1,720,966 research outputs found
Pemenang Hibah Penelitian Dikti tahun Anggaran 2020 sistem filter di MS Excell
No full textDaftar Pemenang Hibah Penelitian Dikti Tahun Anggaran 202
Daftar Pemenang Hibah Abdimas tahun Anggaran 2020 dalam format excell
No full textDaftar Pemenang Hibah Abdimas tahun Anggaran 2020 dalam format Excel
Supplementahibah l materials for preprint: Daftar Pemenang Hibah Abdimas tahun Anggaran 2020 dalam format excell
No full textPemenang Hibah Abdimas Dikti Tahun Anggaran 2020 (MS EXCELL)
No full textdaftar pemenang hibah dikti tanhun anggaran 202
Supplemental materials for preprint: Pemenang Hibah Penelitian Dikti tahun Anggaran 2020 sistem filter di MS Excell
No full textSularso Budilaksono's Quick Files
No full textThe Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity
Supplementahibah l materials for preprint: Daftar Pemenang Hibah Abdimas tahun Anggaran 2020 dalam format excell
No full textSupplemental materials for preprint: Pemenang Hibah Penelitian Dikti tahun Anggaran 2020 sistem filter di MS Excell
No full textRancang Bangun Sistem Deteksi Jatuh (Fall Detection System) pada Penghuni Rumah berbasis Convolutional Neural Network
Get PDFFalls among household members are a serious issue with significant impacts onphysical, psychological, and economic well-being. This proposal outlines the developmentof a fall detection system based on a Convolutional Neural Network (CNN) integrated intoa web application using Flask. The system aims to detect fall incidents accurately and inreal-time without requiring additional devices such as IoT sensors or wearable gadgets. TheCNN model will be trained using public datasets like the Le2i Fall DetectionDataset and UP-Fall Detection Dataset to enhance detection sensitivity and specificity. Byadopting the YOLO (You Only Look Once) algorithm, the system can process visual dataquickly and efficiently. The web-based implementation allows household members,caregivers, or family to monitor residents easily through a user-friendly interface. Theoutcomes of this research are expected to provide a practical solution to improve householdsafety, enable rapid response to incidents, and minimize severe consequences caused bydelayed intervention
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