Advancing Urban Governance In The Digital Era: From Classical Statistics To Lstm Deep Learning For Parking Revenue Prediction

Hardika Khusnuliawati; Dewa Bintang Nugroho; Moch Arifin Fahri; Fahrul Zainnun Mas'ud

Authors

Hardika Khusnuliawati Universitas Sahid Surakarta,Surakarta, Indonesia
Dewa Bintang Nugroho Universitas Sahid Surakarta,Surakarta, Indonesia
Moch Arifin Fahri Universitas Sahid Surakarta,Surakarta, Indonesia
Fahrul Zainnun Mas'ud Universitas Sahid Surakarta,Surakarta, Indonesia

Keywords:

digital urban governance, parking revenue prediction, classical statistics, LSTM deep learning

Abstract

Regional autonomy in Indonesia has increased the responsibility of local governments to enhance public welfare through optimized local revenue. Parking retribution, particularly from on-street parking, serves as an important source of income for Karanganyar Regency, a densely populated and tourism-oriented area with high potential. However, unregulated parking, inconsistent fee collection, and reliance on manual forecasting have resulted in suboptimal revenue outcomes. Existing methods remain limited, depending on simple calculations or linear projections without utilizing modern computational tools. This study compares classical statistical forecasting and Long Short-Term Memory (LSTM) deep learning models to predict parking revenue in Karanganyar Regency. The results show that classical methods capture general trends, while LSTM provides higher accuracy and robustness in handling nonlinear and temporal data. The study highlights the importance of advanced machine learning as a digital governance instrument to improve transparency, optimize revenue management, and support data-driven urban policymaking.

References

Amelia, C. I. (2024). Pengaruh Rasionalisasi Retribusi Jasa Umum Dalam Undang-Undang Nomor 1 Tahun 2022 Terhadap Penerimaan Asli Daerah (Studi Pada Pemerintah Kota Yogyakarta). Wicarana, 3(2), 61–74. https://doi.org/10.57123/wicarana.v3i2.78

Cecaj, A., Lippi, M., Mamei, M., & Zambonelli, F. (2020). Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data. Applied Sciences (Switzerland), 10(18). https://doi.org/10.3390/APP10186580

Jin, B., Zhao, Y., & Ni, J. (2022). Sustainable Transport in a Smart City: Prediction of Short-Term Parking Space through Improvement of LSTM Algorithm. Applied Sciences (Switzerland), 12(21). https://doi.org/10.3390/app122111046

Koçak, H. (2024). Time Series Prediction of Temperature Using Seasonal ARIMA and LSTM Models. Gazi Journal of Engineering Sciences, 9(3), 574–584. https://doi.org/10.30855/gmbd.0705088

Mega Christia, A., & Ispriyarso, B. (2019). Desentralisasi Fiskal dan Otonomi di Indonesia. Law Reform, 15(1), 150–165.

Mynhoff, P., Mocanu, E., Innovative, M. G.-8th I. P., & 2018, undefined. (2018). Statistical Learning versus Deep Learning: Performance Comparison for Building Energy Prediction Methods. Researchgate.Net, September. https://www.researchgate.net/profile/Elena_Mocanu/publication/327605408_Statistical_Learning_versus_Deep_Learning_Performance_Comparison_for_Building_Energy_Prediction_Methods/links/5b9956f5458515310583df6d/Statistical-Learning-versus-Deep-Learning-Perfor

Nugraha, W., Sabaruddin, R., & Murni, S. (2024). Teknik Scaling Menggunakan Robust Scaler Untuk Mengatasi Outlier Data Pada Model Prediksi Serangan Jantung. Techno.Com, 23(2), 319–327. https://doi.org/10.62411/tc.v23i2.10463

Sangeetha, J. M., & Alfia, K. J. (2024). Financial stock market forecast using evaluated linear regression based machine learning technique. Measurement: Sensors, 31(December 2023), 100950. https://doi.org/10.1016/j.measen.2023.100950

Tatachar, A. V. (2021). Comparative Assessment of Regression Models Based On Model Evaluation Metrics. International Research Journal of Engineering and Technology, 8(9), 853–860. www.irjet.net

Taurisa, D. (2020). Pajak Dan Retribusi Daerah Sebagai Penopang Otonomi Daerah Dilema Terhadap Kepastian Hukum Bagi Iklim Usaha. University Of Bengkulu Law Journal, 5(2), 89–105. https://doi.org/10.33369/ubelaj.5.2.89-105

Utama, J. Y., Wira, W., Yogi, Y., Harin, A., & Qurrota, A. (2024). Adaptation of Village Data Center ( DDC ) Application Technology in the Distribution of Special Financial Assistance ( BKK ) of the East Java Provincial Government to Bumdes. INTERNATIONAL CONFERENCE OF HUMANITIES AND SOCIAL SCIENCE (ICHSS), Ddc, 1144–1151.

Yang, S., Ma, W., Pi, X., & Qian, S. (2019). A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources. Transportation Research Part C: Emerging Technologies, 107, 248–265. https://doi.org/10.1016/j.trc.2019.08.010

Yonan, J. F. (2023). Improving Financial Forecasting Accuracy with Artificial Intelligence (AI) Models. Babylonian Journal of Artificial Intelligence, 2023, 74–82. https://doi.org/10.58496/bjai/2023/011

Zhang, F., Liu, Y., Feng, N., Yang, C., Zhai, J., Zhang, S., He, B., Lin, J., Zhang, X., & Du, X. (2022). Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction. IEEE Transactions on Knowledge and Data Engineering, 34(12), 5896–5909. https://doi.org/10.1109/TKDE.2021.3070202