PEMETAAN ASPEK LAYANAN SENTUH TANAHKU MELALUI ANALISIS SENTIMEN LEKSIKON DAN MACHINE LEARNING
Main Article Content
Abstract
Sentuh Tanahku is one of the digital land information service instruments initiated by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency in 2017. Since its launch, the application has been downloaded more than one million times and has received approximately 47,000 user ratings on the Google Play Store. This study examined 19,927 Google Play Store reviews from September 21, 2017 to May 13, 2026 to identify sentiment patterns, classification model performance, and the service aspects that most frequently influenced user evaluations. The analysis involved text preprocessing, rating-based labeling, sentiment lexicon analysis, TF-IDF representation, Multinomial Naive Bayes, Linear Support Vector Machine, and service-aspect mapping. The results showed a polarized rating pattern. Five-star reviews accounted for 57.93%, while one-star reviews reached 23.97%, with an average rating of 3.70. Lexicon analysis identified 9,605 positive reviews, 6,537 negative reviews, and 3,785 neutral reviews. Linear SVM produced the best macro F1-score of 62.29%. The most prominent complaints concerned account authentication, application performance, and the connectivity between land certificates and parcel data. These findings indicate that the quality of land service applications depends on technical stability, clear verification flows, and service data readiness.
Article Details
How to Cite
Kusmiarto, & Wahyuni , W. (2026). PEMETAAN ASPEK LAYANAN SENTUH TANAHKU MELALUI ANALISIS SENTIMEN LEKSIKON DAN MACHINE LEARNING. KHARISMA Tech, 21(2), 200-214. https://doi.org/10.55645/kharismatech.v21i2.735
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References
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[2] Google Play, “Sentuh Tanahku,” 2026. [Online]. Available: https://play.google.com/store/apps/details?id=id.go.bpn.sentuh. Accessed: May 13, 2026.
[3] W. Winarto, I. Alwiah Musdar, and H. Hasniati, “Sentiment analysis of 2024 presidential candidate using the Support Vector Machine algorithm on Twitter,” KHARISMA Tech, vol. 19, no. 1, pp. 86-98, 2024.
[4] K. E. Hadiputra, B. Zaman, and S. Bahri, “Analysis of service quality of Beli.in application using the PIECES framework method,” KHARISMA Tech, vol. 19, no. 2, pp. 58-71, 2024, doi: 10.55645/kharismatech.v19i2.474.
[5] F. A. Tejokusuma, H. Angriani, and Afifah, “Analisis tingkat kepuasan pengguna terhadap aplikasi TIERRA menggunakan metode PIECES Framework,” KHARISMA Tech, vol. 17, no. 2, pp. 157-171, 2022, doi: 10.55645/kharismatech.v17i2.312.
[6] C. V. Wu, Hasniati, and I. Alwiah Musdar, “Implementation of User Centered Design approach in User Interface design and User Experience website worker’s,” KHARISMA Tech, vol. 17, no. 2, pp. 71-84, 2022, doi: 10.55645/kharismatech.v17i2.246.
[7] C. Crystanto, A. Munir S., and H. Surasa, “Analisis kepuasan pengguna aplikasi MyTelkomsel menggunakan PIECES Framework,” KHARISMA Tech, vol. 19, no. 1, pp. 26-38, 2024, doi: 10.55645/kharismatech.v19i1.453.
[8] C. Sentosa, Sudirman, and Afifah, “Analisis kepuasan pengguna terhadap website Kharisma Classroom menggunakan metode PIECES,” KHARISMA Tech, vol. 19, no. 2, pp. 98-112, 2024, doi: 10.55645/kharismatech.v19i2.409.
[9] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” in Proc. EMNLP, 2002, pp. 79-86.
[10] B. Liu, Sentiment Analysis and Opinion Mining. San Rafael, CA: Morgan & Claypool Publishers, 2012.
[11] M. Thelwall, K. Buckley, and G. Paltoglou, “Sentiment strength detection for the social web,” J. Am. Soc. Inf. Sci. Technol., vol. 63, no. 1, pp. 163-173, 2012, doi: 10.1002/asi.21662.
[12] A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of sentiment reviews using n-gram machine learning approach,” Expert Syst. Appl., vol. 57, pp. 117-126, 2016, doi: 10.1016/j.eswa.2016.03.028.
[13] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171-4186.
[14] B. Wilie dkk., “IndoNLU: Benchmark and resources for evaluating Indonesian natural language understanding,” in Proc. AACL-IJCNLP, 2020, pp. 843-857.
[15] F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP,” in Proc. COLING, 2020, pp. 757-770.
[16] U. Khaira, R. Johanda, P. E. P. Utomo, and T. Suratno, “Sentiment Analysis of Cyberbullying on Twitter Using SentiStrength,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 3, no. 1, pp. 21-27, 2020.
[17] H. Jayadianti, W. Kaswidjanti, A. T. Utomo, S. Saifullah, F. R. Arifin, and K. Kusrini, “Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN,” ILKOM Jurnal Ilmiah, vol. 14, no. 3, pp. 348-354, 2022, doi: 10.33096/ilkom.v14i3.1505.348-354.
[18] F. Pedregosa dkk., “Scikit-learn: Machine learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011.
[19] M. A. Shareef, Y. K. Dwivedi, N. P. Rana, and R. Raman, “SQ mGov: A comprehensive service-quality paradigm for mobile government,” Information Systems Management, vol. 31, no. 2, pp. 126-142, 2014, doi: 10.1080/10580530.2014.890432.
[20] A. J. Desmal, “Exploring the information quality of mobile government services,” PeerJ Computer Science, vol. 8, e1028, 2022, doi: 10.7717/peerj-cs.1028.
[21] Z. Mao, Q. Zou, T. Bu, Y. Dong, and R. Yan, “Understanding the role of service quality of government APPs in continuance intention: An expectation-confirmation perspective,” SAGE Open, vol. 13, no. 4, 2023, doi: 10.1177/21582440231201218.
[22] N. Xu and W. Zhang, “User satisfaction with Chinese government apps: Topic mining and sentiment analysis of user reviews,” Lex Localis - Journal of Local Self-Government, pp. 95-124, 2025, doi: 10.52152/23.3.95-124(2025).
[23] T. Liu, C. Wang, K. Huang, P. Liang, B. Zhang, M. Daneva, and M. van Sinderen, “ROSEMATCHER: Identifying the impact of user reviews on app updates,” Information and Software Technology, vol. 161, 107261, 2023, doi: 10.1016/j.infsof.2023.107261.
[24] D. Pagano and W. Maalej, “User feedback in the app store: An empirical study,” in Proc. 21st IEEE International Requirements Engineering Conference, 2013, pp. 125-134, doi: 10.1109/RE.2013.6636712.
[25] E. Guzman and W. Maalej, “How do users like this feature? A fine grained sentiment analysis of app reviews,” in Proc. 22nd IEEE International Requirements Engineering Conference, 2014, pp. 153-162, doi: 10.1109/RE.2014.6912257.
[26] I. Williamson, S. Enemark, J. Wallace, and A. Rajabifard, Land Administration for Sustainable Development. Redlands, CA: ESRI Press Academic, 2010.
[27] S. Enemark, R. McLaren, and C. Lemmen, Fit-for-Purpose Land Administration: Guiding Principles for Country Implementation. Nairobi: UN-Habitat/GLTN, 2016.
[28] Z. Zeng, S. Li, J. W. Lian, J. Li, T. Chen, and Y. Li, “Switching behavior in the adoption of a land information system in China: A perspective of the push-pull-mooring framework,” Land Use Policy, vol. 109, 105629, 2021, doi: 10.1016/j.landusepol.2021.105629.