<![CDATA[The reliability of medium-voltage overhead distribution networks (SUTM) is strongly influenced by vegetation conditions along the Right-of-Way (ROW) corridor. At PT PLN (Persero) UP3 Malang, inspections have conventionally relied on visual estimation, which produces subjective and less accurate distance assessments between trees and power lines, increasing the risk of vegetation-induced disturbances. Operational data from 2024–2025 indicate that vegetation accounts for approximately 189–194 disturbance events per year, despite conducting over 25,000 visual inspection points per month at a cost exceeding IDR 100 million. This study proposes the design and development of a Next Level Inspection Application integrating trigonometric measurement methods using an NX500 Rangefinder to enhance ROW inspection accuracy and support predictive maintenance. A quantitative comparative approach was applied across 15 tree sample objects representing five species with varying growth rates. Results demonstrate visual inspection errors ranging from 7.1% to 37.1%, with 9 of 15 trees misclassified under visual estimation. The application supports vegetation growth modeling validated by two field inspections, enabling risk-based maintenance scheduling with predicted timelines to the 2.5-meter safety limit. The findings confirm that the proposed application substantially improves ROW inspection quality and contributes to improved distribution system reliability.]]>
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