Analisis Sentimen Kebutuhan Fast Track Pada Originals Vidio Menggunakan Support Vector Machine
Abstract
Sentiment analysis is an analysis of textual data that provides a detailed analysis of expressed emotions, opinions, and can predict opportunities related to the results of the analysis. This study was conducted to see how Vidio.Com users responded to the original series, which did not yet have fast track capabilities. This helps Vidio.Com grow and improve its business compared to some other OTT companies that already have this feature. This survey uses Vidio.Com's original series of comments, which consists of 1403 comments. Since the dataset has no data labels, perform sentiment analysis to determine positive, neutral, and negative emotions. Sentiment analysis revealed 663 neutral emotions, 517 positive emotions, and 224 negative emotions. Based on these results, we can conclude that most Vidio.Com users need the fast-track features of the original series. This study also used the support vector machine method to test the distribution of training data and test data with 25%, 50%, 75%, and 100% characteristics. In addition, TfIdf weighting was performed and tests were run using the KFoldCrossValidationSystem. The 25er offers the highest accuracy at 86.04%. In tests using the kfold cross-validation system, a second kfold gives the highest accuracy with an accuracy score of 87.74%.
References
[2] E. Watofa, “ANALISA WAKTU DAN BIAYA MENGGUNAKAN METODE FAST TRACK PADA PELAKSANAAN PROYEK KONSTRUKSI ( Studi Kasus : Pembangunan Ruang Poliklinik Rumah Sakit , Kab . Punjak Jaya ) 2 . TINJAUAN PUSTAKA Proyek Manajemen Waktu pelaksanaan penjadwalan Metode diagram pres,” vol. 7, no. 1, pp. 63–81, 2018.
[3] A. R. Prananda and I. Thalib, “Sentiment Analysis for Customer Review: Case Study of GO-JEK Expansion,” J. Inf. Syst. Eng. Bus. Intell., vol. 6, no. 1, p. 1, 2020, doi: 10.20473/jisebi.6.1.1-8.
[4] D. Alita, S. Priyanta, and N. Rokhman, “Analysis of Emoticon and Sarcasm Effect on Sentiment Analysis of Indonesian Language on Twitter,” J. Inf. Syst. Eng. Bus. Intell., vol. 5, no. 2, p. 100, 2019, doi: 10.20473/jisebi.5.2.100-109.
[5] F. Romadoni, Y. Umaidah, and B. N. Sari, “Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 9, no. 2, pp. 247–253, 2020, doi: 10.32736/sisfokom.v9i2.903.
[6] B. A. Ardhani, N. Chamidah, and T. Saifudin, “Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel,” J. Inf. Syst. Eng. Bus. Intell., vol. 7, no. 2, p. 119, 2021, doi: 10.20473/jisebi.7.2.119-128.
[7] A. A. Lutfi, A. E. Permanasari, and S. Fauziati, “Corrigendum: Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine,” J. Inf. Syst. Eng. Bus. Intell., vol. 4, no. 2, p. 169, 2018, doi: 10.20473/jisebi.4.2.169.
[8] I. M. A. Agastya, “Pengaruh Stemmer Bahasa Indonesia Terhadap Peforma Analisis Sentimen Terjemahan Ulasan Film,” J. Tekno Kompak, vol. 12, no. 1, p. 18, 2018, doi: 10.33365/jtk.v12i1.70.
[9] A. W. Pradana and M. Hayaty, “The Effect of Stemming and Removal of Stopwords on the Accuracy of Sentiment Analysis on Indonesian-language Texts,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, pp. 375–380, 2019, doi: 10.22219/kinetik.v4i4.912.
[10] F. Fitriyani and T. Arifin, “Penerapan Word N-Gram Untuk Sentiment Analysis Review Menggunakan Metode Support Vector Machine (Studi Kasus: Aplikasi Sambara),” Sistemasi, vol. 9, no. 3, p. 610, 2020, doi: 10.32520/stmsi.v9i3.954.
[11] F. F. Irfani, “Analisis Sentimen Review Aplikasi Ruangguru Menggunakan Algoritma Support Vector Machine,” JBMI (Jurnal Bisnis, Manajemen, dan Inform., vol. 16, no. 3, pp. 258–266, 2020, doi: 10.26487/jbmi.v16i3.8607.
[12] A. A. Lutfi, A. E. Permanasari, and S. Fauziati, “Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine,” J. Inf. Syst. Eng. Bus. Intell., vol. 4, no. 1, p. 57, 2018, doi: 10.20473/jisebi.4.1.57-64.
[13] F. D. Ananda and Y. Pristyanto, “Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 20, no. 2, pp. 407–416, 2021, doi: 10.30812/matrik.v20i2.1130.
[14] N. A. Shafirra and I. Irhamah, “Klasifikasi Sentimen Ulasan Film Indonesia dengan Konversi Speech-to-Text (STT) Menggunakan Metode Convolutional Neural Network (CNN),” J. Sains dan Seni ITS, vol. 9, no. 1, 2020, doi: 10.12962/j23373520.v9i1.51825.
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