Author: Anjar Dimara Sakti ; Andani, I Gusti Ayu Andani ; Anissa Dicky Putri ; Muhammad Rizky Zakiar ; Ismail Al Faruqi ; Cokro Santoso ; Rezzy Eko Caraka ; Pitri Rohayani ; Fabian Surya Pramudya ; Arie Wahyu Wijayanto ; Angga Setiyadi ; Wervyan Shalannanda
DOI: 10.1016/j.jag.2024.104274
Abstract
Equitable internet coverage has emerged as a key global priority, which is essential for promoting inclusive and sustainable development. The Indonesian government aims to provide universal internet access by 2024, particularly in remote regions. This study introduces a novel machine-learning-based approach to identify the priority areas for deploying Base Transceiver Station (BTS) towers, which are crucial for achieving the internet access targets of the government. A BTS Network Priority Index was developed by integrating the internet demand estimates with a BTS suitability index derived from key predictors: proximity to fiber optic stations, physical–environmental suitability, and infrastructure–economic readiness. The model identified areas with high internet demand and high BTS suitability as the most critical for immediate development, covering 20 km2. Additionally, future BTS development should target areas with high demand but medium suitability (900 km2) and medium demand but high suitability (280 km2). To validate the methodology, the Random Forest model was employed, which achieved an area under the curve value of 0.7315, indicating strong predictive performance. For the BTS Deployment Suitability parameter, the median was 0.65, with the lower and upper quartiles at 0.44 and 0.85, respectively, confirming that most proposed locations are highly suitable for development. This systematic approach provides data-driven insights for the equitable distribution of BTS towers to ensure efficient internet infrastructure expansion across Indonesia. Furthermore, the study offers a framework that can be adapted by other countries aiming to improve their digital infrastructure and achieve comprehensive, equitable internet access. © 2024 The Author(s)
Author keywords
Internet accessibility; Machine learning; Remote sensing; Spatial assessment
This article can be accessed at: https://www.scopus.com/pages/publications/85209593186
