Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm

Winny purbaratri; Hindriyanto Dwi Purnomo; Danny Manongga; Iwan Setyawan; Hendry Hendry

doi:10.30812/matrik.v23i2.3272

Winny purbaratri Institut Keuangan-Perbankan dan Informatika Asia Perbanas, Jakarta, Indonesia
Hindriyanto Dwi Purnomo Universitas Kristen Satya Wacana University, Salatiga, Indonesia
Danny Manongga Universitas Kristen Satya Wacana University, Salatiga, Indonesia
Iwan Setyawan Universitas Kristen Satya Wacana University, Salatiga, Indonesia
Hendry Hendry Universitas Kristen Satya Wacana University, Salatiga, Indonesia

DOI: https://doi.org/10.30812/matrik.v23i2.3272

Keywords: Classfier, Google Playstore, Naïve Bayes, Opinion Mining, Sentiment Analysis

Abstract

A digital platform called the Alpukat Population Application is used to handle statistics and information regarding DKI Jakarta's population. Using the Naive Bayes Classifier (NBC) approach, sentiment analysis for applications using satellite placement. The Nave Bayes Classifier technique is utilized for sentiment analysis because of its benefits in modeling and categorizing complicated data. The user reviews and comments gathered from the Google Play Store were the source of the data utilized in this research. Feature extraction using methods like TF-IDF, sentiment labeling on data, and the development of Nave Bayes Classifier models for sentiment classification were all part of the research project. It is anticipated that the study's findings would help us better understand how users interact with the Alpukat population app. This sentiment analysis may assist app administrators and developers in identifying the positives and negatives of applications and planning updates and advancements based on user feedback. It is anticipated that the sentiment classification model created using the Naive Bayes Classifier approach would be able to classify user evaluations into positive, negative, or neutral sentiment categories with a high degree of accuracy. The creation of improved alpukat positioning apps and decision-making may both benefit from this emotive analysis.

Downloads

Download data is not yet available.

References

[1] I. Farida, R. Setiawan, A. S. Maryatmi, and N. Juwita, “The Implementation of E-Government in the Industrial Revolution Era 4.0 in Indonesia,” Int. J. Progress. Sci. Technol., vol. 22, no. 2, pp. 340–346, 2020, doi: http://dx.doi.org/10.52155/ijpsat.v22.2.2165.
[2] A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.
[3] G. Y. Iskandarli, “Applying Clustering and Topic Modeling to Automatic Analysis of Citizens’ Comments in EGovernment,” Int. J. Inf. Technol. Comput. Sci., vol. 12, no. 6, pp. 1–10, 2020, doi: 10.5815/ijitcs.2020.06.01.
[4] A. Akram, N. Risal, D. Rezky, and A. Sulaiman, “Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations,” vol. 5, no. 1, pp. 35–44, 2023, doi: https://doi.org/10.55151/ijeedu.v5i1.99.
[5] I. S. Jami and A. S. Zubair, “Semantic Web based E-Government System,” Indian J. Sci. Technol., vol. 11, no. 44, pp. 1–6, 2018, doi: 10.17485/ijst/2018/v11i44/132332.
[6] D. Joshi, M. Khalegaonkar, M. Lohikpure, P. Maan, and R. A. Deshmukh, “Sentimental Analysis on E-Governance,” Int. J. Innov. Res. Sci. Eng., vol. Vol.3, no. Issue 05, pp. 1–9, 2017, doi: http://ijirse.com/wp-content/upload/2017/03/PY2087ijirse.pdf.
[7] M. R. Fahlevvi, “Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi Dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia Di Google Playstore Menggunakan Metode Support Vector Machine,” J. Teknol. dan Komun. Pemerintah., vol. 4, no. 1, pp. 1–13, 2022, doi: 10.33701/jtkp.v4i1.2701.
[8] A. Filemon, H. Kaban, and N. Yudistira, “Analisis Sentimen Aplikasi E-Goverment berdasarkan Ulasan Pengguna menggunakan Metode Maximum Entropy dan Seleksi Fitur Mutual Information,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, pp. 1452–1458, 2021, doi: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/8877.
[9] M. K. Anam, B. N. Pikir, and M. B. Firdaus, “Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 139–150, 2021, doi: 10.30812/matrik.v21i1.1092.
[10] S. Abdelgaber, S. Abdel Gaber, and B. Kazim, “A Proposed Road Map To Enhance E-Government Services: Kuwait Case Study,” Researchgate.Net, no. December 2019, 2019, doi: https://www.researchgate.net/publication/339237461_A_Proposed_Road_Map_To_Enhance_E-Government_Services_Kuwait_Case_Study.
[11] R. I. Syah, Hoiriyah, and M. Walid, “Analisis Sentimen Pengguna Media Sosial Terhadap Aplikasi M-Health Peduli Lindungi dengan Metode Lexicon Based dan Naive Bayes,” vol. 3, no. 2, pp. 54–60, 2020, doi: http://dx.doi.org/10.21927/ijubi.v6i1.3275.
[12] T. Tinaliah and T. Elizabeth, “Analisis Sentimen Ulasan Aplikasi PrimaKu Menggunakan Metode Support Vector Machine,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 4, pp. 3436–3442, 2022, doi: 10.35957/jatisi.v9i4.3586.
[13] R. Apriani et al., “Analisis Sentimen dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia,” J. Rekayasa Teknol. Nusa Putra, vol. 6, no. 1, pp. 54–62, 2019.
[14] A. Faesal, A. Muslim, A. H. Ruger, and K. Kusrini, “Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means,” MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 2, pp. 207–213, 2020, doi: 10.30812/matrik.v19i2.640.
[15] I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.
[16] I. Di Estika, I. Darmawan, and O. N. Pratiwi, “Analisis Sentimen Ulasan Aplikasi Buka Lapak Untuk Peningkatan Layanan Menggunakan Algoritma Naive Bayes,” vol. 8, no. 5, pp. 4367–4376, 2021, doi: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/14676.
[17] S. Surohman, S. Aji, R. Rousyati, and F. F. Wati, “Analisa Sentimen Terhadap Review Fintech Dengan Metode Naive Bayes Classifier Dan K- Nearest Neighbor,” EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 93–105, 2020, doi: 10.31294/evolusi.v8i1.7535.
[18] A. Athallah Muhammad et al., “Analisis Sentimen Pengguna Aplikasi Dana Berdasarkan Ulasan Ada Google Play Menggunakan Metode Support Vector Machine,” pp. 194–204, 2022, doi: https://conference.upnvj.ac.id/index.php/senamika/article/view/2171/1660.
[19] I. Atoum, “A novel framework for measuring software quality-in-use based on semantic similarity and sentiment analysis of software reviews,” J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 1, pp. 113–125, 2020, doi: 10.1016/j.jksuci.2018.04.012.
[20] M. K. Khoirul Insan, U. Hayati, and O. Nurdiawan, “Analisis Sentimen Aplikasi Brimo Pada Ulasan Pengguna Di Google Play Menggunakan Algoritma Naive Bayes,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 478–483, 2023, doi: 10.36040/jati.v7i1.6373.