K-Nearest Neighbor (KNN) untuk Menganalisis Sentimen terhadap Kebijakan Merdeka Belajar Kampus Merdeka pada Komentar Twitter

  • Febby Apri Wenando Universitas Andalas
  • Rahman Septiadi Universitas Muhammadiyah Riau
  • Rahmad Gunawan Universitas Muhammadiyah Riau
  • Harun Mukhtar Universitas Muhammadiyah Riau
  • Syahril Universitas Muhammadiyah Riau

Abstract

On December 11, 2019, the Minister of Education and Culture of the Republic of Indonesia Nadiem Anwar Makarim issued a policy of "Merdeka Belajar". Netizens on Twitter have debated this Merdeka Belajar and became a trending topic. This study tries to analyze the sentiment of tweets about opinions on this policy by classifying whether it is a positive opinion or a negative opinion. The classification method applied is the K-Nearest Neighbor algorithm. In this study, four main processes were carried out, namely text-preprocessing, word-weighting (TF-IDF), classification and validation using k-fold cross validation. Tests were carried out with a dataset of 700 data, training was carried out using 630 training data and 70 testing data. In testing, the highest accuracy of the K-Nearest Neighbor algorithm was obtained at the k-8 value, namely 84.28%. Furthermore, validation is carried out using k-fold cross validation with a value of fold = 10 to get an accuracy of 84.42%.

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Published
2022-08-18
How to Cite
Apri Wenando, F., Septiadi, R., Gunawan, R., Mukhtar, H., & Syahril. (2022). K-Nearest Neighbor (KNN) untuk Menganalisis Sentimen terhadap Kebijakan Merdeka Belajar Kampus Merdeka pada Komentar Twitter. Jurnal CoSciTech (Computer Science and Information Technology), 3(2), 152-158. https://doi.org/10.37859/coscitech.v3i2.3841
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