Pendampingan Analisis Data Pelayanan Transportasi di Dinas Perhubungan Provinsi Sumatera Utara untuk Meningkatkan Efisiensi Pelayanan Publik
DOI:
https://doi.org/10.51903/3qcd1c52Keywords:
Analisis Data, Transportasi, Pelayanan Publik, Efisiensi, PartisipatifAbstract
Kegiatan ini bertujuan untuk meningkatkan kemampuan aparatur Dinas Perhubungan Provinsi Sumatera Utara dalam menganalisis data transportasi guna mendukung efisiensi pelayanan publik. Latar belakang kegiatan ini adalah masih terbatasnya kemampuan pegawai dalam mengolah data secara sistematis, sehingga laporan tahunan belum sepenuhnya berbasis bukti (evidence-based). Metode pelaksanaan menggunakan pendekatan partisipatif (participatory approach) melalui tahapan koordinasi awal, pelatihan teknis, pendampingan analisis data, dan evaluasi hasil. Kegiatan dilaksanakan pada bulan Februari hingga April 2025 dengan melibatkan dua belas peserta dari Bidang Data dan Perencanaan. Hasil kegiatan menunjukkan peningkatan kemampuan peserta dalam mengolah data transportasi menggunakan Microsoft Excel, menyusun grafik, serta menafsirkan tren volume barang dan penumpang di Pelabuhan Belawan. Evaluasi akhir menunjukkan bahwa 80% peserta mampu membuat visualisasi data dan 70% mampu membaca tren pertumbuhan tahunan. Kegiatan ini berkontribusi terhadap peningkatan efisiensi pelaporan serta membangun budaya kerja berbasis data di lingkungan Dinas Perhubungan Provinsi Sumatera Utara.
References
[1] World Bank, “World Bank Annual Report 2022: Working Toward Development Goals Amid Crises,” Washington, D.C., United States, 2022. [Online]. Available: https://thedocs.worldbank.org/en/doc/811305cdbaf5310bc659f14b1e49f05c-0090012022/original/AR2022EN.pdf
[2] A. David et al., “Understanding Local Government Digital Technology Adoption Strategies: A PRISMA Review,” Sustainability, vol. 15, no. 12, p. 9645, Jun. 2023, doi: 10.3390/su15129645.
[3] O. M. Prabowo, E. Mulyana, I. G. B. B. Nugraha, and S. H. Supangkat, “Cognitive City Platform as Digital Public Infrastructure for Developing a Smart, Sustainable and Resilient City in Indonesia,” IEEE Access, vol. 11, pp. 120157–120178, 2023, doi: 10.1109/access.2023.3327305.
[4] N. Rokhman, C. Jatmiko, S. Rakasiwi, and H. Lestiawan, “Pelatihan Diklat Visualisasi Data Menggunakan Google Data Studio untuk Guru dan Dosen pada Perkumpulan Profesi Multimedia dan Teknologi Informasi (PPMultindo),” Community J. Pengabdi. Pada Masy., vol. 3, no. 3, pp. 54–60, Nov. 2023, doi: 10.51903/community.v3i3.415.
[5] K. L. M. Ang, J. K. P. Seng, E. Ngharamike, and G. K. Ijemaru, “Emerging Technologies for Smart Cities’ Transportation: Geo-Information, Data Analytics and Machine Learning Approaches,” ISPRS Int. J. Geo-Information, vol. 11, no. 2, p. 85, Jan. 2022, doi: 10.3390/ijgi11020085.
[6] I. Tsouros et al., “From Raw Data to Informed Decisions: The Development of an Online Data Repository and Visualization Dashboard for Transportation Data,” Procedia Comput. Sci., vol. 231, no. 2023, pp. 161–167, 2024, doi: 10.1016/j.procs.2023.12.188.
[7] D. U. Rais and Y. T. Rini, “Pelatihan Creative Thinking untuk Menghasilkan Inovasi Pelayanan Publik Bagi Sekretaris Kecamatan,” Community J. Pengabdi. Pada Masy., vol. 2, no. 3, pp. 149–157, Dec. 2022, doi: 10.51903/community.v2i3.279.
[8] Z. Zong and Y. Guan, “AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency,” J. Knowl. Econ., vol. 16, no. 1, pp. 864–903, 2025, doi: 10.1007/s13132-024-02001-z.
[9] N. Rokhman, S. Rakasiwi, E. Sugiarto, and F. Budiman, “Optimalisasi Layanan Administrasi Melalui Pemanfaatan Teknologi Informasi untuk Tenaga Pendidik pada Fakultas Ekonomi dan Bisnis Islam UIN Gus Dur Pekalongan,” Community J. Pengabdi. Pada Masy., vol. 3, no. 3, pp. 75–79, Nov. 2023, doi: 10.51903/community.v3i3.421.
[10] W. Woldemariam, “A Framework for Transportation Infrastructure Cost Prediction: A Support Vector Regression Approach,” Transp. Lett., vol. 14, no. 9, pp. 997–1003, Oct. 2022, doi: 10.1080/19427867.2021.1985895.
[11] P. Pavone, P. Ricci, M. Calogero, and P. Capaccioni, “A Literature Overview on Data-Driven Value and Accountability: Connecting the Private and Public Dimensions,” Public Integr., vol. 26, no. 3, pp. 285–304, 2024, doi: 10.1080/10999922.2022.2163049.
[12] N. Gorshkova, K. Al-Battat, Y. Elsukova, T. Yovanovich, and I. Zamyatina, “The Role of Corporate Integrated Reporting in the Management Decision-Making Conditions of the Transport Industry,” Transp. Res. Procedia, vol. 63, pp. 896–903, 2022, doi: 10.1016/j.trpro.2022.06.087.
[13] V. M. Urbano, M. Arena, and G. Azzone, “Big Data for Decision-Making in Public Transport Management: A Comparison of Different Data Sources,” Res. Transp. Bus. Manag., vol. 59, p. 101298, 2025, doi: 10.1016/j.rtbm.2025.101298.
[14] K. Kušić, R. Schumann, and E. Ivanjko, “A Digital Twin in Transportation: Real-Time Synergy of Traffic Data Streams and Simulation for Virtualizing Motorway Dynamics,” Adv. Eng. Informatics, vol. 55, p. 101858, 2023, doi: 10.1016/j.aei.2022.101858.
[15] H. Fatorachian, H. Kazemi, and K. Pawar, “Digital Transformation for Sustainable Transportation: Leveraging Industry 4.0 Technologies to Optimize Efficiency and Reduce Emissions,” Futur. Transp., vol. 5, no. 2, p. 34, 2025, doi: 10.3390/futuretransp5020034.
[16] M. Z. Yaqub and A. Alsabban, “Industry-4.0-Enabled Digital Transformation: Prospects, Instruments, Challenges, and Implications for Business Strategies,” 2023. doi: 10.3390/su15118553.
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