Implementasi Algoritma Random Forest untuk Prediksi Volume Pengunjung pada Sektor Layanan Publik (Studi Kasus Unit Layanan Publik di Jawa Barat Tahun 2021-2022)
DOI:
https://doi.org/10.30606/rjti.v4i3.4042Keywords:
Random Forest, Peramalan Deret Waktu, Fitur Kalender, Layanan Publik, Volume PengunjungAbstract
Peramalan volume pengunjung harian yang akurat merupakan kunci untuk penjadwalan petugas yang andal, antrean yang singkat, dan pengalaman warga yang lebih baik pada layanan publik. Penelitian ini mengajukan pendekatan praktis dan mudah direplikasi yang hanya memanfaatkan sinyal kalender untuk meramalkan kedatangan harian menggunakan Random Forest. Dataset harian unit pelayanan publik tahun 2021 hingga 2022 berisi 730 observasi dan dibagi secara kronologis menjadi 80 persen pelatihan dan 20 persen pengujian agar menyerupai penerapan nyata serta menghindari kebocoran informasi. Prediktor mencakup hari, bulan, hari dalam minggu, indikator akhir pekan, minggu dalam bulan, serta penanda awal dan akhir bulan. Model menggunakan 200 pohon dengan max_depth = 8. Kinerja dievaluasi menggunakan MAE, MAPE, RMSE, dan R² serta dibandingkan dengan dua baseline yang kuat, yaitu naive lag-1 dan rata-rata hari dalam minggu. Pada data uji, model mencapai R² = 0.873, RMSE = 16.328, MAE = 13.103, dan MAPE sekitar 5,00 persen, melampaui kedua baseline. Secara kuantitatif, model ini menurunkan kesalahan prediksi (MAPE) masing-masing sekitar 64% dan 67% dibandingkan baseline naïve lag-1 dan rata-rata hari dalam minggu, menegaskan keunggulan empirisnya. Analisis feature importance menempatkan hari dalam minggu dan akhir pekan sebagai variabel paling berpengaruh dan sejalan dengan intuisi operasional. Temuan ini menunjukkan bahwa peramalan berbiaya rendah dan hemat data mampu memberikan akurasi yang siap pakai untuk penjadwalan, pemantauan risiko antrean, dan alokasi kapasitas, serta mudah ditingkatkan dengan konteks tambahan seperti hari libur dan cuaca.
Downloads
References
United Nations, “UN E-Government Survey 2024: Accelerating Digital Transformation for Sustainable Development, with the addendum on Artificial Intelligence,†New York, 2024.
Organisation for Economic Co-operation and Development (OECD), “2023 OECD Digital Government Index: Results and key findings,†OECD Public Governance Policy Papers, No. 44, OECD Publishing, Paris, 2024.
Organisation for Economic Co-operation and Development (OECD), “Governing with Artificial Intelligence: The State of Play and Way Forward in Core Government Functions,†OECD Publishing, Paris, 2025.
R. Madan and M. Ashok, “AI adoption and diffusion in public administration: A systematic literature review and future research agenda,†Government Information Quarterly, vol. 40, no.1, 2023.
F. Selten and B. Klievink, “Organizing public sector AI adoption: Navigating between separation and integration,†Government Information Quarterly, vol. 41, no. 1, 2024.
T. Haesevoets, B. Verschuere, and A. Roets, “AI adoption in public administration: Perspectives of public sector managers and public sector non-managerial employees,†Government Information Quarterly, vol. 42, no. 2, 2025.
E. Fatmawati, “Artificial Intelligence In Public Administration: Governance, Ethics, And Decision-Making,†JV, vol. 17, no. 2, pp. 37-48, Aug. 2025.
M. Mitzenmacher and R. Shahout, “Queueing, Predictions, and Large Language Models: Challenges and Open Problems,†Stochastic Systems, vol. 15, no. 3, pp. 195-219, 2025, doi: 10.1287/stsy.2025.0106.
F. R. di Torrepadula, E. V Napolitano, S. Di Martino, and N. Mazzocca, “Machine Learning for public transportation demand prediction: A Systematic Literature Review,†Engineering Applications of Artificial Intelligence, vol. 137, 2024, doi: 10.1016/j.engappai.2024.109166.
R. P. Munggaran, M. Nurmalasari, H. Hosizah, and D. Krismawati, “Prediksi Waktu Tunggu Pelayanan Pasien Rawat Jalan dengan Algoritma Random Forest: Predicting Outpatient Service Waiting Times with Random Forest Algorithmâ€, MALCOM, vol. 5, no. 1, pp. 35-40, Nov. 2024.
S. N. Windrasari, H. Margono, and Y. A. N. S. Putra, “Predictive Sales Analysis in Coffee Shops Using the Random Forest Algorithm,â€, MALCOM, vol. 5, no. 3, pp. 1000-1011, Jul. 2025.
M. S. Zeb, M. A. Khan, M. M. H. Khattak, S. Ud-Din, M. F. Habib, and M. Z. Khan, “Forecasting public transit ridership amidst COVID-19: a machine learning approach,†Public Transport, vol. 17, pp. 391–420, 2024, doi: 10.1007/s12469-024-00368-5.
Å. Jevinger, C. Zhao, J. A. Persson, and P. Davidsson, “Artificial intelligence for improving public transport: a mapping study,†Public Transport, vol. 16, pp. 99–158, 2024, doi: 10.1007/s12469-023-00334-7.
H. Almukhalfi, A. Noor, and T. H. Noor “Traffic management approaches using machine learning and deep learning techniques: A survey,†Engineering Applications of Artificial Intelligence, vol. 133, 2024, doi: 10.1016/j.engappai.2024.108147.
B. M. Porto and F. S. Fogliatto, “Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning,†BMC Med Inform Decis Mak, vol. 24, no. 377, 2024, doi: 10.1186/s12911-024-02788-6.
C. Brossard, C. Goetz, P. Catoire, L. Cipolat, C. Guyeux, C. G. Jardine, M. Akplogan, and L. A. Vuillaume, “Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study,†BMC Emerg Med, vol. 25, no. 3, 2025, doi: 10.1186/s12873-024-01141-4.
L. Zhou, Q. Zhu, Q. Chen, P. Wang, and H. Huang, “Predicting hospital outpatient volume using XGBoost: a machine learning approach,†Scientific reports, vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-01265-y.
C. Deina, F. S. Fogliatto, G. J. C. da Silveira, and M. J. Anzanello, “Decision analysis framework for predicting no-shows to appointments using machine learning algorithms,†BMC Health Serv Res, vol. 24, no. 37, 2024, doi: 10.1186/s12913-023-10418-6.
T. Oikonomidi, G. Norman, L. McGarrigle, J. Stokes, S. N. van der Veer, and D. Dowding, “Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review,†Journal of the American Medical Informatics Association : JAMIA, vol. 30, no.3, pp. 559–569, 2023, doi: 10.1093/jamia/ocac242.
S. Ryu, S. Jung, G. Kim, and S. Lee, “Visitor Number Prediction for Daegwallyeong Forest Trail Using Machine Learning,†Sustainability, vol. 17, no. 13, 2025, doi: 10.3390/su17136061.
M. Lu and Q. Xie, “A Novel Approach for Spatially Controllable High-Frequency Forecasts of Park Visitation Integrating Attention-Based Deep Learning Methods and Location-Based Services,†ISPRS International Journal of Geo-Information, vol. 12, no.3, 2023, doi: 10.3390/ijgi12030098.
Lukoseviciute G, Galanakis P, MartÃn-Nieto C, Wells G. K. Madden, G. Lukoseviciute, E. Ramsey, T. Panagopoulos, and J. Condell, “Forecasting daily foot traffic in recreational trails using machine learning.†Journal of Outdoor Recreation and Tourism, 2023 doi: 10.1016/j.jort.2023.100701 ;43:100701.
S. Uddin and H. Lu, “Confirming the statistically significant superiority of tree-based machine learning algorithms over their counterparts for tabular data,†PLoS One, 2024, doi: 10.1371/journal.pone.0301541.
L. Grinsztajn, E. Oyallon, G. Varoquaux, “Why do tree-based models still outperform deep learning on tabular data?,†arXiv, 2022, doi: 10.48550/arXiv.2207.08815.
Z. Sadeghi, R. Alizadehsani et al., “A review of Explainable Artificial Intelligence in healthcare,†Computers and Electrical Engineering, vol. 118, Aug. 2024, doi: 10.1016/j.compeleceng.2024.109370.
H. Shiraishi, T. Nakamura, and R. Shibuki, “Time series quantile regression using random forests,†J Time Ser Anal, vol. 45, no. 4, pp. 639–659, 2024, doi: 10.1111/jtsa.12731.
R. J. Hyndman and G. Athanasopoulos, “Forecasting: Principles and Practice (3rd ed.),†3rd ed. Melbourne: OTexts, 2021.
A. D. Brabandere, T. O. D. Beéck, K. Hendrickx, W. Meert, and J. Davis, “TSFuse: automated feature construction for multiple time series data,†Mach Learn vol. 113, pp. 5001–5056, 2024, doi: 10.1007/s10994-021-06096-2.
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “M5 accuracy competition: Results, findings, and conclusions,†International Journal of Forecasting, vol. 38, no. 4, pp. 1346-1364, 2022, doi: 10.1016/j.ijforecast.2021.11.013.
A. Alroomi, G. Karamatzanis, K. Nikolopoulos, A. Tilba, and S. Xiao, “Fathoming empirical forecasting competitions’ winners,†International Journal of Forecasting, vol, 38, no. 4, pp. 1519-1525, 2022, doi: 10.1016/j.ijforecast.2022.03.010.
F. Zito, V. Cutello, and M. Pavone, “Data-driven forecasting and its role in enhanced decision-making,†Engineering Applications of Artificial Intelligence, vol. 154, 2025, doi: 10.1016/j.engappai.2025.110934.
Tommaso Proietti, Diego J. Pedregal, “Seasonality in High Frequency Time Series,†Econometrics and Statistics, vol. 27, pp. 62-82, 2023, doi: 10.1016/j.ecosta.2022.02.001.
S. B. Chudo, and G. Terdik, “Modeling and Forecasting Time-Series Data with Multiple Seasonal Periods Using Periodograms,†Econometrics, vol. 13, no. 2, 2025, doi: 10.3390/econometrics13020014
F. N. Farida, A. Faqih, and S. E. Permana, “Penerapan Model Prediksi Penjualan pada Usaha Rumah Makan Menggunakan Algoritma Random Forest,†JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 4, pp. 5895-5902, 2025, doi: 10.36040/jati.v9i4.13912.
A. Sari, M. Arifin, and E. Darmanto, “Prediksi Kebutuhan Stok Barang Menggunakan Algoritma Random Forest Untuk Meningkatkan Efisiensi Penjualan,†JOISIE (Journal Of Information Systems And Informatics Engineering), vol. 9, no. 2, pp. 339-351, 2025, doi: 10.35145/joisie.v9i2.5154.
M. S. Efendi, S. Sarwido, and A. K. Zyen, "Penerapan Algoritma Random Forest Untuk Prediksi Penjualan Dan Sistem Persediaan Produk, " Resolusi : Rekayasa Teknik Informatika dan Informasi, vol. 5, no. 1, pp. 12-20 , 2024, doi: 10.30865/resolusi.v5i1.2149.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anisa Pebriyani Huslan, Fathoni Mahardika, Dani Indra Junaedi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Riau Jurnal Teknik Informatika provides open access to anyone so that the information and findings in these articles are useful for everyone. This journal's article content can be accessed and downloaded for free, free of charge, following the creative commons license used.
Riau Jurnal Teknik Informatika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
