Implementasi Data Mining Berbasis AI Menggunakan Docker untuk Big Data Skalabel

M Budi Hartanto

Abstract


Pertumbuhan big data yang sangat pesat menimbulkan tantangan signifikan dalam hal pemrosesan, skalabilitas, serta deployment sistem data mining. Infrastruktur konvensional sering mengalami keterbatasan dalam menangani volume data besar secara efisien, sehingga menimbulkan bottleneck kinerja dan permasalahan konsistensi lingkungan pengembangan. Penelitian ini bertujuan untuk mengimplementasikan kerangka kerja data mining berbasis Artificial Intelligence (AI) dengan memanfaatkan teknologi container Docker guna mendukung analisis big data yang skalabel dan portabel. Penelitian ini dilatarbelakangi oleh kebutuhan akan lingkungan komputasi yang fleksibel, efisien, dan mampu menjamin konsistensi antara tahap pengembangan dan produksi.

Metode penelitian meliputi perancangan arsitektur sistem berbasis Docker yang mengintegrasikan algoritma machine learning dalam lingkungan terisolasi dan ringan. Tahapan penelitian mencakup proses prapemrosesan data, pelatihan model, evaluasi kinerja, serta deployment sistem. Evaluasi dilakukan dengan mengukur waktu pemrosesan, penggunaan sumber daya, serta kemampuan sistem dalam menangani peningkatan volume data. Hasil penelitian menunjukkan bahwa implementasi berbasis Docker mampu meningkatkan efisiensi deployment, memperbaiki skalabilitas sistem, serta mengurangi permasalahan ketergantungan lingkungan dibandingkan pendekatan konvensional.

Temuan ini menunjukkan bahwa integrasi AI dan teknologi container memberikan solusi yang andal dan adaptif untuk pengelolaan big data, serta berkontribusi dalam pengembangan sistem analitik yang berkelanjutan dan berbasis data.


Full Text:

PDF

References


X. Xingquan, G. Wu, and D. Wei, “Practical big data techniques for end-to-end machine learning deployment: A comprehensive review,” *Discover Data*, vol. 5, no. 1, pp. 1–20, 2025.

A. Alaskar, H. Almesned, N. Almuqati, and M. M. Hassan, “Big data mining and its challenges: A review,” *J. Comput. Sci. Softw. Dev.*, vol. 8, no. 2, pp. 45–60, 2023.

Journal of Big Data, “Big data technologies and analytics: Recent trends and challenges,” *J. Big Data*, vol. 9, no. 1, pp. 1–15, 2024.

A. Saputra and D. Mahdiana, “Analysis of machine learning and data mining implementation in predictive analytics,” *J. Teknol. Inf.*, vol. 5, no. 1, pp. 12–20, 2025.

C. S. Veluru, “Data mining best practices in AI-based analytics,” *J. Artif. Intell. Cloud Comput.*, vol. 4, no. 2, pp. 33–42, 2024.

Y. D. Dessalk et al., “Docker container-based big data processing system in multiple clouds,” in *Proc. Int. Conf. Cloud Comput.*, 2021, pp. 112–118.

A. Theofilou et al., “Scalable big data warehouse design,” *Sustainability*, vol. 17, no. 8, pp. 3727–3740, 2025.

M. K. Hassan and S. Rahman, “AI-driven predictive analytics in large-scale environments,” *IEEE Access*, vol. 10, pp. 45678–45690, 2022.

T. White, *Hadoop: The Definitive Guide*, 4th ed. Sebastopol, CA, USA: O’Reilly, 2017.

M. Zaharia et al., “Apache Spark: A unified engine for big data processing,” *Commun. ACM*, vol. 59, no. 11, pp. 56–65, 2016.

H2O.ai, “Distributed machine learning platform,” Tech. Rep., 2020.

L. Merkel, “Docker: Lightweight Linux containers,” *Linux J.*, vol. 2019, no. 239, pp. 2–6, 2019.

P. Pahl, “Containerization and the PaaS cloud,” *IEEE Cloud Comput.*, vol. 2, no. 3, pp. 24–31, 2016.

D. Bernstein, “Containers and cloud: From LXC to Docker to Kubernetes,” *IEEE Cloud Comput.*, vol. 1, no. 3, pp. 81–84, 2016.

S. Zhang et al., “Performance evaluation of container-based virtualization for big data applications,” *IEEE Trans. Cloud Comput.*, vol. 8, no. 4, pp. 1020–1032, 2020.

J. Chen, Y. Mao, and X. Liu, “Big data: A survey,” *Mobile Netw. Appl.*, vol. 19, no. 2, pp. 171–209, 2016.

I. Goodfellow, Y. Bengio, and A. Courville, *Deep Learning*. Cambridge, MA, USA: MIT Press, 2016.

K. Grolinger et al., “Data management in cloud environments: NoSQL and big data,” *J. Cloud Comput.*, vol. 5, no. 1, pp. 1–24, 2016.

S. Sagiroglu and D. Sinanc, “Big data: A review,” in *Proc. Int. Conf. Collaboration Technologies*, 2016, pp. 42–47.

F. Chollet, *Deep Learning with Python*, 2nd ed. Shelter Island, NY, USA: Manning, 2021.

R. Morabito, “Virtualization on Internet of Things edge devices,” *IEEE Internet Things J.*, vol. 5, no. 2, pp. 883–894, 2018.

W. Felter et al., “An updated performance comparison of virtual machines and Linux containers,” in *Proc. IEEE Int. Symp. Perform. Anal. Syst. Softw.*, 2017, pp. 171–172.

B. Burns et al., “Kubernetes: Up and running,” Sebastopol, CA, USA: O’Reilly, 2019.

Y. Chen et al., “Container orchestration for scalable machine learning,” *IEEE Trans. Serv. Comput.*, vol. 14, no. 5, pp. 1402–1415, 2021.

N. Dragoni et al., “Microservices: Yesterday, today, and tomorrow,” in *Present and Ulterior Software Engineering*, Springer, 2017, pp. 195–216.




DOI: https://doi.org/10.57084/jeda.v6i2.2228

Refbacks

  • There are currently no refbacks.


Jurnal Teknologi dan Informatika (JEDA)
Program Studi S1 Informatika, Fakultas Ilmu Komputer, Universitas Mitra Indonesia
Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Mitra Indonesia


Editorial Address
Jl. ZA. Pagar Alam No.7, Gedong Meneng, Kec. Rajabasa, Kota Bandar Lampung
HP : 085269945505 (Yodhi Yuniarthe). E-mail: yodhi@umitra.ac.id
HP : 089509553111 (Khozainuz Zuhri). E-mail: zuhri@umitra.ac.id