Analysis of School Community Sentiment towards Personal Data Protection Law Using Support Vector Machine (SVM) Method

Authors

  • Gusti Fachman Pramudi Universitas Esa Unggul
  • Gerry Firmansyah Universitas Esa Unggul, Indonesia
  • Budi Tjahjono Universitas Esa Unggul, Indonesia
  • Agung Mulyo Widodo Universitas Esa Unggul, Indonesia

DOI:

https://doi.org/10.59888/ajosh.v1i12.121

Keywords:

School Community Sentiment, Personal Data Protection Law, Support Vector Machine (SVM)

Abstract

This research aims to analyze the awareness status of the school community regarding the right to personal data protection, test and analyze the sentiments of the school community towards the implementation of the Personal Data Protection Law and as a means of outreach regarding personal data protection laws in the world of education, especially school community besides that to find out whether the Support Vector Machine method can be used as a method in conducting Sentiment Analysis research. The results of this study can be concluded that only 38% of the school community knows about this regulation while the other 62% still don't know much about this rule. Then for the use of the Support Vector Machine method which has been carried out five (5) trials using different variations of training data and test data produces an average accuracy rate of 85.97% with the highest results on training data and test data 50% - 50% that is equal to 88.00% and the lowest result is in the experiment of training data and test data of 90% - 10% which is equal to 84.44%. For the school community's sentiment towards the Personal Data Protection Act, it was 56% or as many as 496 of 887 words. which shows a neutral response and 8% or as many as 72 out of 887 sentiment words show a negative response.

References

Adrian, Muhammad Rivza, Putra, Muhammad Papuandivitama, Rafialdy, Muhammad Hilman, & Rakhmawati, Nur Aini. (2021). Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB. Jurnal Informatika Upgris, 7(1).

Aldisa, Rima Tamara, & Maulana, Pandu. (2022). Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM. Building of Informatics, Technology and Science (BITS), 4(1), 106–109.

Asshiddiqi, Muhammad Fadli, & Lhaksmana, Kemas Muslim. (2020). Perbandingan Metode Decision Tree dan Support Vector Machine untuk Analisis Sentimen pada Instagram Mengenai Kinerja PSSI. EProceedings of Engineering, 7(3).

Fitri, Evita. (2020). Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine. Jurnal Transformatika, 18(1), 71–80.

Gunawan, Deni, Riana, Dwiza, Ardiansyah, Dian, Akbar, Fajar, & Alfarizi, Salman. (2020). Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023. V (1), 135–138.

Kurniawan, Indra, Hananto, April Lia, Hilabi, Shofa Shofia, Hananto, Agustia, Priyatna, Bayu, & Rahman, Aviv Yuniar. (2023). Perbandingan Algoritma Naive Bayes Dan SVM Dalam Sentimen Analisis Marketplace Pada Twitter. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 10(1), 731–740.

Luthfanida, Luthfanida. (2022). Analisis Sentimen Data Twitter Menggunakan Metode Naive Bayes Dan Support Vector Machine (SVM) Tentang Presiden Jokowi 3 Periode. Djtechno: Jurnal Teknologi Informasi, 3(1), 5–11.

Nada, Diva Durrotun, Soehardjoepri, Soehardjoepri, & Atok, R. Mohamad. (2023). Perbandingan Analisis Sentimen Mengenai BPJS pada Media Sosial Twitter Menggunakan Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM). Jurnal Sains Dan Seni ITS, 11(6), D480–D485.

Najib, Ahmad Choirun, Irsyad, Akhmad, Qandi, Ghiffari Assamar, & Rakhmawati, Nur Aini. (2019). Perbandingan Metode Lexicon-based dan SVM untuk Analisis Sentimen Berbasis Ontologi pada Kampanye Pilpres Indonesia Tahun 2019 di Twitter. Fountain of Informatics Journal, 4(2), 41–48.

Naufal, Mohammad Farid, Arifin, Theofilus, & Wirjawan, Hans. (2023). Analisis Perbandingan Tingkat Performa Algoritma SVM, Random Forest, dan Naïve Bayes untuk Klasifikasi Cyberbullying pada Media Sosial. Jurasik (Jurnal Riset Sistem Informasi Dan Teknik Informatika), 8(1), 82–90.

Ndruru, Agustinus. (2022). Analisis Sentimen UU Cipta Kerja Melalui Omnibus Law Menggunakan Naive Bayes Classifier (NBC) Dan Support Vector Machine (SVM). Pelita Informatika: Informasi Dan Informatika, 10(3), 85–90.

Pamungkas, Fajar Sodik, & Kharisudin, Iqbal. (2021). Analisis Sentimen dengan SVM, NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter. PRISMA, Prosiding Seminar Nasional Matematika, 4, 628–634.

Pertiwi, Melisa Winda. (2019). Analisis sentimen opini publik mengenai sarana dan transportasi mudik tahun 2019 pada twitter menggunakan algoritma naïve bayes, neural network, KNN dan SVM. Inti Nusa Mandiri, 14(1), 27–32.

Pradana, Hizkia Yotant, Slamet, Isnandar, & Zukhronah, Etik. (2023). Analisis Sentimen Kinerja Pemerintahan Menggunakan Algoritma NBC, KNN, dan SVM. Prosiding Simposium Nasional Multidisiplin (SinaMu), 4, 114–121.

Riadi, Imam, Umar, Rusydi, & Aini, Fadhilah Dhinur. (2019). Analisis Perbandingan Detection Traffic Anomaly Dengan Metode Naive Bayes Dan Support Vector Machine (Svm). ILKOM Jurnal Ilmiah, 11(1), 17–24.

Siregar, Dania, Ladayya, Faroh, Albaqi, Naufal Zhafran, & Wardana, Bintang Mahesa. (2023). Penerapan Metode Support Vector Machines (SVM) dan Metode Naïve Bayes Classifier (NBC) dalam Analisis Sentimen Publik terhadap Konsep Child-free di Media Sosial Twitter. Jurnal Statistika Dan Aplikasinya, 7(1), 93–104.

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Published

2023-09-25

Issue

Vol. 1 No. 12 (2023): Asian Journal of Social and Humanities

Section

Articles

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Copyright (c) 2023 Gusti Fachman Pramudi, Gerry Firmansyah

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