Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning

https://doi.org/10.22146/ijccs.65176

Auliya Rahman Isnain(1*), Jepi Supriyanto(2), Muhammad Pajar Kharisma(3)

(1) Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia, Lampung
(2) Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia, Lampung
(3) Fakultas Teknik dan Ilmu Komputer, Universitas Teknokrat Indonesia, Lampung
(*) Corresponding Author

Abstract


This research was conducted to apply the KNN (K-Nearest Neighbor) algorithm in conducting sentiment analysis of Twitter users on issues related to government policies regarding Online Learning. Research using Tweet data as much as 1825 Indonesian tweet data data were collected from February 1, 2020 to September 30, 2020. Using the python library, Tweepy. word weighting using TF-IDF, will be classified into two classes of sentiment values, positive and negative. After testing with K of 20, the highest accuracy results were obtained when K = 10 with an accuracy value of 84.65% with a precision of 87%, a recall of 86% f measure 87% and an error rate of 0.12% and a tendency was also obtained. public opinion on online learning tends to be positive.


Keywords


Sentiment analysis; online learning; K Nearest Neighbor; TF-IDF; Confusion Matrix

Full Text:

PDF


References

[1]      R. R. Setiawan et al., “Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., 2017, doi: 10.1074/jbc.M209498200.

[2]      A. R. Isnain, N. S. Marga, and D. Alita, “Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm,” vol. 15, no. 1, pp. 55–64, 2021.

[3]      E. M. Ferrazzi et al., “COVID-19 Obstetrics Task Force, Lombardy, Italy: Executive management summary and short report of outcome,” Int. J. Gynecol. Obstet., 2020, doi: 10.1002/ijgo.13162.

[4]      A. M. Ramadhani and H. S. Goo, “Twitter sentiment analysis using deep learning methods,” 2017, doi: 10.1109/INAES.2017.8068556.

[5]      D. Alita, Y. Fernando, and H. Sulistiani, “Sentiment Ana,” Tekno Kompak, vol. 14, no. 2, pp. 86–91, 2020.

[6]      A. R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, p. 169, 2020, doi: 10.22146/ijccs.51743.

[7]      A. Subhan, E. Sediyono, and O. Dwi, “Analisis Sentimen Berbasis Ontologi di Level Kalimat untuk Mengukur Persepsi Produk,” vol. 02, pp. 84–97, 2015.

[8]      W. E. Nurjanah, R. S. Perdana, and M. A. Fauzi, “Analisis Sentimen Terhadap Tayangan Televisi Berdasarkan Opini Masyarakat pada Media Sosial Twitter menggunakan Metode K-Nearest Neighbor dan Pembobotan Jumlah Retweet,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, 2017, doi: 10.1074/jbc.M209498200.

[9]      M. W. Pertiwi, “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, 2019.

[10]    R. R. A. Siregar, Z. U. Siregar, and R. Arianto, “KLASIFIKASI SENTIMENT ANALYSIS PADA KOMENTAR PESERTA DIKLAT MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” KILAT, 2019, doi: 10.33322/kilat.v8i1.421.

[11]    A. Deviyanto and M. D. R. Wahyudi, “PENERAPAN ANALISIS SENTIMEN PADA PENGGUNA TWITTER MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” JISKA (Jurnal Inform. Sunan Kalijaga), 2018, doi: 10.14421/jiska.2018.31-01.

[12]    P. Studi et al., “OTOMOTIF DARI TWITTER MENGGUNAKAN KOMBINASI ALGORITMA K-NEAREST NEIGHBOR DAN PENDEKATAN LEXICON ( STUDI KASUS : MOBIL TOYOTA ) Skripsi,” 2019.

[13]    R. P. Fitrianti, A. Kurniawati, and D. Agusten, “Implementasi Algoritma K - Nearest Neighbor Terhadap Analisis Sentimen Review Restoran Dengan Teks Bahasa Indonesia,” Semin. Nas. Apl. Teknol. Inf. 2019, pp. 27–32, 2019.

[14]    M. Jabal Tursina, “Sentimen Analisis Sistem Zonasi Sekolah Pada Media Sosial Youtube Menggunakan Metode K-Nearest Neighbor Dengan Algoritma Levenshtein Distance,” Univ. Islam Negeri Syarif Hidayatullah Jakarta, 2019.

[15]    Y. Tu, “Machine learning,” in EEG Signal Processing and Feature Extraction, 2019.

[16]    T. H. Simanjuntak, W. F. Mahmudy, and Sutrisno Sutrisno, “Implementasi Modified K-Nearest Neighbor Dengan Otomatisasi Nilai K Pada Pengklasifikasian Penyakit Tanaman Kedelai,” Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, No.2, no. 2, pp. 75–79, 2017, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/15/21.

[17]    S. Zainuddin, N. Hidayat, and A. A. Soebroto, “Penerapan Algoritma Modified K-Nearest Neighbour (MKNN) Pada pengklasifikasian Penyakit Tanaman Kedelai,” Repos. J. Mhs. PTIIK UB, vol. 3, p. 8, 2014.

 



DOI: https://doi.org/10.22146/ijccs.65176

Article Metrics

Abstract views : 148 | views : 131

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2