pencatatan stok barang yang tidak terstruktur, yang menyebabkan ketidakefisienan dan potensi kesalahan dalam pengelolaan produk. Untuk mengatasi masalah ini, penelitian ini bertujuan mengembangkan sebuah website inventaris yang dapat meningkatkan efisiensi dan ketepatan manajemen persediaan. Metode pengembangan yang digunakan adalah Software Development Life Cycle (SDLC) dengan model prototype, menggunakan React JS untuk front-end, Express.js untuk back-end, dan Firebase sebagai database. Proses pengembangan meliputi tahapan perencanaan, analisis, perancangan, implementasi, dan uji coba. Setelah website inventaris Toko Berkah Lestari berhasil dibuat, dilakukan uji coba blackbox testing dan validasi input. Hasilnya menunjukkan bahwa website berjalan dengan baik dan telah dihosting, sehingga dapat diakses melalui tautan https://berkah-lestari-4c4d4dc7163e.herokuapp.com. Dengan adanya website inventaris, Toko Berkah Lestari dapat memenuhi kebutuhan pengelolaan inventarisnya secara lebih efektif

Floods are natural disasters that often occur in the DKI Jakarta area. DKI Jakarta government needs to anticipate the impact of the flood disaster by estimating the number of people affected by the flood. The number of people affected by floods that are uncertain every month can be predicted for the future. There are many ways that can be predict the number of people affected by floods, one of them is artificial neural network method. One of learning algorithms in artificial neural networks is backpropagation algorithm. This research applies an artificial neural network method with backpropagation algorithm to predict the number of people affected by floods in DKI Jakarta. In this research, training process was carried out 100 times on each network architecture (12-10-1, 12-12-1, 12-14-1) with several parameters such as epoch, momentum constant, and learning rate. The best results in the training process are carried out to testing process to test the network. In the testing process, the best results are 12-10-1 architecture with an accuracy rate 98.704%. Based on these results, it can be said that this network can predict well and can be implemented for forecasting the number of people affected by floods in DKI Jakarta.