Analisis Sentimen Fasilitas Belajar dan Alat Laboratorium menggunakan metode Naïve Bayes Classifier

A Ulfah Tenripada Syahar; Avin Savitri; Dewi Widyawati; Hariani Ma’Tang

doi:10.57093/jisti.v7i2.265

A Ulfah Tenripada Syahar Universitas Muslim Indonesia
Avin Savitri Universitas Muslim Indonesia
Dewi Widyawati Universitas Muslim Indonesia
Hariani Ma’Tang Universitas Muslim Indonesia

DOI: https://doi.org/10.57093/jisti.v7i2.265

Abstract

ABSTRACK

This study aims to analyze student sentiment towards learning facilities and laboratory equipment using the Naïve Bayes Classifier method. The data used in this study were obtained from social media platforms, which include student comments and statements regarding the facilities and tools available at educational institutions. The collected data is then analyzed to identify the sentiments contained, namely positive, negative, and neutral. Based on the analysis results, 170 negative sentiments, 135 positive sentiments, and 147 neutral sentiments were obtained. The Naïve Bayes algorithm produces an accuracy value of 77%, precision of 75%, recall of 66%, and F1-score of 7%. These results show that Naïve Bayes can be used to classify student sentiment towards laboratory facilities and equipment, although there is still room for improvement in increasing recall and F1-score. This research provides an overview of the quality of learning facilities and laboratory equipment and identifies areas that require more attention in the improvement and maintenance of facilities in educational institutions.

Downloads

Download data is not yet available.

Author Biographies

A Ulfah Tenripada Syahar, Universitas Muslim Indonesia

Program studi Teknik Informatika

Avin Savitri, Universitas Muslim Indonesia

Program studi Teknik Informatika

Dewi Widyawati, Universitas Muslim Indonesia

Program studi sistem informasi

Hariani Ma’Tang, Universitas Muslim Indonesia

Program Studi Teknik Elektro

References

D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier,” J. Inform. dan Tek. Elektro Terap., vol. 10, no. 1, pp. 34–40, 2022, doi: 10.23960/jitet.v10i1.2262.
[2] D. Aryanti, “Analisis Sentimen Ibukota Negara Baru Menggunakan Metode Naïve Bayes Classifier,” J. Inf. Syst. Res., vol. 3, no. 4, pp. 524–531, 2022, doi: 10.47065/josh.v3i4.1944.
[3] A. Sopian, “Manajemen Sarana Dan Prasarana,” Raudhah Proud To Be Prof. J. Tarb. Islam., vol. 4, no. 2, pp. 43–54, 2019, doi: 10.48094/raudhah.v4i2.47.
[4] W. Rachmawati and F. S. Nisa, “Sistem Informasi Pengelolaan Laboratorium Komputer Jurusan Administrasi Niaga Politeknik Negeri Malang,” J. Adm. dan Bisnis, vol. 16, no. 1, pp. 60–68, 2022.
[5] V. N. Lunetta, A. Hofstein, and M. P. Clough, “Learning and Teaching in the School Science Laboratory: An Analysis of Research, Theory, and Practice,” Handb. Res. Sci. Educ., no. January 2007, pp. 393–441, 2013, doi: 10.4324/9780203824696-18.
[6] N. L. P. Kertiasih, “Peranan Laboratorium Pendidikan untuk Menunjang Proses Perkuliahan di Poltekkes Denpasar,” J. Kesehat. Gigi (Dental Heal. Journal), vol. 4, no. 2, pp. 59–66, 2016.
[7] I. Nur Fakhri, Jondri, and R. Febrian Umbara, “Analisis Sentimen pada Kuisioner Kepuasan Terhadap Layanan dan Fasilitas Kampus Universitas Dengan Menggunakan Klasifikasi Support Vector Machine (SVM),” e-Proceeding Eng., vol. 6, no. 2, pp. 8682–8691, 2019.
[8] Parlindungan Ravelino, “Kajian Tingkat Kenyamanan dan Kemudahan Penggunaan Tangga di Fakultas Teknik Universitas Lancang Kuning,” J. Karya Ilm. Multidisiplin, vol. 2, no. 1, pp. 55–61, 2022, doi: 10.31849/jurkim.v2i1.9214.
[9] P. A. Permatasari, L. Linawati, and L. Jasa, “Survei Tentang Analisis Sentimen Pada Media Sosial,” Maj. Ilm. Teknol. Elektro, vol. 20, no. 2, p. 177, 2021, doi: 10.24843/mite.2021.v20i02.p01.