Analisis Sentimen Publik Terhadap Elektabilitas Ganjar Pranowo di Tahun Politik 2024 di Twitter dengan Algoritma KNN dan Naïve Bayes

 (*)Dede Sandi Mail (Universitas Amikom, Yogyakarta, Indonesia)
 Ema Utami (Universitas Amikom, Yogyakarta, Indonesia)
 Kusnawi Kusnawi (Universitas Amikom, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: June 9, 2023; Published: July 23, 2023

Abstract

The political year for 2024 has now increasingly entertained all Indonesian people to hold a democratic party. Various political parties have become quite dramatic in expressing their coalitions and declaring their alignment with several presidential candidates that are known to the whole community. The electability of each presidential candidate that is determined is increasingly interesting and hotly discussed, which often makes anyone take action to voice their partisanship between the pros and cons. One of them is Ganjar Pranowo, who is a political figure for the governor of Central Java. Recently, in the middle of 2023, a political party has proposed him to advance to the seat of head of state as a presidential candidate for the upcoming 2024 election. With the existence of various polemics of opinion from various layers of society, this is the right moment to carry out an analysis as a form of polarization unanimity which is presented from various public opinions as a general description and an outline in sentiment in the form of information on the conclusions of public opinion. The stages in this research began with conducting a literature study and exploring studies related to opinions and alignments with public sentiment regarding the electability of Ganjar Pranowo as a presidential candidate, and then collecting opinion data from Twitter on the electability of Ganjar Pranowo. At the experimental stage, the authors divided the data with a percentage of 80% training data and 20% testing data. The modeling used is K-Nearest Neighbor (KNN) and Naïve Bayes to classify text data as well as make comparisons of the two. In the implementation process, the author uses python as a programming language in building the model. Confusion Matrix is used for every performance evaluation related to model accuracy in each algorithm. The results showed that the division of training data and testing data and the value of k in the K-Nearest Neighbor (KNN) model greatly affect the accuracy of the model. From the test results on the comparison of the two models, the K-Nearest Neighbor model has the best accuracy with an accuracy value of 99% of the K-Nearest Neighbor with an accuracy value of 96%. The percentage of sentiment with a comparison of 96.6% positive sentiment and 3.4% negative sentiment concluded that most people still dominate positive sentiment.

Keywords


Ganjar Pranowo; Sentiment Analysis; Twitter; K-Nearest Neighbor; Naïve Bayes

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References

A. R. Isnain, A. I. Sakti, D. Alita, and N. S. Marga, “SENTIMEN ANALISIS PUBLIK TERHADAP KEBIJAKAN LOCKDOWN PEMERINTAH JAKARTA MENGGUNAKAN ALGORITMA SVM,” Jurnal Data Mining dan Sistem Informasi, vol. 2, no. 1, p. 31, Feb. 2021, doi: 10.33365/jdmsi.v2i1.1021.

A. Bayhaqy, S. Sfenrianto, K. Nainggolan, and E. R. Kaburuan, “Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes.” [Online]. Available: http://dlvr.it/Qb83n8pic.twitter.com/8MucIMhUMO,

A. Deviyanto, M. R. Didik Wahyudi, and T. Informatika UIN Sunan Kalijaga Yogyakarta Jl Marsda Adi Sucipto No, “PENERAPAN ANALISIS SENTIMEN PADA PENGGUNA TWITTER MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” Jurnal Informatika Sunan Kalijaga), vol. 3, no. 1, pp. 1–13, 2018, [Online]. Available: https://twitter.com/search?l=id&q=AHY%20since%3A2017-01-01%20until%3A2017-01-

J. A. Septian, T. M. Fachrudin, and A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor,” Journal of Intelligent System and Computation, vol. 1, no. 1, pp. 43–49, Aug. 2019, doi: 10.52985/insyst.v1i1.36.

S. S. Salim and J. Mayary, “ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP DOMPET ELEKTRONIK DENGAN METODE LEXICON BASED DAN K – NEAREST NEIGHBOR,” Jurnal Ilmiah Informatika Komputer, vol. 25, no. 1, pp. 1–17, 2020, doi: 10.35760/ik.2020.v25i1.2411.

D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 10, no. 1, Jan. 2022, doi: 10.23960/jitet.v10i1.2262.

D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes untuk Analisis Sentimen Review Data Twitter BMKG Nasional,” vol. 15, no. 1.

R. Fajar, S. Program, P. Rekayasa, N. Lunak, and R. Bengkalis, “Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter,” vol. 3, no. 1.

T. Krisdiyanto, E. Maricha, and O. Nurharyanto, “Analisis Sentimen Opini Masyarakat Indonesia Terhadap Kebijakan PPKM pada Media Sosial Twitter Menggunakan Naïve Bayes Clasifiers,” Jurnal CoreIT, vol. 7, no. 1, 2021.

D. Anjas Ramadhan and E. Budi Setiawan SSi, “ANALISIS SENTIMEN PROGRAM ACARA DI SCTV PADA TWITTER MENGGUNAKAN METODE NAIVE BAYES DAN SUPPORT VECTOR MACHINE.”

A. Firdaus, “Aplikasi Algoritma K-Nearest Neighbor pada Analisis Sentimen Omicron Covid-19,” Jurnal Riset Statistika, pp. 85–92, Dec. 2022, doi: 10.29313/jrs.v2i2.1148.

S. Rahayu, Y. MZ, J. E. Bororing, and R. Hadiyat, “Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP,” Edumatic: Jurnal Pendidikan Informatika, vol. 6, no. 1, pp. 98–106, Jun. 2022, doi: 10.29408/edumatic.v6i1.5433.

A. D. Adhi Putra, “Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 8, no. 2, pp. 636–646, Jun. 2021, doi: 10.35957/jatisi.v8i2.962.

A. Asro’i and H. Februariyanti, “ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP PERPANJANGAN PPKM MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” Jurnal Khatulistiwa Informatika, vol. 10, no. 1, pp. 17–24, Jun. 2022, doi: 10.31294/jki.v10i1.12624.

A. Yoga Pratama et al., “Analisis Sentimen Media Sosial Twitter Dengan Algoritma K-Nearest Neighbor Dan Seleksi Fitur Chi-Square (Kasus Omnibus Law Cipta Kerja),” 2021.

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

B. Sifa Amalia, Y. Umaidah, R. Mayasari, S. Karawang Jl HSRonggo Waluyo, K. Telukjambe Timur, and K. Karawang, “ANALISIS SENTIMEN REVIEW PELANGGAN RESTORAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR,” vol. 19, no. 1, pp. 28–34, 2021.

N. Cahyani and F. Fauziah, “Analisis Sentimen Kepuasan Investasi Pada Masa Pandemi dengan Metode Support Vector Machine dan K-Nearest Neighbors,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 7, no. 2, p. 177, Dec. 2022, doi: 10.30998/string.v7i2.13937.

A. Yoga Syantara, E. Dwi Wahyuni, and V. Rahmayanti Setyaning Nastiti, “Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naïve Bayes Classifier Terhadap Kata Kunci ‘#Asiangames2018,’” REPOSITOR, vol. 3, no. 5, pp. 493–500, 2021.

P. Arsi, B. A. Kusuma, and A. Nurhakim, “Analisis Sentimen Pindah Ibu Kota Berbasis Naive Bayes Classifier,” Jurnal Informatika Upgris, vol. 7, no. 1, Jun. 2021, doi: 10.26877/jiu.v7i1.7636.

N. Krisandi, B. Helmi, and I. Prihandono, “ALGORITMA k-NEAREST NEIGHBOR DALAM KLASIFIKASI DATA HASIL PRODUKSI KELAPA SAWIT PADA PT. MINAMAS KECAMATAN PARINDU,” 2013.

R. Y. Hayuningtyas and R. Sari, “ANALISIS SENTIMEN OPINI PUBLIK BAHASA INDONESIA TERHADAP WISATA TMII MENGGUNAKAN NAÏVE BAYES DAN PSO,” Jurnal TECHNO Nusa Mandiri, vol. 16, no. 1, p. 37, 2019, [Online]. Available: http://nusamandiri.ac.id/

A. N. Syahrudin, “Jurnal Dasar Pemograman Python STMIK,” 2018.

A. Naik and L. Samant, “Correlation Review of Classification Algorithm Using Data Mining Tool: WEKA, Rapidminer, Tanagra, Orange and Knime,” in Procedia Computer Science, Elsevier B.V., 2016, pp. 662–668. doi: 10.1016/j.procs.2016.05.251.

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