Implementasi Filter Foto Cerdas Berbasis CNN pada Sistem Pelaporan Jalan Rusak Berbasis Web
DOI:
https://doi.org/10.46880/jmika.Vol10No2.pp437-444Keywords:
CNN, Image Classification, GIS, Road Damage, Web ReportingAbstract
Road infrastructure damage threatens user safety, but the effectiveness of public reporting is often hindered by irrelevant reports (spam) that overburden manual verification. This study proposes an update to smart public services by integrating a Convolutional Neural Network (CNN) as an automatic filter and a Geographic Information System (GIS) for spatial mapping. Evaluation using the MobileNetV2 architecture on 500 images yielded 95% accuracy, 89% recall, 94% F1-Score, and a perfect 100% precision. This 100% precision highlights the main scientific contribution: the model's ability to reduce false positives to zero, actively blocking all spam reports at the initial gateway in real-time. Validated reports are automatically visualized on the WebGIS map. This integration is proven to digitize and automate initial report filtering, offering significant bureaucratic efficiency for government agencies.
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