Analisis Sentimen Pengguna Sistem E-Kinerja Desa Kabupaten Jembrana Menggunakan Metode Naive Bayes

  • eka aditya Universitas Pendidikan Ganesha
  • I Gede Karya Astawa Universitas Pendidikan Ganesha
  • Kevin Gary Limbong Universitas Pendidikan Ganesha
  • Gede Indrawan Universitas Pendidikan Ganesha
  • Gede Indrawan Universitas Pendidikan Ganesha
  • Made Agus Oka Gunawan Universitas Tabanan

Abstract

The current era of globalization where information is needed very quickly. However, this is an obstacle, especially at the village level where the performance of the village, especially in rural areas, is difficult to obtain information, because it still uses manual bookkeeping. In response to this, the Jembarana Regency Government has improved its services using the website-based E-Kinerja application. Even though E-Kinerja has been used, it is important to know how user sentiment is when using the E-Kinerja system so that it can be used as an evaluation. This study aims to determine the sentiment of E-Kinerja users using the Naive Bayes method, Naive Bayes is a simple method and has high effectiveness in classification. The results of sentiment analysis with the Naive Bayes method get an accuracy of 66% precison 67% and recall 67% with a total of 88 datasets, an accuracy of 77% precison 69% and recall 69% in a total of 150 datasets.

Downloads

Download data is not yet available.

References

B. Liu, Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge university press, 2020.

S. Syafrizal, M. Afdal, and R. Novita, “Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor: Sentiment Analysis of PLN Mobile Application Review Using Naïve Bayes Classifier and K-Nearest Neighbor Algorithm,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 1, pp. 10–19, 2024.

M. Luo and X. Mu, “Entity sentiment analysis in the news: A case study based on negative sentiment smoothing model (nssm),” International Journal of Information Management Data Insights, vol. 2, no. 1, p. 100060, 2022.

R. Cahyanti, D. N. Maftuhah, A. B. Santoso, and I. Budi, “Twitter Sentiment Analysis Towards Candidates of the 2024 Indonesian Presidential Election,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 4, pp. 516–524, 2024.

S. Ahmad, A. M. Ridwan, and G. D. Setyawan, “Analisis Sentimen Product Tools & Home Menggunakan Metode Cnn Dan Lstm,” Teknokom, vol. 6, no. 2, pp. 133–140, 2023.

A. Yadav and D. K. Vishwakarma, “Sentiment analysis using deep learning architectures: a review,” Artif Intell Rev, vol. 53, no. 6, pp. 4335–4385, 2020.

Y. Durachman, S. J. Putra, H. Nanang, and H. T. Sukmana, “Analysis Sentiment of Public Opinion on Social Media Using Naïve Bayes and TF-IDF Algorithms,” in 2024 3rd International Conference on Creative Communication and Innovative Technology (ICCIT), IEEE, 2024, pp. 1–6.

M. Maaskri, S. A. M. Mostefaoui, M. H. Meghazi, and M. Goismi, “Multi-class Sentiment Analysis of COVID-19 Tweets by Machine Learning and Deep Learning Approaches,” Computación y Sistemas, vol. 28, no. 2, 2024.

K. R. F. Kusuma, “Analisis Sentimen Kuliah Online Menggunakan Metode Naïve Bayes Classifier Pada Google Form (Angket Mahasiswa),” Jurnal Ilmu Data, vol. 2, no. 10, 2022.

S. Ishak, Metodologi Penelitian Kesehatan. Bandung: Media Sains Indonesia, 2023.

Abd. A. Syam, G. Hardy M, A. Salim, D. F. Surianto, and M. Fajar B, “ANALISIS TEKNIK PREPROCESSING PADA SENTIMEN MASYARAKAT TERKAIT KONFLIK ISRAEL-PALESTINA MENGGUNAKAN SUPPORT VECTOR MACHINE,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1464–1472, Aug. 2024, doi: 10.29100/jipi.v9i3.5527.

M. I. A. Guno Wibowo and I. Pratama, “Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 4, pp. 715–722, Oct. 2024, doi: 10.47233/jteksis.v6i4.1552.

I. A. Fahrezi and N. A. Verdikha, “Analisis Sentimen Twitter Atas Isu Hak Angket Menggunakan Pembobotan TF-IDF dan Algoritma SVM,” Sci-tech Journal, vol. 3, no. 2, pp. 179–192, 2024.

C. H. Yutika, A. Adiwijaya, and S. Al Faraby, “Analisis Sentimen Berbasis Aspek pada Review Female Daily Menggunakan TF-IDF dan Naïve Bayes,” Jurnal Media Informatika Budidarma, vol. 5, no. 2, pp. 422–430, 2021.

C. C. Le, P. W. C. Prasad, A. Alsadoon, L. Pham, and A. Elchouemi, “Text classification: Naïve bayes classifier with sentiment Lexicon,” IAENG Int J Comput Sci, vol. 46, no. 2, pp. 141–148, 2019.

A. P. Husaini and A. Lisdiyanto, “Sistem Prediksi Penjualan Produk APD Terlaris di PT A3 Karunia Sidoarjo menggunakan Metode Naive Bayes,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 2, pp. 431–437, Apr. 2024, doi: 10.47233/jteksis.v6i2.1266.

N. Herlinawati, Y. Yuliani, S. Faizah, W. Gata, and S. Samudi, “Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine,” CESS (Journal Comput. Eng. Syst. Sci., vol. 5, no. 2, p. 293, 2020, doi: 10.24114/cess. v5i2. 18186, 2020.

R. N. Devita, H. W. Herwanto, and A. P. Wibawa, “Perbandingan kinerja metode naive bayes dan k-nearest neighbor untuk klasifikasi artikel berbahasa indonesia,” J. Teknol. Inf. dan Ilmu Komput, vol. 5, no. 4, 2018.

A. Tharwat, “Classification assessment methods,” Applied computing and informatics, vol. 17, no. 1, pp. 168–192, 2021.

E. Wibowo and I. Pratama, “Analisis Sentimen Terhadap Ulasan Hotel Melalui Platform Google Review Menggunakan Metode Stacking,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 4, pp. 774–784, Oct. 2024, doi: 10.47233/jteksis.v6i4.1475.

A. A. Khan, O. Chaudhari, and R. Chandra, “A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation,” Jun. 15, 2024, Elsevier Ltd. doi: 10.1016/j.eswa.2023.122778.

R. Rahmaddeni, M. K. Anam, Y. Irawan, S. Susanti, and M. Jamaris, “Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination,” ILKOM Jurnal Ilmiah, vol. 14, no. 1, pp. 32–38, Apr. 2022, doi: 10.33096/ilkom.v14i1.1090.32-38.

Published
2025-01-04
How to Cite
aditya, eka, Astawa, I. G. K., Limbong, K. G., Indrawan, G., Indrawan, G., & Gunawan, M. A. O. (2025). Analisis Sentimen Pengguna Sistem E-Kinerja Desa Kabupaten Jembrana Menggunakan Metode Naive Bayes. Jurnal Teknologi Dan Sistem Informasi Bisnis, 7(1), 8-14. https://doi.org/10.47233/jteksis.v7i1.1693
Section
Articles