Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024

ika septiana; Debby Alita

doi:10.30591/jpit.v9i3.6640

Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024

ika septiana, Debby Alita

Abstract

The implementation of the 2024 elections is regulated in the General Election Commission Regulation (PKPU) Number 3 of 2022, which also stipulates the election schedule and stages.After the simultaneous general elections that took place on February 14, 2024, problems arose among the public regarding the Quick Count results, especially for the Presidential election.The Quick Count results themselves generated various opinions, both positive and negative.In the post-election Twitter page, there are many conversations in cyberspace related to the Quick Count results on Twitter. Thus, sentiment analysis can be used to classify tweets and comments about the 2024 election quick count results into three categories, namely positive, negative, and neutral.Thus, this analysis is expected to provide some significant benefits related to the quick count results in the 2024 election. Random Forest and Support Vector Machine are two machine learning techniques used to measure how accurate the resulting sentiment analysis is. From the results of the research that has been carried out, there are 2000 data collected during February 2024. After preprocessing and labeling, there are 1,116 positive class data, 730 negative class data and 154 neutral class data.From the results of the comparison of the algorithms evaluated, the accuracy value of the two algorithms was obtained.The Random Forest algorithm produces an accuracy of 78%, while the SVM algorithm produces an accuracy of 80%.This shows that in sentiment analysis on the 2024 election quick count, the SVM method obtained a greater accuracy value compared to Random Forest.

Keywords

Analisis Sentimen, Machine Learning, Quick Count, Random Forest, Support Vector Machine, Twitter.

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References

Id.wikipedia.org, “Demokrasi,” id.wikipedia.org. Accessed: Feb. 25, 2024. [Online]. Available: https://id.wikipedia.org/wiki/Demokrasi

R. Vindua and A. U. Zailani, “Analisis Sentimen Pemilu Indonesia Tahun 2024 Dari Media Sosial Twitter Menggunakan Python,” JURIKOM (Jurnal Ris. Komputer), vol. 10, no. 2, p. 479, Apr. 2023, doi: 10.30865/jurikom.v10i2.5945.

A. E. Subiyanto, “Pemilihan Umum Serentak yang Berintegritas sebagai Pembaruan Demokrasi Indonesia,” J. Konstitusi, vol. 17, no. 2, p. 355, Aug. 2020, doi: 10.31078/jk1726.

S. Arsyad, “Pemilu Indonesia 2024 Tanggal Berapa? Cek Jadwal dan Tahapannya di Sini!,” detikSulsel. Accessed: Feb. 28, 2024. [Online]. Available: https://www.detik.com/sulsel/berita/d-7119220/pemilu-indonesia-2024-tanggal-berapa-cek-jadwal-dan-tahapannya-di-sini%0A

D. Savitri, “Metode dan Cara Kerja Quick Count Pilpres 2024, Ternyata Pakai Langkah Ilmiah!,” detikEdu. Accessed: Feb. 25, 2024. [Online]. Available: https://www.detik.com/edu/detikpedia/d-7194893/metode-dan-cara-kerja-quick-count-pilpres-2024-ternyata-pakai-langkah-ilmiah%0A

O. Manullang and C. Prianto, “Analisis Sentimen dalam Memprediksi Hasil Pemilu Presiden dan Wakil Presiden : Systematic Literature Review,” J. J-COM (Jurnal Inform. dan Teknol. Komputer), vol. 04, pp. 104–113, 2023, [Online]. Available: https://ejurnalunsam.id/index.php/jicom/

P. Elisa and A. Rahman Isnain, “COMPARISON OF RANDOM FOREST, SUPPORT VECTOR MACHINE AND NAIVE BAYES ALGORITHMS TO ANALYZE SENTIMENT TOWARDS MENTAL HEALTH STIGMA,” J. Tek. Inform., vol. 5, no. 1, pp. 321–329, 2024, doi: 10.52436/1.jutif.2024.5.1.1817.

L. Aji Andika and P. Amalia Nur Azizah, “Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier,” Indones. J. Appl. Stat., vol. 2, no. 1, 2019.

P. Wahyuningtias, H. Warih Utami, U. Ahda Raihan, H. Nur Hanifah, and Y. Nicholas Adanson, “COMPARISON OF RANDOM FOREST AND SUPPORT VECTOR MACHINE METHODS ON TWITTER SENTIMENT ANALYSIS (CASE STUDY: INTERNET SELEBGRAM RACHEL VENNYA ESCAPE FROM QUARANTINE),” J. Tek. Inform., vol. 3, no. 1, pp. 141–145, 2022, doi: 10.20884/1.jutif.2022.3.1.168.

N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” J. Inform. J. Pengemb. IT, vol. 6, no. 3, pp. 150–155, 2021, [Online]. Available: http://situs.com

A. N. Syafia, M. F. Hidayattullah, and W. Suteddy, “Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS,” J. Tek. Inform., vol. 8, no. 3, 2023.

S. Algifari Rismawan, Y. Syahidin, P. Piksi Ganesha Bandung, J. Gatot Subroto No, and K. Batununggal, “Implementasi Website Berita Online Menggunakan Metode Crawling Data Dengan Bahasa Pemrograman Python,” J. Tek. Inform. dan Sist. Inf., vol. 10, no. 3, pp. 167–178, 2023, [Online]. Available: http://jurnal.mdp.ac.id

A. Nofandi, N. Y. Setiawan, and D. W. Brata, “Analisis Sentimen Ulasan Pelanggan dengan Metode sSupport Vector Machine (SVM) untuk Peningkatan Kualitas Layanan pada Restoran Warung Wareg,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 1, pp. 458–466, 2023, [Online]. Available: http://j-ptiik.ub.ac.id

A. Ilham and W. Pramusinto, “ANALISIS SENTIMEN MASYARAKAT TERHADAP KESEHATAN MENTAL PADA TWITTER MENGGUNAKAN ALGORITME K-NEAREST NEIGHBOR,” Indones. J. Appl. Stat., vol. 2, no. 2, 2023.

V. Fitriyana et al., “Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine,” 2023.

R. Vincent, I. Maulana, and O. Komarudin, “PERBANDINGAN KLASIFIKASI NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM ANALISIS SENTIMEN DENGAN MULTICLASS DI TWITTER,” 2023.

M. Fachriza and Munawar, “Analisis Sentimen Kalimat Depresi Pada Pengguna Twitter DenganNaive Bayes, Support Vector Machine, Random Forest,” J. Tek. Univ. Muhammadiyah Ponorogo, pp. 49–58, 2023, [Online]. Available: http://studentjournal.umpo.ac.id/index.php/komputek

Suci Amaliah, M. Nusrang, and A. Aswi, “Penerapan Metode Random Forest Untuk Klasifikasi Varian Minuman Kopi di Kedai Kopi Konijiwa Bantaeng,” VARIANSI J. Stat. Its Appl. Teach. Res., vol. 4, no. 3, pp. 121–127, Dec. 2022, doi: 10.35580/variansiunm31.

Intan Permata and Esther Sorta Mauli Nababan, “Analisis Perbandingan Algoritma XGBoost dan Algoritma Random Forest Ensemble Learning pada Klasifikasi Keputusan Kredit,” J. Ris. RUMPUN Mat. DAN ILMU Pengetah. ALAM, vol. 2, no. 2, pp. 65–71, Jul. 2023, doi: 10.55606/jurrimipa.v2i2.1336.

N. Fathirachman Mahing et al., “KLASIFIKASI TINGKAT STRES DARI DATA BERBENTUK TEKS DENGAN MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN RANDOM FOREST,” J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 7, pp. 1527–1536, 2023, doi: 10.25126/jtiik2023108010.

A. Saepudin, A. Faqih, and G. Dwilestari, “Perbandingan Algoritma Klasifikasi Support Vector Machine, Random Forest dan Logistic Regression Pada Ulasan Shopee,” J. TEKNO KOMPAK, vol. 18, no. 1, pp. 178–192, 2024.

R. Nurhidayat and K. E. Dewi, “PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DAN FITUR EKSTRAKSI N-GRAM DALAM ANALISIS SENTIMEN BERBASIS ASPEK,” KOMPUTA J. Ilm. Komput. dan Inform. , vol. 12, no. 1, pp. 91–100, 2023, [Online]. Available: https://www.kaggle.com/datasets/hafidahmusthaanah/skincare-review?select=00.+Review.csv.

A. C. Khotimah and E. Utami, “Comparison Naive Bayes Classifier, K-Nearest Neighbor, and Support Vector Machine in the classification of individual on twitter account,” J. Tek. Inform., vol. 3, no. 3, pp. 673–680, 2022, [Online]. Available: https://doi.org/10.20884/1.jutif.2022.3.3.254

DOI: https://doi.org/10.30591/jpit.v9i3.6640