PEMANFAATAN MACHINE LEARNING UNTUK PERENCANAAN LOKASI OPTICAL DISTRIBUTION POINT DALAM MENDUKUNG PEMERATAAN AKSES INTERNET

Widiatry Widiatry; Nova Noor Kamala Sari; Aprilita Aprilita

doi:10.47111/jti.v20i1.24531

Authors

Widiatry Widiatry Universitas Palangka Raya
Nova Noor Kamala Sari Universitas Palangka Raya
Aprilita Aprilita Universitas Palangka Raya

DOI:

https://doi.org/10.47111/jti.v20i1.24531

Keywords:

machine learning, Optical Distribution Point, network planning, digital economy

Abstract

Equitable access to the internet is a crucial factor in supporting digital transformation and improving the quality of public services, education, and technology-based economic activities within the digital economy. One of the key components in a fiber optic network is the Optical Distribution Point (ODP), which functions as a distribution node connecting the network to end users. Conventional ODP location planning commonly relies on field surveys and subjective technical judgment, resulting in time-consuming processes and potentially suboptimal decisions.

This study utilizes machine learning as a decision-support tool for ODP location planning to support more structured and data-driven decision-making. The proposed approach integrates spatial customer data, ODP capacity information, and distance parameters to generate ODP location recommendations that can support network expansion planning. The planning system is implemented using a Streamlit-based application to facilitate systematic and measurable location analysis.

The implementation results show that the proposed system can support a more streamlined planning process compared to manual approaches, while also providing consistent, data-driven recommendations. By supporting more structured internet infrastructure planning, this approach contributes to equitable internet access and provides supporting infrastructure for the development of the digital economy, particularly in geographically constrained areas. In addition, the system can serve as a foundation for decision support systems in digital infrastructure planning for academic institutions and local governments.

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DOI: 10.47111/jti.v20i1.24531 DOI URL: https://doi.org/10.47111/jti.v20i1.24531

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