SENTIMENT ANALYSIS OF CONTENT PERMENKOMINFO NO.5 OF 2020 USING A CLASSIFICATION ALGORITHM

  • Mohammad Amada Universitas Esa Unggul
  • Munawar Munawar Esa Unggul University
  • Marzuki Pilliang Esa Unggul University
Keywords: Sentiment analysis, PERMENKOMINFO No.5 of 2020, Content Regulation

Abstract

This study aims to evaluate the impact of the policy issued by the Minister of Communication and Information Technology (PERMENKOMINFO No.5 of 2020) on the public's ability to access content through Private Scope Electronic System Providers (PSE). The study uses sentiment analysis and data classification methods to analyze the content of PERMENKOMINFO No.5 of 2020 and provides results on the accuracy of sentiment prediction. The results of the study show that the data classification method in sentiment analysis can provide accurate results in predicting the sentiment towards the content of PERMENKOMINFO No.5 of 2020. The study also highlights the need for improvement and better policy to ensure the interests of the public in accessing online information. The negative sentiment of 80.34% obtained through sentiment analysis provides important contributions for policy evaluation and feedback for improvement. This study provides valuable insights into the public's sentiment towards the PERMENKOMINFO No.5 of 2020 policy and its impact on their ability to access content. It also contributes to understanding the legal uncertainty in accessing content and reinforces the case for better policy to ensure the interests of the public.

Downloads

Download data is not yet available.

References

I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, Jan. 2023.

M. Pilliang, H. Akbar, and G. Firmansyah, “Sentiment Analysis for Super Applications in Indonesia: A Case Study of Gov2Go App,” Proc. Int. Conf. Electr. Eng. Informatics, vol. 2022-October, pp. 80–85, 2022.

I. G. N. D. Adnyana, F. Adams, A. W. Oktavia, E. Ermatita, and S. Sarika, “Analisis Sentimen Terhadap Undang-Undang Cipta Kerja Menggunakan Metode Naïve Bayes,” in SENAMIKA, 2021, vol. 2, no. 2, pp. 120–129.

P. Sejati, M. Munawar, M. Pilliang, and H. Akbar, “Studi Komparasi Naive Bayes, K-Nearest Neighbor, dan Random Forest untuk Prediksi Calon Mahasiswa yang Diterima atau Mundur,” J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 7, pp. 1341–1348, Dec. 2022.

Y. V. Wijaya, A. Erfina, and C. Warman, “Analisis Sentimen Seputar UU ITE Menggunakan Algoritma Support Vector Machine,” Progresif J. Ilm. Komput., vol. 17, no. 2, pp. 1–14, Aug. 2021.

E. N. Hamdana, M. Balya, and I. Alfahmi, “Pengembangan Sistem Analisis Sentimen Berbasis Java Pada Data Twitter Terhadap Omnibus Law Menggunakan Algoritma Naïve Bayes dan K-Nearst Neighbor (K-NN),” J. Inform. Polinema, vol. 7, no. 2, pp. 79–84, Feb. 2021.

A. Syaifuddin and M. Muslimin, “Analisis Sentimen Pada Sosial Media Tentang Implementasi Kebijakan PSE Kominfo Menggunakan Algoritme Lexicon Based,” in Seminar Nasional Fakultas Teknik, 2022, vol. 1, no. 1, pp. 7–14.

A. Rahman, W. Wiranto, and A. Doewes, “Online News Classification Using Multinomial Naive Bayes,” ITSMART J. Teknol. dan Inf., vol. 6, no. 1, pp. 32–38, Aug. 2017.

R. Rismayani, H. SY, T. Darwansyah, and I. Mansyur, “Implementasi Algoritma Text Mining dan Cosine Similarity untuk Desain Sistem Aspirasi Publik Berbasis Mobile,” Komputika J. Sist. Komput., vol. 11, no. 2, pp. 169–176, Aug. 2022.

D. S. Suparno and M. Rosyda, “Penggunaan Text Modeling Untuk Identifikasi Kesalahan Penulisan Kata Pada Teks Pidato Bupati Banggai Sulawesi Tengah,” J. Media Inform. Budidarma, vol. 5, no. 3, pp. 779–789, Jul. 2021.

Y. Afrillia, L. Rosnita, and D. Siska, “Analisis Sentimen Pengguna Twitter Terhadap Isu Kesetaraan Gender Dalam Penerapan Permendikbudristek Nomor 30 Tahun 2021 Menggunakan Textblob Analysis Of Twitter User Sentiment Towards To Issue Of Gender Equality In The Implementation Of Permendikbudristek Number 30 Of 2021 Using Textblob,” J. INFORMATICS Comput. Sci., vol. 8, no. 2, pp. 93–98, 2022.

P. Aditiya, U. Enri, and I. Maulana, “Analisis Sentimen Ulasan Pengguna Aplikasi Myim3 Pada Situs Google Play Menggunakan Support Vector Machine,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 4, pp. 1020–1028, Aug. 2022.

S. Mujahidin, B. Prasetio, and M. C. C. Utomo, “Implementasi Analisis Sentimen Masyarakat Mengenai Kenaikan Harga BBM Pada Komentar Youtube Dengan Metode Gaussian Naïve Bayes,” Voteteknika (Vocational Tek. Elektron. dan Inform., vol. 10, no. 3, pp. 17–24, Sep. 2022.

O. I. Gifari, M. Adha, I. Rifky Hendrawan, and F. F. S. Durrand, “Film Review Sentiment Analysis Using TF-IDF and Support Vector Machine,” J. Inf. Technol., vol. 2, no. 1, pp. 36–40, Mar. 2022.

F. Koto and G. Y. Rahmaningtyas, “Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs,” Proc. 2017 Int. Conf. Asian Lang. Process. IALP 2017, vol. 2018-January, pp. 391–394, Feb. 2018.

M. G. Pradana, “Penggunaan Fitur Wordcloud Dan Document Term Matrix Dalam Text Mining,” J. Ilm. Inform., vol. 8, no. 01, pp. 38–43, Mar. 2020.

A. M. Priyatno and L. Ningsih, “TF-IDF Weighting to Detect Spammer Accounts on Twitter based on Tweets and Retweet Representation of Tweets,” Sist. J. Sist. Inf., vol. 11, no. 3, pp. 614–622, Sep. 2022.

Published
2023-02-28
How to Cite
[1]
M. Amada, M. Munawar, and M. Pilliang, “SENTIMENT ANALYSIS OF CONTENT PERMENKOMINFO NO.5 OF 2020 USING A CLASSIFICATION ALGORITHM”, jitk, vol. 8, no. 2, pp. 131 - 138, Feb. 2023.