ImplementasiAlgoritma Naïve Bayes Classifier (NBC) Untuk Analisis Sentimen Komentar Kebijakan Full Day School

Authors

  • Yarma Agustya Dewi Utami Universitas Labuhan Batu
  • Volvo Sihombing Universitas Labuhan Batu
  • Muhammad Halmi Dar Universitas Labuhan Batu

Keywords:

Analisis Sentimen, Pengklasifikasi Naïve Bayes, Metode Berbasis Leksikon

Abstract

Sentiment analysis is an important research topic and is currently being developed. Sentiment analysis is carried out to see the opinion or tendency of a person's opinion on a problem or object, whether it tends to have a negative or positive view. The main purpose of this research is to find out public sentiment towards the Full Day school policy comments from the Facebook Page of the Ministry of Education and Culture of the Republic of Indonesia and to determine the performance of the Na-ïve Bayes Classifier Algorithm. The results of this study indicate that the public's negative sentiment towards the Full Day School policy is higher than positive or neutral sentiment. The highest accuracy value is the Naïve Bayes Classifier algorithm with the trigram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy of the results. Meanwhile, the simulation results from 10 test data with 5 different NBC and Lexicon algorithms also show that the Full Day School Policy proposed by the Indonesian Minister of Education and Culture has a higher negative sentiment than positive or neutral by most Facebook users who express opinions through comments. The highest accuracy value is the Naïve Bayes Classifier algorithm with the trigram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy of the results. Meanwhile, the simulation results from 10 test data with 5 different NBC and Lexicon algorithms also show that the Full Day School Policy proposed by the Indonesian Minister of Education and Culture has a higher negative sentiment than positive or neutral by most users. Facebook that expresses opinions through comments. The highest accuracy value is the Naïve Bayes Classifier algorithm with the tri-gram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy results.

References

F. A. Sianturi, B. Sinaga, P. M. Hasugian, T. Informatika, and S. Utara, “Fuzzy Multiple Attribute Decisison Macking Dengan Metode Oreste Untuk Menentukan Lokasi Promosi,†J. Inform. Pelita Nusant., vol. 3, no. 1, pp. 63–68, 2018, [Online]. Available: http://e-jurnal.pelitanusantara.ac.id/index.php/JIPN/article/view/289.

Fricles Ariwisanto Sianturi, “Analisa metode teorema bayes dalam mendiagnosa keguguran pada ibu hamil berdasarkan jenis makanan,†Tek. Inf. dan Komput., vol. 2, no. 1, pp. 87–92, 2019, [Online]. Available: http://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/78.

T. Juninda, E. Andri, U. Kahirunnisa, N. Kurniawati, and M. Mustakim, “Penerapan Metode Promethee Untuk Pendukung Keputusan Pemilihan Smartphone Terbaik,†J. Ilm. Rekayasa dan Manaj. Sist. Inf., vol. 5, no. 2, p. 224, 2019, doi: 10.24014/rmsi.v5i2.7677.

S. P. Tamba, D. R. Hia, D. Prayitna, and A. Tryvaldy, “Pemanfaatan Teknologi Berbasis Mobile Untuk Manajemen Kontrol Nilai Dan Absensi Siswa Pada Mts Al-Ittihadiyah Medan,†vol. 2, no. 1, pp. 18–22, 2020.

D. A. Butar-butar, D. Amalia, K. Mayra, A. Nst, and Y. Naibaho, “Pemanfaatan Teknologi Informasi Dalam Pengambilan Keputusan Penilaian Karyawan Terbaik,†vol. 2, no. 1, pp. 43–46, 2020.

J. Banjarnahor and A. X. Lim, “Aplikasi Pembayaran Uang Kuliah Pada Universitas Prima Indonesia Menggunakan Metode Fuzzy Logic Berbasis Android,†vol. 2, no. 1, pp. 7–13, 2020.

O. Sihombing, N. S. Nainggolan, B. L. Gaol, and N. Kesuma, “Rancang Bangun Aplikasi Objek Wisata Kabupaten Tapanuli Tengah Berbasis Android,†vol. 2, no. 1, pp. 14–17, 2020.

J. Wijaya, V. Frans, and F. Azmi, “Aplikasi Traveling Salesman Problem Dengan GPS dan Metode Backtracking,†vol. 3, no. 2, pp. 81–90, 2020.

B. Krismoyo and J. R. Sagala, “PENERAPAN METODE WEIGHTED PRODUCT ( WP ) MENENTUKAN SISWA DROP OUT PADA,†vol. 3, no. 2, pp. 8–14, 2020.

W. Purba, D. Ujung, T. Wahyuni, L. Sihaloho, and J. Damanik, “PERANCANGAN SISTEM INFORMASI PEMESANAN TIKET ONLINE PADA KMP . IHAN BATAK BERBASIS,†vol. 3, no. 2, pp. 65–75, 2020.

A. Firman, H. F. Wowor, X. Najoan, J. Teknik, E. Fakultas, and T. Unsrat, “Sistem Informasi Perpustakaan Online Berbasis Web,†E-Journal Tek. Elektro Dan Komput., 2016.

D. Sitanggang, S. Simangunsong, R. U. Sipayung, and A. S. Nababan, “Perancangan Aplikasi Penyeleksian Penerimaan Siswa Untuk Mengikuti Oliampiade Sains Berbasis Android,†vol. 3, no. 2, pp. 34–43, 2020.

B. Kurniawan, S. Effendi, and O. S. Sitompul, “Klasifikasi Konten Berita Dengan Metode Text Mining,†J. Dunia Teknol. Inf., 2012.

Published

2021-06-25

How to Cite

Dewi Utami, Y. A., Sihombing, V., & Dar, M. H. (2021). ImplementasiAlgoritma Naïve Bayes Classifier (NBC) Untuk Analisis Sentimen Komentar Kebijakan Full Day School. MEANS (Media Informasi Analisa Dan Sistem), 6(1), 61–66. Retrieved from https://ejournal.ust.ac.id/index.php/Jurnal_Means/article/view/1251

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