Analisis Sentimen Program Mbkm Pada Media Sosial Twitter Menggunakan KNN Dan SMOTE

Authors

  • Komang Pramayasa Universitas Pendidikan Ganesha
  • I Md Dendi Maysanjaya Universitas Pendidikan Ganesha
  • I Gusti Ayu Agung Diatri Indradewi Universitas Pendidikan Ganesha

DOI:

https://doi.org/10.31598/sintechjournal.v6i2.1372

Keywords:

MBKM, sentiment analysis, KNN, SMOTE

Abstract

The Merdeka Belajar-Kampus Merdeka (MBKM) program is a relatively new program implemented in Indonesia since February 2020. Like a new program, the implementation of the MBKM program is also followed by various pro and con attitudes. Therefore, a sentiment analysis technique is needed to determine the public opinion towards the MBKM program. The purpose of this study is to determine the performance of the KNN method in performing sentiment classification optimized by the SMOTE method in overcoming the problem of unbalanced data and to determine the tendency of public sentiment towards the implementation of the MBKM program. Based on the research results, the KNN method optimized with the SMOTE method is proven to improve classification performance. From initially producing an accuracy value of 76.13%, precision of 76.03%, recall of 76.13% and f1-score of 76.01% there was an increase in accuracy value to 76.13%, precision to 76.03%, recall to 76.13%, and f1-score to 76.01%. In this study, it was found that community responses tended to be neutral towards the MBKM program. The community feels that the MBKM program is a program that can increase student experience. However, there are still program systems that are considered complicated and need to be evaluated.

Downloads

Download data is not yet available.

References

L. W. Santoso dan Yulia, “Predicting Student Performance in Higher Education using Multi-Regression Models,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 3, hal. 1354–1360, 2020, doi: 10.12928/TELKOMNIKA.v18i3.14802.

Dirjen Pendidikan Tinggi Kementerian Pendidikan dan Kebudayaan, “Buku Panduan MBKM,” Buku Pandu. Merdeka Belajar-Kampus Merdeka, hal. 1–42, 2020.

I. B. G. Sarasvananda, D. Selivan, M. L. Radhitya, dan I. N. T. A. Putra, “Analisis Sentimen Pada Pembelajaran Daring di Indonesia Melalui Twitter Menggunakan Naïve Bayes Classifier,” SINTECH (Science Inf. Technol. J., vol. 5, no. 2, hal. 227–233, 2022.

W. E. Nurjanah, R. S. Perdana, dan 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, vol. 1, no. 12, hal. 1750–1757, 2017.

A. Salam, J. Zeniarja, dan R. S. U. Khasanah, “Analisis Sentimen Data Komentar Sosial Media Facebook Dengan K-Nearest Neighbor (Studi Kasus Pada Akun Jasa Ekspedisi Barang J&T Ekpress Indonesia),” in SINTAK, 2018, hal. 480–486.

M. W. Pertiwi, “Analisis Sentimen Opini Publik Mengenai Sarana dan Transportasi Mudik Tahun 2019 Pada Twitter Menggunakan Algoritma Naïve Bayes, Neural Network, K-NN dan SVM,” Inti Nusa Mandiri, vol. 14, no. 1, hal. 27–32, 2019.

N. P. Y. T. Wijayanti, E. N. Kencana, dan I. W. Sumarjaya, “SMOTE: Potensi dan Kekurangannya Pada Survei,” E-Jurnal Mat., vol. 10, no. 4, hal. 235, 2021, doi: 10.24843/mtk.2021.v10.i04.p348.

S. Maldonado, J. López, dan C. Vairetti, “An Alternative SMOTE Oversampling Strategy for High-dimensional Datasets,” Appl. Soft Comput. J., vol. 76, hal. 380–389, 2019, doi: 10.1016/j.asoc.2018.12.024.

A. Nurhopipah dan C. Magnolia, “Perbandingan Metode Resampling Pada Imbalanced Dataset Untuk Klasifikasi Komentar Program MBKM,” J. Publ. Ilmu Komput. Dan Multimed., vol. 1, no. 2, hal. 9–22, 2022.

J. A. Septian, T. M. Fahrudin, dan A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor,” INSYST J. Intell. Syst. Comput., vol. 1, no. 1, hal. 43–49, 2019, [Daring]. Tersedia pada: https://t.co/9WloaWpfD5

F. N. Rozi dan D. H. Sulistyawati, “Klasifikasi Berita Hoax Pilpres Menggunakan Metode Modified K-Nearest Neighbor dan Pembobotan Menggunakan Ff-IDF,” Konvergensi, vol. 15, no. 1, hal. 1–10, 2019, doi: 10.30996/konv.v15i1.2828.

M. I. Ramadhon, “Analisis Sentimen Terhadap Pemindahan Ibu Kota Indonesia Pada Media Sosial Twitter Menggunakan Metode Algoritma K-Nearest Neighbor (K-Nn),” 2020.

M. Rivki dan A. M. Bachtiar, “Implementasi Algoritma K-Nearest Neighbor Dalam Pengklasifikasian Follower Twitter Yang Menggunakan Bahasa Indonesia,” J. Sist. Inf., vol. 13, no. 1, hal. 31–37, 2017, doi: 10.21609/jsi.v13i1.500.

Downloads

Published

2023-08-31

How to Cite

[1]
K. Pramayasa, I. M. D. Maysanjaya, and I. G. A. A. D. Indradewi, “Analisis Sentimen Program Mbkm Pada Media Sosial Twitter Menggunakan KNN Dan SMOTE”, SINTECH Journal, vol. 6, no. 2, pp. 89-98, Aug. 2023.

Issue

Vol. 6 No. 2 (2023): SINTECH Journal Edition Agustus 2023

Section

Articles

License

Copyright (c) 2023 Komang Pramayasa, I Md Dendi Maysanjaya, I Gusti Ayu Agung Diatri Indradewi

Creative Commons License

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

Copyright in each article belongs to the author.

  1. The authors admit that SINTECH Journal as a publisher who published the first time under   Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
  2. Authors can include writing separately, regulate distribution of non-ekskulif of manuscripts that have been published in this journal into another version (eg sent to respository institution author, publication into a book, etc.), by recognizing that the manuscripts have been published for the first time in SINTECH Journal