Published: 2023-01-01
Analisa Sentimen Terhadap Twitter IndihomeCare Menggunakan Perbandingan Algoritma Smote, Support Vector Machine, AdaBoost dan Particle Swarm Optimization
DOI: 10.35870/jtik.v7i1.686
Ferdian Syah, Hanif Fajrin, Abiyyu Nur Afif, Muhamad Rafi Saeputra, Dinda Mirranty, Dedi Dwi Saputra
-
Ferdian Syah:
Nusamandiri University
-
Hanif Fajrin:
Nusamandiri University
-
Abiyyu Nur Afif:
Nusamandiri University
-
Muhamad Rafi Saeputra:
Nusamandiri University
-
Dinda Mirranty:
Nusamandiri University
-
Dedi Dwi Saputra:
Nusamandiri University
Abstract
Indihome is one of the largest internet service providers in Indonesia with an increasing number of subscribers every year. Indihome subscribers until the end of March 2022 were recorded at 8.7 million, growing 7.2 percent over the same period last year. However, over time, many customers have complained to IndiHome about slow internet access, sudden increases in billing and so on. Based on the description above, it is interesting to conduct research on the use of Indihome tweets which are the result of channeling opinions and comments on Twitter social media. This study uses the Smote method, Support vector machine, Adaboost and Particle swarm optimization so that the results can be compared with the level of accuracy. The results of this research show that by using the Smote method, Support vector machine obtained values of Accuracy 80.48, Precision 85.29, Recall 73.75 and AUC 0.907. As for the Smote, Support vector machine and AdaBoost methods, the Accuracy values are 80.21, Precision 85.01, Recall 73.36 and AUC 0.861. Finally, the results of the Smote and Particle swarm optimization methods obtained Accuracy values of 76.59, Precision 76.57, Recall 80.35 and AUC 0.868. Based on the research results, the Smote method and the Support vector machine (SVM) have the largest results and are considered effective with the existing dataset
Keywords
Indihome; Twitter; Support Vector Machine (SVM); Adaboost; Particle Swarm Optimization (PSO)
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 7 No. 1 (2023)
-
Section: Computer & Communication Science
-
Published: 2023-01-01
-
License: CC BY 4.0
-
Copyright: © 2023 Authors
-
DOI: 10.35870/jtik.v7i1.686
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem.
Ferdian Syah, Nusamandiri University
Fakultas Sistem Informasi, Universitas Nusa Mandiri
Hanif Fajrin, Nusamandiri University
Fakultas Sistem Informasi, Universitas Nusa Mandiri
Abiyyu Nur Afif, Nusamandiri University
Fakultas Sistem Informasi, Universitas Nusa Mandiri
Muhamad Rafi Saeputra, Nusamandiri University
Fakultas Sistem Informasi, Universitas Nusa Mandiri
Dinda Mirranty, Nusamandiri University
Fakultas Sistem Informasi, Universitas Nusa Mandiri
Dedi Dwi Saputra, Nusamandiri University
Fakultas Sistem Informasi, Universitas Nusa Mandiri
-
Dewi. 2022. Telkom (TLKM) Raup Pendapatan Rp35,20 Triliun, Pelanggan IndiHome Tembus 8,7 Juta - Market Bisnis. [Online] Bisnis.com.
-
Muliyono. 2021. Identifikasi Chatbot dalam Meningkatkan Pelayanan Online Menggunakan Metode Natural Language Processing. Jurnal Informatika Ekonomi Bisnis, 3(4), 142-147. DOI: https://doi.org/10.37034/infeb.v3i4.102.
-
Syukron, M., Santoso, R. and Widiharih, T., 2020. Perbandingan Metode Smote Random Forest Dan Smote Xgboost Untuk Klasifikasi Tingkat Penyakit Hepatitis C Pada Imbalance Class Data. Jurnal Gaussian, 9(3), pp.227-236. DOI: https://doi.org/10.14710/j.gauss.9.3.227-236.
-
Assuja, M.A. and Saniati, S., 2016. Analisis Sentimen Tweet Menggunakan Backpropagation Neural Network. Jurnal Teknoinfo, 10(2), pp.48-53. DOI: https://doi.org/10.33365/jti.v10i2.20.
-
Muhamad, H., Prasojo, C.A., Sugianto, N.A., Surtiningsih, L., Cholissodin, I. and Ilmu, F., 2017. Optimasi naïve bayes classifier dengan menggunakan particle swarm optimization pada data iris. J. Teknol. Inf. dan Ilmu Komput, 4(3), p.180.
-
Salim, S.S. and Mayary, J., 2020. Analisis Sentimen pengguna Twitter terhadap dompet elektronik dengan metode lexicon based dan k–nearest neighbor. Jurnal Ilmiah Informatika Komputer, 25(1), pp.1-17. DOI: http://dx.doi.org/10.35760/ik.2020.v25i1.2411.
-
Alita, D. and Isnain, A.R., 2020. Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier. Jurnal Komputasi, 8(2), pp.50-58. DOI: http://dx.doi.org/10.23960%2Fkomputasi.v8i2.2615.
-
Pradany, L.N. and Fatichah, C., 2016. Analisa Sentimen Kebijakan Pemerintah Pada Konten Twitter Berbahasa Indonesia Menggunakan Svm Dan K-Medoid Clustering. SCAN-Jurnal Teknologi Informasi dan Komunikasi, 11(1), pp.59-66.
-
Utami, L.D. and Wahono, R.S., 2015. Integrasi metode information gain untuk seleksi fitur dan adaboost untuk mengurangi bias pada analisis sentimen review restoran menggunakan algoritma naive bayes. Journal of Intelligent Systems, 1(2), pp.120-126.
-
Fadilah, E., 2018. Implementasi Metode Profile Matching Terhadap Sistem Pendukung Keputusan Penerimaan Dana Zakat pada Badan Amil Zakat Pertamina (BAZMA). Jurnal MATICS Vol, 10(2). DOI: https://doi.org/10.18860/mat.v10i2.5745.
-
Rusdiaman, D. and Rosiyadi, D., 2019. Analisa Sentimen terhadap Tokoh Publik Menggunakan Metode Naïve Bayes Classifier Dan Support Vector Machine. CESS (Journal of Computer Engineering, System and Science), 4(2), pp.230-235. DOI: https://doi.org/10.24114/cess.v4i2.13796.
-
Yunefri, Y., Fadrial, Y.E. and Sutejo, S., 2021. Chatbot Pada Smart Cooperative Oriented Problem Menggunakan Natural Language Processing dan Naive Bayes Classifier. INTECOMS: Journal of Information Technology and Computer Science, 4(2), pp.131-140. DOI: https://doi.org/10.31539/intecoms.v4i2.2704.
-
Muhammad, F., 2022. Analisa Sentimen Mengenai Pendidikan Tatap Muka pada Twitter Menggunakan Metode Naïve Bayes dan Support Vector Machine (Doctoral dissertation, Universitas Mercu Buana Jakarta).
-
Herwijayanti, B., Ratnawati, D.E. and Muflikhah, L., 2018. Klasifikasi Berita Online dengan menggunakan Pembobotan TF-IDF dan Cosine Similarity. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer. e-ISSN, pp.306-312.
-
Munawarah, R., Soesanto, O. and Faisal, M.R., 2016. Penerapan Metode Support Vector Machine Pada Diagnosa Hepatitis. KLIK-KUMPULAN JURNAL ILMU KOMPUTER, 3(1), pp.103-113. DOI: http://dx.doi.org/10.20527/klik.v3i1.39.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.