Pengembangan Sistem Pengaduan Masyarakat Berbasis Citra Digital Menggunakan CNN dan Content-Based Filtering pada Kantor Kelurahan Peguyangan

Putu Muna Saskara Widnyana (Politeknik Negeri Bali)
Ni Gusti Ayu Putu Harry Saptarini (Politeknik Negeri Bali)
Putu Desiana Wulaning Ayu (Politeknik Negeri Bali)

METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi

This study aims to develop a digital image-based road damage reporting system at the Peguyangan Subdistrict Office to assist in the automatic classification of road damage levels and detect duplicate reports. The system was built by integrating a MobileNetV2-based Convolutional Neural Network (CNN) for classifying road damage images and Content-Based Filtering (CBF) for identifying similar reports based on image similarity and proximity of location. Model evaluation was conducted using 5-fold cross-validation on a dataset of 500 images of damaged roads consisting of two classes: severe damage and minor damage. The test results showed that the model achieved an average accuracy of 89.40% with a best F1-score of 0.9194. The duplicate detection mechanism successfully grouped similar reports, thereby aiding in the prioritization of repairs. Additionally, black-box testing across 20 test scenarios showed that all system functions operated as designed with a 100% success rate. The research results indicate that the integration of CNN and CBF can support the management of road damage complaints in a more effective, structured, and responsive manner.