Public Sentiment Analysis Against Identitas Kependudukan Digital Application Using Decision Trees
Main Article Content
Abstract
The Identitas Kependudukan Digital (IKD) application is an innovation in the government sector that is expected to facilitate public administration through electronic means. The implementation of the Identitas Kependudukan Digital application has sparked public debate regarding its application and security. This study aims to analyze public sentiment towards the Identitas Kependudukan Digital application using the Decision Tree algorithm and to present the performance of the Decision Tree algorithm in terms of positive and negative sentiment scores. The author collected review data from Google Play for the Identitas Kependudukan Digital application using web scraping techniques with Google Collaboration. Based on the analysis results, the Decision Tree algorithm predicted 240 negative and 4 positive data points out of 244 test data points. The testing and evaluation results using the Decision Tree algorithm yielded an accuracy of 89.34%, a precision of 90.00%, and a recall of 99.08%. According to the word frequency results in the word cloud, the words “Aplikasi” and “Data” had the highest word frequencies, representing the main contributors to the high percentage of negative sentiment in the analysis of public sentiment towards the Identitas Kependudukan Digital application.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
References
[2] CNNIndonesia, "Empty E-KTP Blanks, DKI Issues Identitas Kependudukan Digital," www.cnnindonesia.com. Accessed: Jan. 03, 2024. [Online]. Available: https://www.cnnindonesia.com/nasional/20221130114014-20- 880777/blangko-e-ktp- kosong-dki-issued-digital-identity-dependency
[3] M. O. Erwanti, "No More e-KTP Blanks, Government Promotes Digital ID Cards on Cellphones," news.detik.com. Accessed: Jan. 03, 2024. [Online]. Available: https://news.detik.com/berita/d-6562080/blangko-e-ktp-tak-ditambah-lagi- government-intensifies-digital-ID card-on-phone
[4] C. Cahyaningtyas, Y. Nataliani, and I. R. Widiasari, "Sentiment Analysis on Shopee Application Ratings Using the SMOTE-Based Decision Tree Method," Aiti, vol. 18, no. 2, pp. 173-184, 2021, doi: 10.24246/aiti.v18i2.173-184.
[5] R. A. Lestari, A. Erfina, and W. Jatmiko, "Application of Support Vector Machine Algorithm on Sentiment Analysis of Digital Population Identity," J. Technol. Inf. and Comput. Science, vol. 10, no. 5, pp. 1063-1070, 2023, doi: 10.25126/jtiik.20231057264.
[6] N. Wahyuningsih, "Comparison of Classification Methods in Analyzing Public Sentiment towards Identitas Kependudukan Digital (IKD)," vol. 8, no. 4, pp. 1218-1227, 2023, [Online]. Available: https://repository.uksw.edu/handle/123456789/30436%0Ahttps://repository.uksw.edu/bitstream/123456789/30436/8/T1_67 2019296_Daftar Pustaka.pdf
[7] I. Ahmad, S. Samsugi, and Y. Irawan, "Implementation of Data Mining as Data Processing," J. Teknoinfo, vol. 16, no. 1,
p. 46, 2022, [Online]. Available: http://portaldata.org/index.php/portaldata/article/view/107
[8] R. S. Wahono, Data Mining, vol. 2, no. January 2013. 2023. [Online]. Available: https://www.cambridge.org/core/product/identifier/CBO9781139058452A007/type/book_part
[9] Dispendukmagetan, "11 Facts of Identitas Kependudukan Digital Application," dispenduk.magetan.go.id. Accessed: Dec. 09, 2023. [Online]. Available: https://dispenduk.magetan.go.id/11-fakta-aplikasi-identitas-kependudukan-digital/
[10] A. T. Octa Nuryawan, M. Hasbullah, M. Rizal, M. F. Rajab, and N. Agustina, "Decision Tree Algorithm for Public Sentiment Analysis towards Marketplace in Indonesia," Naratif J. Nas. Research, Appl. and Tech. Inform., vol. 5, no. 1, pp. 18-25, 2023, doi: 10.53580/naratif.v5i1.186.
[11] G. K. Locarso, "Sentiment Analysis of Pedulilindungi Application Review on Google Play Store Using NBC," J. Tek. Inform. Kaputama, vol. 6, no. 2, pp. 353-361, 2022.
[12] A. M. Pravina, I. Cholissodin, and P. P. Adikara, "Sentiment Analysis of Airline Opinions on Twitter Documents Using Support Vector Machine (SVM) Algorithm," J. Pengembemb. Technol. Inf. and Comput. Science, vol. 3, no. 3, pp. 2789- 2797, 2019, [Online]. Available: http://j-ptiik.ub.ac.id
[13] A. Mukminin, "Analysis of Public Sentiment towards Covid-19 Swab-PCR Test Services in Indonesia Using the Support Vector Machine Algorithm," Univ. Din., 2021.
[14] R. Adinugroho, Comparison of Split Ratio of Training Data and Testing Data Using the LSTM Method in Predicting Price Index Shares Asia. 2022. [Online]. Available:
https://repository.uinjkt.ac.id/dspace/handle/123456789/67314%0Ahttps://repository.uinjkt.ac.id/dspace/bitstream/1234567 89/67314/1/RAHMADHAN ADINUGROHO-FST.pdf
[15] N. L. W. S. R. Ginantra, C. P. Yanti, G. D. Prasetya, I. B. G. Sarasvananda, and I. K. A. G. Wiguna, "Sentiment Analysis of Villa Reviews in Ubud Using Naive Bayes, Decision Tree, and K-NN Methods," J. Nas. Educ. Tech. Inform., vol. 11, no. 3, pp. 205-215, 2022, doi: 10.23887/janapati.v11i3.49450.