COMPARISON OF NAÏVE BAYES CLASSIFIER, SUPPORT VECTOR MACHINE, RANDOM FOREST ALGORITHMS FOR PUBLIC SENTIMENT ANALYSIS OF KIP-K PROGRAM ON TWITTER
Abstract
The Kartu Indonesia Pintar Kuliah (KIP-K) program has become a hot topic of conversation on social media Twitter, with various public sentiments regarding its implementation. The program is regulated through Minister of Education and Culture Regulation (Permendikbud) No. 10/2020, which notes an increase in the number of recipients from 552,706 in 2020 to 985,577 in 2024. However, controversy has arisen due to the alleged misuse of KIP-K funds by some influencers to support lavish lifestyles. This study aims to compare the performance of Naive Bayes, Support Vector Machine, and Random Forest algorithms in classifying public sentiment towards the KIP-K program. The research dataset was obtained from Twitter with a total of 6,842 tweets collected using crawling techniques in the time span of 2023 to 2024. The dataset was then processed through the preprocessing stage to produce clean data. The three algorithms were tested to evaluate the accuracy of each model in predicting public sentiment. The test results show that the Random Forest algorithm has the best performance with 100% accuracy, followed by Support Vector Machine with 99% accuracy, and Naive Bayes with 91% accuracy after optimization (SMOTE). Based on these findings, Random Forest proved to be the most effective algorithm in classifying sentiments related to the KIP-K program. It is hoped that the results of this research can help the management of the KIP-K program to be more targeted by providing a better understanding of public perception.
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References
D. Amrizal, D. F. Nasution, and A. Amran, “Efektivitas Pelayanan Program Kartu Indonesia Pintar (KIP) dalam Rangka Peningkatan Kualitas Pendidikan di SMA Negeri 1 Tanjung Balai,” Jurnal SOMASI (Sosial Humaniora Komunikasi), vol. 1, no. 1, pp. 11–20, 2020, doi: 10.53695/js.v1i1.27.
M. Romdoni, “Combination of Ahp and Maut Method To Determine Scholarship Recipients in Higher Education ( Case Study : Universitas Teknokrat Indonesia ) Kombinasi Metode Ahp Dan Maut Untuk Menentukan Penerima Beasiswa Pada Perguruan Tinggi ( Studi Kasus : Universitas ,” vol. 5, no. 4, pp. 1125–1135, 2024, doi: 10.52436/1.jutif.2024.5.4.1940.
Gagan Suganda, Marsani Asfi, Ridho Taufiq Subagio, and Ricky Perdana Kusuma, “Penentuan Penerima Bantuan Beasiswa Kartu Indonesia Pintar (Kip) Kuliah Menggunakan Naïve Bayes Classifier,” JSiI (Jurnal Sistem Informasi), vol. 9, no. 2, pp. 193–199, 2022, doi: 10.30656/jsii.v9i2.4376.
Sabrina Fitri Jasmine, “Pengaruh Beasiswa KIP-K Terhadap Prestasi Belajar Mahasiswa Manajemen Pendidikan Angkatan 2021 Universitas Negeri Surabaya,” Jurnal Pendidikan, Bahasa dan Budaya, vol. 2, no. 2, pp. 61–70, 2023, doi: 10.55606/jpbb.v2i2.1437.
R. Winata and R. Khairunnisa, “Pengaruh Penggunaan Dana KIP-K Terhadap Pemenuhan Kebutuhan Mahasiswa Dalam Prespektif Ekonomi Islam (Studi Pada Mahasiswa Penerima KIP- K UIN Raden Intan Lampung),” Business and Enterpreneurship Journal (BEJ), vol. 4, no. 1, pp. 8–14, 2023, doi: 10.57084/bej.v4i1.1037.
J. Homepage, D. Pramudita, Y. Akbar, and T. Wahyudi, “MALCOM: Indonesian Journal of Machine Learning and Computer Science Sentiment Analysis of the Indonesian Smart College Card Program on Social Media X Using the Naive Bayes Algorithm Analisis Sentimen Terhadap Program Kartu Indonesia Pintar Kuliah Pada Med,” Malcom , vol. 4, no. October, pp. 1420–1430, 2024, doi: 10.57152/malcom.v4i4.1565
I. Amelia and F. M. Sarimole, “Analisis Sentimen Tanggapan Pengguna Media Sosial X Terhadap Program Beasiswa KIP-Kuliah dengan Menggunakan Algoritma Support Vector Machine ( SVM ),” vol. 5, no. 3, pp. 2994–3003, 2024, doi: 10.35870/jimik.v5i3.990
A. Suryasuciramdhan, M. Meliasari, B. Meilani Ifada, and M. A. Faidh, “Analisis Framing Dalam Kasus Penyalahgunaan Kip Kuliah Mahasiswi Universitas Diponegoro Di Media Sosial X Dan Media Online Kompas.Tv,” Journal of Creative Student Research (JCSR), vol. 2, no. 3, pp. 40–51, 2024, [Online]. Available: https://doi.org/10.55606/jcsrpolitama.v2i3.3824
M. F. Z. Jannan, Y. Dwi, and P. Negara, “Analisis Sentimen Masyarakat Terhadap Penerima Beasiswa Kartu Indonesia Pintar Kuliah Dengan Metode Support Vector Machine ( Analysis of Community Sentiment Toward Indonesia Smart Card Scholarship Recipients with Support Vector Machine Method ),” pp. 26–30, 2024, doi: 10.32938/jitu.v4i2.7598.
C. Fitri, N. Halizah, and M. Kartikasari, “Implementasi Algoritma Naïve Bayes Dalam Penentuan Pemberian Beasiswa Kip Kuliah (Studi Kasus Stiki Malang) Implementation of Naïve Bayes Algorithm in Determining Kip-Kuliah Scholarship (Case Study Stiki Malang),” vol. 12, no. 2, pp. 1–6, 2024, [Online]. Available: http://repository.stiki.ac.id/id/eprint/2220.
D. A. Ardan, Mukhsar, G. N. A. Wibawa, B. Abapihi, D. C. Arisona, and A. Tenriawaru, “Analisis Sentimen Persepsi Publik Tentang Program Merdeka Belajar Kampus Merdeka di X Mengggunakan Support Vector Machine,” Science, and Technology (J-HEST), vol. 6, no. 2, pp. 151–161, 2024, doi: 10.36339/j-hest.v6i2.37.
E. Fitri, “Sentiment Analysis of the Ruangguru Application Using Naive Bayes, Random Forest and Support Vector Machine Algorithms,” Jurnal Transformatika, vol. 18, no. 1, p. 71, 2020, doi: 10.26623/transformatika.v18i1.2317.
P. Elisa and A. Rahman Isnain, “Comparison of Random Forest, Support Vector Machine and Naive Bayes Algorithms To Analyze Sentiment Towards Mental Health Stigma,” Jurnal Teknik Informatika (JUTIF), vol. 5, no. 1, pp. 321–329, 2024, doi: 10.52436/1.jutif.2024.5.1.1817
D. S. Ningsih and R. R. Suryono, “Comparison of Naïve Bayes and Information Gain Algorithms in Cyberbullying Sentiment Analysis on Twitter Perbandingan Algoritma Naïve Bayes Dan Information Gain,” vol. 5, no. 4, pp. 1085–1091, 2024, doi: 10.52436/1.jutif.2024.5.4.1908
I. Syahrohim, S. D. Saputra, R. W. Saputra, V. H. Pranatawijaya, and R. Priskila, “Perbandingan Analisis Sentimen Setelah Pilpres 2024 Di Twitter Menggunakan Algoritma Machine Learning,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, 2024, doi: 10.23960/jitet.v12i2.4249.
R. S. Arischo and D. Damayanti, “Analisis Sentimen Pinjaman Online di Twitter dengan Metode Naive Bayes Classifier dan SVM,” Jurnal Media Informatika Budidarma, vol. 8, no. 2, p. 1120, 2024, doi: 10.30865/mib.v8i2.7406.
M. J. Palepa, N. Pratiwi, and R. Q. Rohmansa, “Analisis Sentimen Masyarakat Tentang Pengaruh Politik Identitas Pada Pemilu 2024 Terhadap Toleransi Beragama Menggunakan Metode K - Nearest Neighbor,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 1, pp. 389–401, 2024, doi: 10.29100/jipi.v9i1.4957.
H. C. Husada and A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Teknika, vol. 10, no. 1, pp. 18–26, 2021, doi: 10.34148/teknika.v10i1.311.
Syahril Dwi Prasetyo, Shofa Shofiah Hilabi, and Fitri Nurapriani, “Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN,” Jurnal KomtekInfo, vol. 10, pp. 1–7, 2023, doi: 10.35134/komtekinfo.v10i1.330.
D. Pratmanto and F. F. D. Imaniawan, “Analisis Sentimen Terhadap Aplikasi Canva Menggunakan Algoritma Naive Bayes Dan K-Nearest Neighbors,” Computer Science (CO-SCIENCE), vol. 3, no. 2, pp. 110–117, 2023, doi: 10.31294/coscience.v3i2.1917.
A. Hendra and F. Fitriyani, “Analisis Sentimen Review Halodoc Menggunakan Nai ̈ve Bayes Classifier,” JISKA (Jurnal Informatika Sunan Kalijaga), vol. 6, no. 2, pp. 78–89, 2021, doi: 10.14421/jiska.2021.6.2.78-89.
I. Alamsyah and R. T. Shita, “Penerapan Metode Multinomial Naïve Bayes Untuk Implementation of the Multinomial Naïve Bayes Method To Analyze Sentiment of User Reviews of Wahyoo,” vol. 2, no. September, pp. 436–444, 2023, Available: https://senafti.budiluhur.ac.id/index.php/senafti/article/view/750
M. H. Wicaksono, M. D. Purbolaksono, and S. Al Faraby, “Perbandingan Algoritma Machine Learning untuk Analisis Sentimen Berbasis Aspek pada Review Female Daily,” eProceedings of Engineering, vol. 10, no. 3, pp. 3591–3600, 2023, [Online]. Available: https://openlibrary.telkomuniversity.ac.id/home/catalog/id/185919.html
M. I. Putri and I. Kharisudin, “Penerapan Synthetic Minority Oversampling Technique (SMOTE) Terhadap Analisis Sentimen Data Review Pengguna Aplikasi Marketplace Tokopedia,” PRISMA, Prosiding Seminar Nasional Matematika, vol. 5, pp. 759–766, 2022, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/
L. A. Hayurian and N. Hendrastuty, “Comparison of Naïve Bayes Algorithm and Support Vector Machine in Sentiment Analysis of Boycott Israeli Products on Twitter,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 3, pp. 731–738, 2024, [Online]. Available: https://doi.org/10.52436/1.jutif.2024.5.3.1813
M. R. A. Nasution and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,” Jurnal Informatika, vol. 6, no. 2, pp. 226–235, 2019, doi: 10.31311/ji.v6i2.5129.
M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Comparison of Naïve Bayes and Support Vector Machine Methods in Twitter Sentiment Analysis,” Smatika Jurnal, vol. 10, no. 02, pp. 71–76, 2020, doi: 10.32664/smatika.v10i02.455
T. J. Firdaus et al., “SENTIMENT ANALYSIS OF THE SAMBARA APPLICATION USING THE SUPPORT,” vol. 5, no. 4, pp. 1183–1192, 2024, doi: 10.52436/1.jutif.2024.5.4.2673.
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