TWITTER (X) SENTIMENT ANALYSIS OF KAMPUS MERDEKA PROGRAM USING SUPPORT VECTOR MACHINE ALGORITHM AND SELECTION FEATURE CHI-SQUARE

Mutiara Sari; Syahrullah Syahrullah; Nouval Trezandy Lapatta; Rizka Ardiansyah

doi:10.52436/1.jutif.2024.5.5.2037

Mutiara Sari Informatics Study Program, Faculty of Engineering, Universitas Tadulako, Indonesia
Syahrullah Informatics Study Program, Faculty of Engineering, Universitas Tadulako, Indonesia
Nouval Trezandy Lapatta Informatics Study Program, Faculty of Engineering, Universitas Tadulako, Indonesia
Rizka Ardiansyah Informatics Study Program, Faculty of Engineering, Universitas Tadulako, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.5.2037

Keywords: best-case scenario, chi-square, kampus merdeka, sentiment analysis, support vector machine

Abstract

Ministry of Education, Culture, Research and Technology (Kemendikbudristek) has implemented numerous policies aimed at enhancing the quality of education in the country. One of these policies is Kampus Merdeka program. The program includes various initiatives such as Teaching Campus, the Merdeka Student Exchange program, and Internship and Independent Study programs, which have gained significant popularity among students across Indonesia. However, the Kampus Merdeka program has drawn many pros and cons, with some parties supporting the initiative, but also many criticisms related to its implementation, which is considered not optimal in some educational institutions. Social media is where many of these opinions are voiced, one of the most widely used of which is twitter. In light of these circumstances, this study conducted a sentiment analysis of the independent campus program to assess public sentiment towards it. The dataset used in this research consisted of 500 tweets containing the keyword "kampus merdeka" with 250 tweets reflecting positive sentiment and 250 tweets reflecting negative sentiment. The results of the tests carried out obtained the highest increase in results in the 10:90 ratio, namely with an accuracy that increased by 14% from the previous 66% to 80%, precision also increased by 22% from the previous 67% to 89%, recall increased by 16% from the previous 58% to 79%, and the f1-score value which was previously 62% turned into 79% because it also increased by 17%.

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References

E. Simatupang and I. Yuhertiana, “Merdeka Belajar Kampus Merdeka terhadap Perubahan Paradigma Pembelajaran pada Pendidikan Tinggi: Sebuah Tinjauan Literatur,” J. Bisnis, Manajemen, dan Ekon., vol. 2, no. 2, pp. 30–38, 2021, doi: 10.47747/jbme.v2i2.230.

N. Siregar, R. Sahirah, and A. A. Harahap, “Konsep Kampus Merdeka Belajar di Era Revolusi Industri 4.0,” Fitrah J. Islam. Educ., vol. 1, no. 1, pp. 141–157, 2020, doi: 10.53802/fitrah.v1i1.13.

B. Liu, Sentiment Analysis and Opinion Mining. Springer, 2022. [Online]. Available: https://books.google.co.id/books?hl=en&lr=&id=xYhyEAAAQBAJ&oi=fnd&pg=PP1&dq=sentiment+analysis&ots=rlRyIAW1Ax&sig=hRwwZz9k4PB4LFV9Z34MPbN2GuA&redir_esc=y#v=onepage&q=sentiment analysis&f=false

I. P. Rahayu, A. Fauzi, and J. Indra, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine,” J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 296, 2022, doi: 10.30865/json.v4i2.5381.

L. A. Pramesti and N. Pratiwi, “Analisis Sentimen Twitter Terhadap Program MBKM Menggunakan Decision Tree dan Support Vector Machine,” J. Inf. Syst. Res., vol. 4, no. 4, pp. 1145–1154, 2023, doi: 10.47065/josh.v4i4.3807.

M. K. Kusairi and S. Agustian, “Metode SVM dengan Fitur Representasi FastText untuk Klasifikasi Sentimen Twitter Mengenai Program Vaksinasi Covid-19,” J. Teknol. Inf. dan Komun., vol. 13, no. 2, pp. 140–150, 2022.

M. Tsani, A. Rupaka, L. Asmoro, and B. Pradana, “Analisis Sentimen Review Transportasi Menggunakan Algoritma Support Vector Machine Berbasis Chi Square,” Smart Comp Jurnalnya Orang Pint. Komput., vol. 9, no. 1, pp. 35–39, 2020, doi: 10.30591/smartcomp.v9i1.1817.

M. HUSSEİN and F. ÖZYURT, “A New Technique for Sentiment Analysis System Based on Deep Learning Using Chi-Square Feature Selection Methods,” Balk. J. Electr. Comput. Eng., vol. 9, no. 4, pp. 320–326, 2021, doi: 10.17694/bajece.887339.

A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2789–2797, 2019, [Online]. Available: http://j-ptiik.ub.ac.id

F. Farasalsabila, E. Utami, M. Hanafi, and U. A. Yogyakarta, “ANALYSIS OF PUBLIC OPINION ON INDONESIAN TELEVISION SHOWS USING SUPPORT VECTOR MACHINE,” vol. X, no. 2, pp. 239–246, 2024.

L. Wulandari, “Penerapan Text Mining Pada Search Engine (Studi Kasus E-Commerce Shopee),” J. Teknol. Inf. …, vol. 1, no. 1, pp. 21–27, 2023, [Online]. Available: https://ejournal.pustakainovasiindonesia.com/index.php/jtmbis/article/view/4%0Ahttps://ejournal.pustakainovasiindonesia.com/index.php/jtmbis/article/download/4/3

Nurhayati, A. E. Putra, L. K. Wardhani, and Busman, “Chi-Square Feature Selection Effect on Naive Bayes Classifier Algorithm Performance for Sentiment Analysis Document,” 2019 7th Int. Conf. Cyber IT Serv. Manag. CITSM 2019, no. February, 2019, doi: 10.1109/CITSM47753.2019.8965332.

W. A. Prabowo and C. Wiguna, “Sistem Informasi UMKM Bengkel Berbasis Web Menggunakan Metode SCRUM,” J. Media Inform. Budidarma, vol. 5, no. 1, p. 149, 2021, doi: 10.30865/mib.v5i1.2604.

E. Haryatmi and S. Pramita Hervianti, “Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 386–392, 2021, doi: 10.29207/resti.v5i2.3007.

M. G. Pradana, “Penggunaan Fitur Wordcloud dan Document Term Matrix dalam Text Mining,” J. Ilm. Inform., vol. 8, no. 1, pp. 38–43, 2020.

R. Puspitasari, Y. Findawati, M. A. Rosid, P. S. Informatika, and U. M. Sidoarjo, “Sentiment Analysis of Post-Covid-19 Inflation Based on Twitter Using the K-Nearest Neighbor and Support Vector Machine Analisis Sentimen Terhadap Inflasi Pasca Covid-19 Berdasarkan Twitter Dengan Metode Klasifikasi K-Nearest Neighbor Dan,” vol. 4, no. 4, pp. 1–11, 2023.