indonesia, termasuk di Semarang. Penelitian ini bertujuan untuk melakukan klasifikasi sentimen ulasan pengguna mengenai lapangan futsal di Kota Semarang menggunakan metode Support Vector Machine. Data penelitian diperoleh melalui scraping ulasan Google Maps dengan ekstensi Chrome “Instant Data Scraper” dan terdiri dari 1.189 ulasan. Proses penelitian mencakup pengumpulan data, Cleaning dan pre-processing (normalisasi teks, modifikasi data, tokenisasi, stop word filtering, stemming), pelabelan (single label dan multi label), pembagian data (80% pelatihan dan 20% pengujian), pemodelan menggunakan SVM (single label dengan GridSearchCV dan multi label dengan One-vs-Rest Classifier), serta evaluasi model dengan metrik presisi, recall, dan F1-Score. Hasil menunjukkan pemodelan Support Vector Machine single-label mencapai presisi 0,84, recall 0,73, dan F1-Score 0,78. Sementara pemodelan Support Vector Machine multi-label mencapai presisi 0,96, recall 0,88, dan F1-Score 0.92. Dari ulasan yang dinalisis, sebaran data pada single-label maupun multi-label menunjukan dominasi ulasan kategori Fasilitas, menegaskan bahwa Fasilitas merupakan kategori yang paling sering dikomentari oleh pengguna. Temuan ini tidak hanya memberikan wawasan praktis bagi pengelola lapangan futsal, tetapi juga berkontribusi pada pengembangan metode klasifikasi ulasan berbasis machine learning dalam domain analisis opini, khususnya dalam membandingkan performa pendekatan single-label dan multi-label pada data multi-kategori di bidang teknologi informasi.Downloads
References
A. N. Putra and S. Soegiyanto, “Manajemen Pembinaan Prestasi Futsal Kota Semarang dalam Persiapan Menghadapi PORPROV Jawa Tengah Tahun 2023,” Jurnal Sains Keolahragaan dan Kesehatan, vol. 8, no. 2, pp. 161–176, Mar. 2024, doi: 10.5614/jskk.2023.8.2.6.
J. Ipmawati, S. Saifulloh, and K. Kusnawi, “Analisis Sentimen Tempat Wisata Berdasarkan Ulasan pada Google Maps Menggunakan Algoritma Support Vector Machine,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 247–256, Jan. 2024, doi: 10.57152/malcom.v4i1.1066.
P. M. Aryanto and R. Mardhiyyah, “Analisis Sentimen Terhadap Review Google Maps Jogja City Mall Menggunakan Algoritma Support Vector Machine,” 2024, doi: 10.47065/josyc.v6i1.6049.
S. M. Liu and J.-H. Chen, “A multi-label classification based approach for sentiment classification,” Expert Syst Appl, vol. 42, no. 3, pp. 1083–1093, Feb. 2015, doi: 10.1016/j.eswa.2014.08.036.
M. Birjali, M. Kasri, and A. Beni-Hssane, “A comprehensive survey on sentiment analysis: Approaches, challenges and trends,” Knowl Based Syst, vol. 226, p. 107134, Aug. 2021, doi: 10.1016/j.knosys.2021.107134.
J. Hartmann, M. Heitmann, C. Siebert, and C. Schamp, “More than a Feeling: Accuracy and Application of Sentiment Analysis,” International Journal of Research in Marketing, vol. 40, no. 1, pp. 75–87, Mar. 2023, doi: 10.1016/j.ijresmar.2022.05.005.
A. M. S. Shaik Afzal, “Optimized Support Vector Machine Model for Visual Sentiment Analysis,” in 2021 3rd International Conference on Signal Processing and Communication (ICPSC), IEEE, May 2021, pp. 171–175. doi: 10.1109/ICSPC51351.2021.9451669.
B. Irena and Erwin Budi Setiawan, “Fake News (Hoax) Identification on Social Media Twitter using Decision Tree C4.5 Method,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi),
vol. 4, no. 4, pp. 711–716, Aug. 2020, doi: 10.29207/resti.v4i4.2125.
R. Friedman, “Tokenization in the Theory of Knowledge,” Encyclopedia, vol. 3, no. 1, pp. 380–386, Mar. 2023, doi: 10.3390/encyclopedia3010024.
N. Umar and M. Adnan Nur, “Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 4, pp. 585–590, Aug. 2022, doi: 10.29207/resti.v6i4.4179.
M. W. Sardjono, M. Cahyanti, M. Mujahidin, and R. Arianty, “PENDETEKSI KESAMAAN KATA UNTUK JUDUL PENULISAN BERBAHASA INDONESIA MENGGUNAKAN ALGORITMA STEMMING NAZIEF-ADRIANI,” Sebatik, vol. 22, no. 2, pp. 138–146, Dec. 2018, doi: 10.46984/sebatik.v22i2.320.
B. Siswanto and Y. Dani, “Sentiment Analysis about Oximeter as Covid-19 Detection Tools on Twitter Using Sastrawi Library,” in 2021 8th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), IEEE, Sep. 2021, pp. 161–164. doi: 10.1109/ICITACEE53184.2021.9617216.
Z. Rais, F. T. T. Hakiki, and R. Aprianti, “Sentiment Analysis of Peduli Lindungi Application Using the Naive Bayes Method,” SAINSMAT: Journal of Applied Sciences, Mathematics, and Its Education, vol. 11, no. 1, pp. 23–29, Mar. 2022, doi: 10.35877/sainsmat794.
N. Widaad and D. Anggraini, “SENTIMENT ANALYSIS OF CHATGPT APP USER REVIEWS USING SVM AND CNN METHODS,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 6, pp. 1687–1700, 2024, doi: 10.52436/1.jutif.2024.5.6.4010.
N. A. Sajid et al., “Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes,” Jun. 01, 2023, MDPI. doi: 10.3390/app13116804.
K. Alemerien, S. Alsarayreh, and E. Altarawneh, “Diagnosing Cardiovascular Diseases using Optimized Machine Learning Algorithms with GridSearchCV,” Journal of Applied Data Sciences, vol. 5, no. 4, pp. 1539–1552, Dec. 2024, doi: 10.47738/jads.v5i4.280.
J. Xu, “An extended one-versus-rest support vector machine for multi-label classification,” Neurocomputing, vol. 74, no. 17, pp. 3114–3124, Oct. 2011, doi: 10.1016/j.neucom.2011.04.024.
Downloads
Additional Files
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.
