Penerapan Sistem Jasa Tukang Untuk Klasifikasi Jenis Kerusakan Rumah Menggunakan Metode Convolutional Neural Network (CNN) Berbasis Website Pada PT. Raja Dwiguna Semesta
Keywords:
Convolutional Neural Network, house damage classification, handyman service, websiteAbstract
The rapid development of artificial intelligence technology has encouraged innovation in various service sectors, including home repair services. PT. Raja Dwiguna Semesta still relies on manual identification of house damage, resulting in delays and inaccuracies in determining appropriate repair actions and assigning workers. This study aims to develop a web-based handyman service system capable of automatically classifying house damage using the Convolutional Neural Network (CNN) method. The research employed a system development approach using the Waterfall model, while the classification model was developed through stages of data collection, image preprocessing, training, validation, and testing. The dataset consisted of four categories of house damage, namely roof leakage, wall cracks, floor damage, and paint deterioration. The developed system integrates CNN-based image classification with a handyman recommendation feature to support service efficiency. The results indicate that the proposed system is capable of identifying house damage categories accurately and providing appropriate handyman recommendations based on classification outcomes. The implementation of this system contributes to improving service effectiveness, accelerating response time, reducing identification errors, and supporting digital transformation in the home repair service industry.Downloads
References
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