Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode K-Nearest Neighbor


Authors

  • Dimas Ferarizki Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Yusra Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Muhammad Fikry Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Febi Yanto Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
  • Fitri Insani Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia

DOI:

https://doi.org/10.30865/klik.v4i2.1306

Keywords:

Recession; Sentiment Classification; K-Nearest Neighbor; Twitter; Economy

Abstract

A recession is a decline in overall economic activity, this is considered a phase of significant and sustainable economic decline in various sectors and economic indicators. The threat of a recession in 2023 has become a topic of discussion in many countries, including Indonesia. This happens because Indonesia is threatened as a country affected by a recession due to weakening economic activity in the real sector. This sentiment classification research aims to analyze public opinion and opinion regarding the issue of recession news in 2023 which is conveyed via the social media platform Twitter. This research aims to understand whether these opinions fall into the category of positive sentiment or negative sentiment. Apart from that, this research also aims to measure the level of accuracy in classifying these sentiments into appropriate classes. This research has several main processes starting from data collection then manual data labeling, text processing, feature weighting (TF-IDF), Thresholding feature selection and K-Nearest Neighbor method classification. Based on the classification results using a testing model from a total of 1000 comment data divided between 596 positive class data and 404 negative class Twitter data regarding the threat of recession in 2023, the highest accuracy results were obtained at 85% at a value of k = 3 using the 90:10 comparison model training and testing data

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References

S. Blandina, A. Noor Fitrian, dan W. Septiyani, “Strategi Menghindarkan Indonesia dari Ancaman Resesi Ekonomi di Masa Pandemi,” Efektor, vol. 7, no. 2, hlm. 181–190, Des 2020, doi: 10.29407/e.v7i2.15043.

S. Itmam, “Resesi Ekonomi dan Implikasinya dari Perspektif Hukum Bisnis,” Equilibrium: Jurnal Ekonomi Syariah, vol. 9, no. 1, hlm. 179, Jun 2021, doi: 10.21043/equilibrium.v9i1.10012.

A. Adhari, “Penataan Ancaman Ekonomi Sebagai Bagian Dari Keadaan Bahaya Di Indonesia,” Dialogia Iuridica: Jurnal Hukum Bisnis dan Investasi, vol. 11, hlm. 33, Nov 2020.

Badan Pusat Statistik, “Berita Resmi Statistik,” 2022. Diakses: 11 Desember 2022. Tersedia pada: https://www.bps.go.id/website/materi_ind/materiBrsInd-20220805094500

A. Sandra, A. Syach, V. Aulya, dan D. Kustiawati, “Studi Literatur?: Mempersiapkan Investasi untuk Hadapi Isu Resesi Ekonomi di Indonesia,” Jurnal Pendidikan dan Konseling, vol. 4, no. 6, hlm. 1–9, Des 2022, doi: https://doi.org/10.31004/jpdk.v4i6.9609.

Suaidah dan Marliyah, “Upaya Keuangan Syariah Terhadap Ancaman Resesi Global,” Edunomika, vol. 07, no. 01, hlm. 1–9, Nov 2022.

A. P. Rodrigues dan N. N. Chiplunkar, “A new big data approach for topic classification and sentiment analysis of Twitter data,” Evol Intell, vol. 15, no. 2, hlm. 877–887, Jun 2022, doi: 10.1007/s12065-019-00236-3.

M. Cindo dan D. P. Rini, “Metode Klasifikasi Pada Sentimen Analisis,” Seminar Nasional Teknologi Komputer & Sains (SAINTEKS), hlm. 66–70, 2019.

I. A. Angreni, S. A. Adisasmita, M. I. Ramli, dan S. Hamid, “Pengaruh Nilai K Pada Metode K-Nearest Neighbor (KNN) Terhadap Tingkat Akurasi Identifikasi Kerusakan Jalan,” Rekayasa Sipil, vol. 7, no. 2, hlm. 63, Jan 2019, doi: 10.22441/jrs.2018.v07.i2.01.

L. M. Sinaga, Sawaluddin, dan S. Suwilo, “Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining,” dalam IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jan 2020. doi: 10.1088/1757-899X/725/1/012106.

R. Gunawan, R. Septiadi, F. Apri Wenando, H. Mukhtar, dan Syahril, “K-Nearest Neighbor (KNN) untuk Menganalisis Sentimen terhadap Kebijakan Merdeka Belajar Kampus Merdeka pada Komentar Twitter,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 3, no. 2, hlm. 152–158, Agu 2022, doi: 10.37859/coscitech.v3i2.3841.

D. Muhidin dan A. Wibowo, “Perbandingan Kinerja Algoritma Support Vector Machine Dan K-Nearest Neighbor Terhadap Analisis Sentimen Kebijakan New Normal,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 5, hlm. 1–7, Des 2020.

M. R. Irfan, M. A. Fauzi, T. Tibyani, dan N. D. Mentari, “Twitter Sentiment Analysis on 2013 Curriculum Using Ensemble Features and K-Nearest Neighbor,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6, hlm. 5409, Des 2018, doi: 10.11591/ijece.v8i6.pp5409-5414.

E. Indrayuni, A. Nurhadi, dan D. A. Kristiyanti, “Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc,” Faktor Exacta, vol. 14, no. 2, hlm. 64, Agu 2021, doi: 10.30998/faktorexacta.v14i2.9697.

I. Romli, S. Prameswari R, dan A. Z. Kamalia, “Sentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor,” Jurnal Online Informatika, vol. 6, no. 1, hlm. 96, Jun 2021, doi: 10.15575/join.v6i1.670.

A. Yoga Pratama, Y. Umaidah, dan A. Voutama, “Analisis Sentimen Media Sosial Twitter Dengan Algoritma K-Nearest Neighbor Dan Seleksi Fitur Chi-Square (Kasus Omnibus Law Cipta Kerja),” Jurnal Sains Komputer & Informatika (J-SAKTI, vol. 5, no. 2, hlm. 897–910, 2021.

G. K. Pati dan E. Umar, “Analisis Sentimen Komentar Pengunjung Terhadap Tempat Wisata Danau Weekuri Menggunakan Metode Naive Bayes Classifier Dan K-Nearest Neighbor,” Jurnal Media Informatika Budidarma, vol. 6, no. 4, hlm. 2309, Okt 2022, doi: 10.30865/mib.v6i4.4635.

M. Fikry, L. Oktavia, dan T. Dwi Arista, “Klasifikasi Sentimen Masyarakat di Twitter terhadap Kenaikan Harga BBM dengan Metode K-NN,” JUKI?: Jurnal Komputer dan Informatika, vol. 5, 2023.

A. Deviyanto dan M. R. Didik Wahyudi, “Penerapan Analisis Sentimen Pada Pengguna Twitter Menggunakan Metode K-Nearest Neighbor” Jurnal Informatika Sunan Kalijaga, vol. 3, no. 1, hlm. 1–13, 2018.

R. Kosasih dan A. Alberto, “Analisis Sentimen Produk Permainan Menggunakan Metode TF-IDF Dan Algoritma K-Nearest Neighbor,” Jurnal Nasional Informatika dan Teknologi Jaringan, vol. 6, no. 1, 2021, doi: 10.30743/infotekjar.v6i1.3893.


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Published: 2023-10-30
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