Faktor Pendorong, Penghambat, dan Rekomendasi Strategis Aplikasi Salaman Berbasis Data dan Analisis Digital
DOI:
https://doi.org/10.33701/jtkp.v7i1.4937Keywords:
Sentimen Analisis, Metode digital, Kepuasan pengguna, Public value.Abstract
Analisis sentimen menggunakan metode digital untuk menilai aplikasi Salaman dapat dijadikan umpan balik untuk perbaikan dan memperoleh solusi bagi pengambil kebijakan, ketika pendekatan manual-konvensional tidak mampu menggambarkan respon pengguna. Kajian bersifat eksploratif untuk mendapatkan faktor pendorong dan penghambat aplikasi salaman dengan menggunakan analisis sentimen berbasis lexicon-based dan menggunakan konsep yang berkaitan dengan digital governance untuk memperoleh rekomendasi strategis. Data diperoleh pada rentang waktu 22 Februari 2020-19 November 2024, dengan jumlah 717 ulasan di google playstore. Hasil analisis memperlihatkan mayoritas pengguna memberikan sentimen netral dengan persentase 76,4%, sentimen positif 16,7%, sentimen negatif 6,6% dan tidak terindetifikasi 0,3%. Dominasi sentimen netral perlu diwaspadai dan ditindaklanjuti agar tidak bergeser menjadi sentimen negatif. Hasil word cloud untuk faktor pendorong kesuksesan aplikasi salaman adalah pelayanan digital yang ‘mudah’, ‘membantu’, ‘good’ dan ‘cepat’. Faktor determinan hambatan aplikasi salaman bersifat teknis; error, otentikasi, validasi, registrasi, runtime dan server. Perlu intervensi penyempurnaan aplikasi salaman untuk meningkatkan kepercayaan, inklusivitas layanan, dan mencegah ketimpangan digital, memastikan bahwa aplikasi seperti Salaman berkontribusi secara signifikan terhadap implementasi kebijakan e-government di Kota Bandung.
Kata Kunci: Sentimen Analisis, Metode digital, Kepuasan pengguna, Public value.
Downloads
References
Alannsary, M. O. (2025). Evaluating Citizen Satisfaction with Saudi Arabia’s E-Government Services: A Standards-Based, Theory-Informed Approach. ArXiv. https://doi.org/https://doi.org/10.48550/arXiv.2508.17912
Amirkhalili, Y., & Wong, H. Y. (2025). Banking on Feedback: Text Analysis of Mobile Banking iOS and Google App Reviews. ArXiv. https://doi.org/https://doi.org/10.48550/arXiv.2503.11861
Baser, E., & Evrim, T. (2023). Citizen expectations, agency reputation and public service quality. Public Management Review, https://doi.org/10.1080/14719037.2023.2245842. https://doi.org/https://doi.org/10.1080/14719037.2023.2245842
Binsar, Bayu, M., Abbas, I., Budiman, A., & Abidah. (2025). Analysis of Digital Content of Indonesian Society in the Public Service Sector. In A. Naga, D. S. Jat, D. Mishr, & A. Joshi (Eds.), Intelligent Sustainable Systems (pp. 87–97). Springer. https://doi.org/DOI: 10.1007/978-981-97-9324-2_8
Bonta, V., Kumaresh, N., & Janardhan, N. (2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology, 8(2), 1–6. https://doi.org/DOI: https://doi.org/10.51983/ajcst-2019.8.S2.2037
Bouaziz, M., & Ohtsuki, T. (2016). Sentiment analysis: From binary to multi-class classification: A pattern-based approach for multi-class sentiment analysis in Twitter. IEEE International Conference on Communications (ICC) 2106, 1–6. https://doi.org/DOI: 10.1109/ICC.2016.7511392
Chakravorti, B., Chaturvedi, R. S., & Troein, C. (2017). Building Smart Societies. The Fletcher School, Tufts University.
Chan, B., Purwanto, E., & Hendratono, T. (2020). Social Media Marketing, Perceived Service Quality, Consumer Trust and Online Purchase Intentions. TRKU, Technology Report Kansai University, 62(10), 6265–6272.
Chan, F. K. Y., Thong, J. Y. L., Brown, S. A., & Venkatesh, V. (2020). Service Design and Citizen Satisfaction with E-Government Services: A Multidimensional Perspective. Public Administration Review, 81(5), 874–894. https://doi.org/https://doi.org/10.1111/puar.13308
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/https://doi.org/10.2307/249008
Eberendu, A. C. (2016). Unstructured Data: an overview of the data ofBig Data. International Journal of Computer Trends and Technology (IJCTT), 38(1), 46–50.
Engdaw, B. (2019). The Impact of Quality Public Service Delivery on Customer Satisfaction in Bahir Dar City Administration: The Case of Ginbot 20 Sub-city. International Journal of Public Administration, 43(7), 644–654. https://doi.org/http://dx.doi.org/10.1080/01900692.2019.1644520
Fansuri, E. Q., & Hidayah, E. S. (2021). Pemanfaatan Aplikasi E-PUNTEN Dalam Pembuatan Surat Keterangan Tinggal Sementara (SKTS) di Kota Bandung. JTKP, 3(1), 17–35. https://doi.org/https://doi.org/10.33701/jtkp.v3i1.2123
Harwenda, R. W., Angelo, M. D., Budi, I., Santoso, A. B., & Putra, P. K. (2025). Sentiment Analysis on Government Public Policies: A Systematic Literature Review. Dinasti International Journal of Education Management And Social Science, 6(5), 4192–4211. https://doi.org/https://doi.org/10.38035/dijemss.v6i5.4699
Hutchinson, J. (2016). An introduction to digital media research methods: how to research and the implications of new media data. Communication Research and Practice, 2(1), 1–6. https://doi.org/https://doi.org/10.1080/22041451.2016.1155307
IBM. (2024). Data terstruktur vs Data tidak terstruktur. retrieved 18/11/2024.
Iksan, M. (2019). Pendekatan Technology Acceptance Model (TAM) Dalam Menganalisis Minat Perilaku Penggunaan E-Money Pada Mahasiswa UIN Sunan Gunung Djati Bandung. JTKP, 1, 32–41. https://doi.org/https://doi.org/10.33701/jtkp.v1i1.717
Ismail, A. R., & Hakim, R. B. F. (2023). Implementasi Lexicon Based Untuk Analisis Sentimen Dalam Mengetahui Trend Wisata Pantai Di DI Yogyakarta Berdasarkan Data Twitter. Emerging Statistics and Data Science Journal, 1(1), 37–46.
Kaur, C., & Sharma, A. (2020). Social Issues Sentiment Analysis using Python. Conference: 2020 5th International Conference on Computing, Communication and Security (ICCCS). https://www.researchgate.net/publication/346962692_Social_Issues_Sentiment_Analysis_using_Python
Kumar, A., Beri, T., & Sobti, T. (2020). A Survey of Sentiment Analysis and Opinion Mining. In Emerging Technologies in Data Mining and Information Security. https://doi.org/DOI: 10.1007/978-981-33-4367-2_39
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers. https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf
Meijer, A., & Bolívar, M. P. R. (2016). Governing the Smart City: A Review of the Literature on Smart Urban Governance. International Review of Administrative Sciences, 82(2), 392–408. https://doi.org/http://dx.doi.org/10.1177/0020852314564308
Mills, K. A. (2019). Big data for qualitative research. Routledge, Taylor & Francis Group. https://doi.org/10.4324/9780429056413
Muhammad, W., Mushtaq, M., Junejo, K. N., & Khan, M. Y. (2020). Sentiment Analysis of Product Reviews in The Absence of Labelled Data Using Supervised Learning Approaches. Learning Approaches., 33(2), 118–132. https://doi.org/DOI: https://doi.org/10.22452/mjcs.vol33no2.3
Norman, D. A. (2004). Emotional Design: Why We Love (or Hate) Everyday Things. Basic Books,.
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Jounal Ofmarketing Research. https://doi.org/https://doi.org/10.1177/0022243780017004
Parasuraman, A., & Zeithaml, V. (1988). SERVQUAL: A Multiple-Item Scale for Measuring Consumer Perceptions of Service Quality. 64(1), 12–40.
Pınarbaşı, F. (2024). Mapping the Online Reviews Sentiment Landscape: An Exploration of Emotion Spectrum in User Reviews of Mobile Apps. 14(3), 1598–1619. https://doi.org/https://doi.org/10.30783/nevsosbilen.1508802
Poria, S., Majumder, N., Hazarika, D., Ghosal, D., Bhardwaj, R., Jian, S. Y. B., Hong, P., Ghosh, R., Roy, A., Chhaya, N., Gelbukh, A., & Mihalcea, R. (2021). Recognizing Emotion Cause in Conversations. Cognitive Computation, 13, 1317–1332. https://doi.org/https://doi.org/10.1007/s12559-021-09925-7
Rogers, R. (2019). Doing Digital Methods (1st editio). SAGE Publication.
Ryzin, G. G. Van. (2015). Service Quality, Administrative Process, and Citizens’ Evaluation of Local Government in the US. Public Management Review, 17(3), 425–442. https://doi.org/https://doi.org/10.1080/14719037.2013.841456
Setyono, G. (2020). Penerapan Sistem Antrian Pelayanan (SIAP). JTKP, 2(1), 28–42. https://doi.org/https://doi.org/10.33701/jtkp.v2i1.2298
Shakir, A., & Arora, J. (2018). Accuracy in Binary, Ternary and Multi-class classification sentimental analysis -A Survey. International Journal of Advanced Research in Computer Science, 9(2), 524–526. https://doi.org/DOI: http://dx.doi.org/10.26483/ijarcs.v9i2.5866
Simonofski, A., Fink, J., & Burnay, C. (2021). Supporting policy-making with social media and e-participation platforms data: A policy analytics framework. Government Information Quarterly, 38(3). https://doi.org/https://doi.org/10.1016/j.giq.2021.101590
Singh, P., Dwivedi, Y. K., Kahlon, K. S., Sawhney, R. S., Alalwan, A. A., & Rana, N. P. (2020). Smart Monitoring and Controlling of Government Policies Using Social Media and Cloud Computing. Information Systems Frontiers, 22, 315–337. https://doi.org/https://doi.org/10.1007/s10796-019-09916-y
Son, J., Koo, S. H., Koo, L. H., & On-Ook, C. (2022). Are Neutral Sentiments Worth Considering When Investigating Online Consumer Reviews? Their Relationship with Review Ratings. Conference: 55th Hawaii International Conference on System Sciences. https://doi.org/DOI: 10.24251/HICSS.2022.565
Stanford. (2020). Scientific Research and Big Data. In Stanford Encyclopedia of Philosophy (p. retrieved 19/11/2024). Stanford University.
T.K., B., Annavarapu, C. S. R., & Bablani, A. (2021). Machine learning algorithms for social media analysis: A survey. Computer Science Review, 40(100395). https://doi.org/https://doi.org/10.1016/j.cosrev.2021.100395
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Association for Computational Linguistic, 37(2), 267–3027. https://doi.org/DOI: 10.1162/COLI_a_00049
Twist, A. van, Ruijer, E., & Meijer, A. (2023). Smart cities & citizen discontent: A systematic review of the literature. Government Information Quarterly, 40(2), 101799. https://doi.org/https://doi.org/10.1016/j.giq.2022.101799
Verma, S. (2022). Sentiment analysis of public services for smart society: Literature review and future research directions. Government Information Quarterly, 39(3), 101708. https://doi.org/https://doi.org/10.1016/j.giq.2022.101708
Vryniotis, V. (2013). The importance of Neutral Class in Sentiment Analysis. DatumBox. https://blog.datumbox.com/the-importance-of-neutral-class-in-sentiment-analysis/
WPLongform. (2024). , Digital vs Traditional Research Methods; Booming Options are a Boon for Researchers.
Xu, N., & Zhang, W. (2025). User Satisfaction with Chinese Government Apps: Topic Mining and Sentiment Analysis of User Reviews. Lex Localis, 23(3). https://doi.org/https://doi.org/10.52152/23.3.95-124(2025)
Ye, X., Su, X., Yao, H., Dong, L., Ling, Q., & Yu, S. (2023). How Do Citizens View Digital Government Services? Study on Digital Government Service Quality Based on Citizen Feedback. Mathematics, 11(14), 3122. https://doi.org/https://doi.org/10.3390/math11143122
Yuliantini, L. S., & Sukarno, M. (2023). The Twitter Sentiment Analysis of Public Service Innovation: Pandawa BPJS Health Service. Proceedings The 4 Th UMY Grace 2023 (Universitas Muhammadiyah Yogyakarta Undergraduate Conference), 146–153.
Yuniar, E., Safiroh, D., & Wahyuningsih, D. (2022). Implementasi Scraping Data Untuk SentimentAnalysis PenggunaDompet Digital Dengan Menggunakan Algoritma MachineLearning. Jurnal Janitra Informatika Dan Sistem Informasi, 2(1), 35–42. https://doi.org/DOI: 10.25008/janitra.v2i1.145
Zhang, J., Wolfram, D., & Ma, F. (2023). The impact of big data on research methods in information science. Data and Information Management, 7(2). https://doi.org/https://doi.org/10.1016/j.dim.2023.100038
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Ella Lesmanawaty' Wargadinata, Arief Syamsudin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
