Sentiment Analysis Against IndiHome and First Media Internet Providers Using Ensemble Stacking Method
- Arya Rafif Muhammad Fikri Telkom University, Bandung, Indonesia
-
Jondri Jondri
*
Telkom University, Bandung,
Indonesia
- Widi Astuti Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
Customer satisfaction is one of the factors that can be used to measure the success of service in a company. In the era of the 2000s until now, internet service providers have continued to grow throughout the world, including in Indonesia. IndiHome and First Media are companies that provide internet services that make it easy for the public to communicate and obtain information. With many uses of IndiHome and First Media internet services, there are often several obstacles that cause various responses from users. Users usually channel these responses to IndiHome or First Media customer care on Twitter. The dataset for this study was obtained from Twitter using the Twitter API and the Tweepy library. The dataset that has been collected is 6.962 tweets for the IndiHome dataset and 8,089 tweets for the First Media dataset. This study conducts sentiment analysis using the Ensemble Stacking with three base classifiers and a meta classifier. The base classifier used is Naïve Bayes, K-Nearest Neighbor, and Decision Tree, while the meta classifier used is Logistic Regression. This study uses the term frequency-inverse document frequency (TF-IDF) to determine the frequency value of a word in a document. This study uses two test scenarios: testing without oversampling and testing with oversampling on the dataset. The results show that Ensemble Stacking with term frequency-inverse document frequency feature extraction produces the highest accuracy, with an accuracy value of 88.27% on the IndiHome dataset and 92.56% on the First Media dataset by oversampling on both datasets.
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References
Marsya Nabila, “APJII Survey: Internet Users In Indonesia Reached 171.17 Million Throughout 2018,” Dailysocial.Id, 2019. Https://Dailysocial.Id/Post/Pengguna-Internet-Indonesia-2018 (Accessed May 28, 2022).
S. Rizkia, E. Budi, S. S. Si, D. Puspandari, And M. Pd, “Analisis Sentimen Kepuasan Pelanggan Terhadap Internet Provider Indihome Di Twitter Menggunakan Metode Decision Tree Dan Pembobotan TF-IDF,” E-Proceeding Of Engineering, Vol. 6, Pp. 9683–9693, 2019.
A. Nisa, E. Darwiyanto, And I. Asror, “Analisis Sentimen Menggunakan Naive Bayes Classifier Dengan Chi-Square Feature Selection Terhadap Penyedia Layanan Telekomunikasi,” E-Proceeding Of Engineering , Vol. 6, Pp. 8650–8658, 2019.
A. Deviyanto, M. R. Didik Wahyudi, And T. Informatika UIN Sunan Kalijaga Yogyakarta Jl Marsda Adi Sucipto No, “PENERAPAN ANALISIS SENTIMEN PADA PENGGUNA TWITTER MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” Jurnal Informatika Sunan Kalijaga), Vol. 3, No. 1, Pp. 1–13, 2018, [Online]. Available: Https://Twitter.Com/Search?L=Id&Q=AHY%20since%3A2017-01-01%20until%3A2017-01-
J. A. Septian, T. M. Fahrudin, And A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF Dan K-Nearest Neighbor,” JOURNAL OF INTELLIGENT SYSTEMS AND COMPUTATION, Pp. 43–49, 2019, [Online]. Available: Https://T.Co/9wloawpfd5
F. Selva Jumeilah, “Penerapan Support Vector Machine (SVM) Untuk Pengkategorian Penelitian,” Rekayasa Sistem Dan Teknologi Informasi, Vol. 1, No. 1, Pp. 2580–0760, 2017, [Online]. Available: Http://Jurnal.Iaii.Or.Id
N. S. Wardani, A. Prahutama, And P. Kartikasari, “ANALISIS SENTIMEN PEMINDAHAN IBU KOTA NEGARA DENGAN KLASIFIKASI NAÏVE BAYES UNTUK MODEL BERNOULLI DAN MULTINOMIAL,” JURNAL GAUSSIAN, Vol. 9, Pp. 237–246, 2020, [Online]. Available: Https://Ejournal3.Undip.Ac.Id/Index.Php/Gaussian/
R. N. Chory, M. Nasrun, And C. Setianingsih, “SENTIMENTAL ANALYSIS ON USER SATISFACTION LEVEL OF MOBILE DATA SERVICES USING SUPPORT VECTOR MACHINE (SVM) ALGORITHM,” 2019, [Online]. Available: Https://Github.Com/Jefferson-Henrique/Getoldtweets-Python.
A. Fikri Zulfikar, “PENGEMBANGAN ALGORITMA STEMMING BAHASA INDONESIA DENGAN PENDEKATAN DICTIONARY BASE STEMMING UNTUK MENENTUKAN KATA DASAR DARI KATA YANG BERIMBUHAN,” UNIVERSITAS PAMULANG, Vol. 143, No. 3, Pp. 143–146, 2017.
A. Fernández, S. García, F. Herrera, And N. V Chawla, “SMOTE For Learning From Imbalanced Data: Progress And Challenges, Marking The 15-Year Anniversary,” Journal Of Artificial Intelligence Research, Vol. 61, Pp. 863–905, 2018.
K. H. Manguri, R. N. Ramadhan, And P. R. Mohammed Amin, “Twitter Sentiment Analysis On Worldwide COVID-19 Outbreaks,” Kurdistan Journal Of Applied Research, Pp. 54–65, May 2020, Doi: 10.24017/Covid.8.
M. Farhan Muzakki And R. Febrian Umbara, “ANALISIS SENTIMEN MAHASISWA TERHADAP FASILITAS UNIVERSITAS TELKOM MENGGUNAKAN METODE JARINGAN SARAF TIRUAN DAN TF-IDF,” 2019.
O. I. Gifari, M. Adha, I. Rifky Hendrawan, F. Freddy, And S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF Dan Support Vector Machine,” JIFOTECH (JOURNAL OF INFORMATION TECHNOLOGY, Vol. 2, No. 1, 2022.
S. Fransiska And A. Irham Gufroni, “Sentiment Analysis Provider By.U On Google Play Store Reviews With TF-IDF And Support Vector Machine (SVM) Method,” Scientific Journal Of Informatics, Vol. 7, No. 2, Pp. 2407–7658, 2020, [Online]. Available: Http://Journal.Unnes.Ac.Id/Nju/Index.Php/Sji
R. Ananda Fitriansyah And Saparudin, “Penerapan Ensemble Stacking Untuk Klasifikasi Multi Kelas,” Vol. 2, No. 1, 2016, [Online]. Available: Http://Ars.Ilkom.Unsri.Ac.Id
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