Analisis Sentimen Pengguna Twiter terhadap Perubahan Kebijakan Skripsi sebagai Syarat Wajib Kelulusan menggunakan Metode Naïve Bayes Classifier
DOI:
https://doi.org/10.30865/mib.v8i3.7746Keywords:
Sentiment Analysis, Naïve Bayes Classifier, Thesis, Twitter, ClassificationAbstract
The Minister of Education, Culture, Research, and Technology, Nadiem Makarim, has issued a policy to abolish theses, dissertations, or final papers as mandatory graduation requirements for undergraduate and postgraduate students in universities. The requirement to write a thesis is still enforced in most universities in Indonesia to obtain a bachelor's degree. The advancement of information system technology and the ease of accessing social media have caused news to spread rapidly. This policy has sparked pros and cons among the public, including on the social media platform X (formerly Twitter). Some people agree with it, considering that it can reduce the burden on students and increase the relevance of higher education to the needs of the job market. However, others argue that abolishing theses could lower the quality of university graduates and that the replacement could be even more burdensome. The purpose of this research is to understand Twitter users' sentiments towards the policy of abolishing theses as a graduation requirement and to determine the accuracy of the Naïve Bayes Classifier in classifying these sentiments. The data used consists of 656 tweets, which were processed through several stages, including cleaning, case folding, normalization, stopword removal, tokenizing, and stemming. The data was then labeled using a lexicon-based approach, resulting in 353 negative labels and 273 positive labels. The data was subsequently weighted using TF-IDF for the classification process. The dataset was split into training and testing data with a ratio of 90:10. After classification, the study found that the Naïve Bayes Classifier successfully categorized sentiments with an accuracy of 76%.References
A. Darmansyah, “Final Semester Student Problems in Completing the Final Thesis KKU Assignment at the Faculty of Da’wah and Communication, Uin Ar-Raniry,†Ar-Raniry State Islam. Univ., vol. 1, no. 1, pp. 1–89, 2020, [Online]. Available: http://repository.radenintan.ac.id/21969/
M. T. Hidayat and W. Hasim, PANDUAN PRAKTIS UNTUK MENULIS SKRIPSI. Bandung: CV WIDINA MEDIA UTAMA, 2023.
H. Hasni, E. Malik, D. Agustyawati, A. Daholu, and R. Rusdin, “Sukses Skripsi Melalui Sosialisasi Pedoman Penulisan Skripsi Pada Mahasiswa Fakultas Ekonomi Universitas Muhammadiyah Buton,†J. Community Empower., vol. 1, no. 1, p. 1, 2022, doi: 10.31764/joce.v1i1.9549.
J. Florensius Sianipar, Y. R. Ramadhan, and I. Jaelani, “Kajian Ilmiah Informatika dan Komputer,†Media Online), vol. 4, no. 1, pp. 360–367, 2023, doi: 10.30865/klik.v4i1.1033.
Imam and I. Santoso, “Analisis Sentimen Pada Twitter Terhadap Gagalnya Pelaksanaan Piala Dunia Di Indonesia,†J. IKRAITH-Informatika, vol. 7, no. 2, p. 6, 2023.
T. N. Wijaya, R. Indriati, and M. N. Muzaki, “Analisis Sentimen Opini Publik Tentang Undang-Undang Cipta Kerja Pada Twitter,†Jambura J. Electr. Electron. Eng., vol. 3, no. 2, pp. 78–83, 2021, doi: 10.37905/jjeee.v3i2.10885.
D. N. I. Huda and C. Prianto, “Analisis Sentimen Layanan Jasa Pengiriman Pada Ulasan Play Store: Systematic Literature Review,†J. Inform. dan Teknol. Komput., vol. 4, no. 2, pp. 87–98, 2023, [Online]. Available: https://ejurnalunsam.id/index.php/jicom/
S. Ariqoh, M. A. Sunandar, and Y. Muhyidin, “Analisis Sentimen Pada Produk Cushion Di Website Female Daily Menggunakan Metode Support Vector Machine (Svm),†STORAGE J. Ilm. Tek. dan Ilmu Komput., vol. 2, no. 3, pp. 137–142, 2023, doi: 10.55123/storage.v2i3.2345.
Rayuwati, Husna Gemasih, and Irma Nizar, “IMPLEMENTASI AlGORITMA NAIVE BAYES UNTUK MEMPREDIKSI TINGKAT PENYEBARAN COVID,†Jural Ris. Rumpun Ilmu Tek., vol. 1, no. 1, pp. 38–46, 2022, doi: 10.55606/jurritek.v1i1.127.
T. A. Sundara, S. Ekaputri, and S. Sotar, “Naïve Bayes Classifier untuk Analisis Sentimen Isu Radikalisme,†Pros. SISFOTEK, vol. 4, no. 1, pp. 93–98, 2020, [Online]. Available: https://www.seminar.iaii.or.id/index.php/SISFOTEK/article/view/159
W. Wahyuni, “Analisis Sentimen terhadap Opini Feminisme Menggunakan Metode Naive Bayes,†J. Inform. Ekon. Bisnis, vol. 4, pp. 148–153, 2022, doi: 10.37034/infeb.v4i4.162.
Y. Nurtikasari, Syariful Alam, and Teguh Iman Hermanto, “Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes,†INSOLOGI J. Sains dan Teknol., vol. 1, no. 4, pp. 411–423, 2022, doi: 10.55123/insologi.v1i4.770.
K. Zuhri and N. A. O. Saputri, “Analisis Sentimen Masyarakat Terhadap Pilpres 2019 Berdasarkan Opini Dari Twitter Menggunakan Metode Naive Bayes Classifier,†J. Comput. Inf. Syst. Ampera, vol. 1, no. 3, pp. 185–199, 2020, doi: 10.51519/journalcisa.v1i3.45.
M. A. A. A. Solichin, “Analisis Sentimen MotoGP Mandalika Pada Twitter Menggunakan Metode Naïve Bayes,†J. Ticom Technol. Inf. Commun., vol. 11, no. Vol 11 No 1 (2022): Jurnal Ticom: Technology of Information and Communication, pp. 20–25, 2022, [Online]. Available: https://jurnal-ticom.jakarta.aptikom.or.id/index.php/Ticom/article/view/66/55
D. Alita and A. R. Isnain, “Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier,†J. Komputasi, vol. 8, no. 2, pp. 50–58, 2020, doi: 10.23960/komputasi.v8i2.2615.
N. Cahyono and Dewi Setiyawati, “Analisis Sentimen Pengguna Sosial Media Twitter Terhadap Perokok Di Indonesia,†Indones. J. Comput. Sci., vol. 12, no. 1, pp. 262–272, 2023, doi: 10.33022/ijcs.v12i1.3154.
R. R. Salam, M. F. Jamil, Y. Ibrahim, R. Rahmaddeni, S. Soni, and H. Herianto, “Analisis Sentimen Terhadap Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM) Menggunakan Support Vector Machine,†MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 27–35, 2023, doi: 10.57152/malcom.v3i1.590.
M. H. Mahendra, D. T. Murdiansyah, and K. M. Lhaksmana, “Analisis Sentimen Tweet COVID-19 Menggunakan Metode K-Nearest Neighbors dengan Ekstraksi Fitur TF-IDF dan CountVectorizer,†Dike J. Ilmu Multidisiplin, vol. 1, no. 2, pp. 37–43, 2023.
I. Widaningrum, D. Mustikasari, R. Arifin, S. L. Tsaqila, and D. Fatmawati, “Algoritma Term Frequency-Inverse Document Frequency (TF-IDF) dan K-Means Clustering Untuk Menentukan Kategori Dokumen,†Pros. Semin. Nas. Sist. Inf. dan Teknol., pp. 145–149, 2022.
I. Novitasari, T. B. Kurniawan, D. A. Dewi, and Misinem, “Analisis sentimen masyarakat terhadap tweet ruang guru menggunakan algoritma naive bayes classifier (NBC) [Analysis of public sentiment towards ruang guru’s tweets using the Naive Bayes Classifier (NBC) algorithm],†J. Mantik, vol. 6, no. 3, pp. 2685–4236, 2022.
S. Suhardiman and F. Purwaningtias, “Analisis Sentimen Masyarakat Terhadap Virus Corona Berdasarkan Opini Masyarakat Menggunakan Metode Naïve Bayes Classifier,†J. Pengemb. Sist. Inf. dan Inform., vol. 1, no. 4, pp. 220–232, 2021, doi: 10.47747/jpsii.v1i4.551.
K. E. Rofran et al., “SKRIPSI MENGGUNAKAN METODE ANALISIS VADER DAN METODE KLASIFIKASI NAÃVE BAYES,†vol. 7, pp. 393–402, 2024.
D. Musfiroh, U. Khaira, P. E. P. Utomo, and T. Suratno, “Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon,†MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 1, no. 1, pp. 24–33, 2021, doi: 10.57152/malcom.v1i1.20.
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