ANALISA SENTIMEN UNTUK MENGIDENTIFIKASI KECENDERUNGAN RADIKALISME DENGAN NAÏVE BAYES

  • Fajar Yulianto Magister Teknologi Informasi, Teknik, Institut Sains Dan Teknologi Terpadu Surabaya
  • Hartarto Junaedi Magister Teknologi Informasi, Teknik, Institut Sains Dan Teknologi Terpadu Surabaya
  • Suhatati Tjandra Magister Teknologi Informasi, Teknik, Institut Sains Dan Teknologi Terpadu Surabaya
  • Amanda Pascarini Magister Psikologi, Universitas 17 Agustus 1945 Surabaya
Keywords: sentiment analysis, machine learning, naïve bayes, radikalisme

Abstract

ABSTRACT
Radicalism in Indonesia is still an issue that is often discussed considering that there are still many acts of radicalism in Indonesia. The relatively rapid spread of radicalism requires special handling to stop the spread. This study aims to identify radicalism by grouping it into 3 (three) groups or levels which are later expected to facilitate the search for solutions to stop the spread of radicalism. The analysis system will use sentiment analysis using the method of machine learning, namely Naive Bayes. The data used were collected through surveys to students and religious leaders. The Naive Bayes method will later serve to determine the survey results of each individual based on his group. The total data collected for this study amounted to 250 respondents who have filled out survey questions, the data will be divided into 2 types, there are 165 data used for the training phase and 85 data for testing. After processing the data, the results were obtained using classification report calculations and obtained an accuracy of 85% from 3 radical groups.

Keywords: sentiment analysis; machine learning; naïve Bayes; radicalism

 

ABSTRAK
Radikalisme di Indonesia, hingga saat ini masih menjadi suatu isu yang diperbincangkan mengingat masih banyaknya aksi-aksi radikalisme di Indonesia. Penyebaran paham radikalisme yang relative cepat memerlukan adanya penanganan khusus untuk menghentikan penyebaran tersebut. Penelitian ini bertujuan untuk mengidentifikasi radikalisme dengan mengelompokannya menjadi 3 (tiga) kelompok atau tingkatan yang nantinya diharapkan dapat memudahkan pencarian solusi penghentian penyebaran paham radikalisme tersebut. Sistem analisis akan menggunakan analisis sentiment dengan menggunakan metode dari machine learning yaitu naive bayes. Data yang digunakan sendiri dikumpulkan melalui survey kepada santri dan pemuka agama. Metode naive bayes nantinya akan berfungsi untuk menentukan hasil survey tiap individu tersebut berdasarkan kelompoknya. Total data yang terkumpul untuk penelitian ini berjumlah 250 responden yang sudah mengisi pertanyaan survey, data tersebut nantinya akan dibagi 2 jenis, terdapat 165 data digunakan untuk tahap training dan 85 data untuk testing. Setelah melakukan proses olah data hasil yang didapat menggunakan perhitungan classification report dan didapat akurasi sebesar 85% dari 3 kelompok radikal.

Kata Kunci: sentiment analysis; machine learning; naïve bayes; radikalisme

References

T. A. Sundara, S. Ekaputri, and S. Sotar, “Naïve Bayes Classifier untuk Analisis Sentimen Isu Radikalisme,” in Prosiding SISFOTEK, 2020, pp. 93–98.

M. Ibrahim, E. Bu’ulolo, and I. Lubis, “Penerapan Algoritma Naive Bayes Classifier Untuk Mendeteksi Tingkat Krediblitas Hoax News/ Fake News Pada Sosial Media Di Indonesia Berbasis Android (Studi Kasus : Kantor Tribun Medan),” RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI, vol. 1, no. 1, 2020.

B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? sentiment classification using machine learning technique,” in Empirical methods in natural language processing, 2002, pp. 79–86.

M. Al-Zewairi and G. Naymat, “Spotting the Islamist Radical within: Religious Extremists Profiling in the United State,” in Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2017) / The 7th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (ICTH-2017) / Affiliated Workshops, 2017, pp. 162–169.

V. Narayanan, I. Arora, and A. Bhatia, “Fast and accurate sentiment classification using an enhanced Naive Bayes model,” Intelligent Data Engineering and Automated Learning IDEAL, vol. 8206, pp. 194–201, 2013.

K. Suppala and N. Rao, “Sentiment Analysis Using Naïve Bayes Classifier,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, no. 8, 2019.

M. S. Muchith, “Radikalisme Dalam Dunia Pendidikan,” ADDIN Media Dialektika Ilmu Islam, vol. 10, no. 1, 2016.

A. Hermanto, “Implementasi Text Mining Menggunakan Naive Bayes Untuk Penentuan Kategori Tugas Akhir Mahasiswa Berdasarkan Abstraksinya,” KONVERGENSI, vol. 12, no. 2, pp. 1–10, 2016.

J. E. Manurung and E. T. Putri, “Penentuan Minat Bakat Menggunakan Metode Bayes Berbasiss Web,” KONVERGENSI, vol. 16, no. 2, pp. 80–89, 2020.

A. P. Wibawa et al., “Naïve Bayes Classifier for Journal Quartile Classification,” International Journal of Recent Contributions from Engineering, Science & IT (iJES), vol. 7, no. 2, p. 91, 2019.

A. W. Syaputri, E. Irwandi, and M. Mustakim, “Naïve Bayes Algorithm for Classification of Student Major’s Specialization,” Journal of Intelligent Computing & Health Informatics, vol. 1, no. 1, p. 17, 2020.

A. H. Mirza, “Application of Naive Bayes Classifier Algorithm in Determining New Student Admission Promotion Strategies,” Journal of Information Systems and Informatics, vol. 1, no. 1, pp. 14–28, 2019.

A. L. Maas, R. Daly, P. T. Pham, D. M. Huang, A. Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis,” in Conference: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011.

N. Normah, “Naïve Bayes Algorithm For Sentiment Analysis Windows Phone Store Application Reviews,” SinkrOn, vol. 3, no. 2, p. 13, 2019.

S. Khomsah, “Naive Bayes Classifier Optimization on Sentiment Analysis of Hotel Reviews,” Jurnal Penelitian Pos dan Informatika, vol. 10, no. 2, p. 157, 2020.

L. F. Narulitas, “Analisa Sentimen Pada Tinjauan Buku Dengan Algoritma K-Nearest Neighbour,” KONVERGENSI, vol. 13, no. 2, pp. 76–81, 2017

Published
2022-02-10
Section
Articles