BibTex Citation Data :
@article{JSINBIS27085, author = {Chrisdano Dasmasela and Irwan Sembiring and Hindriyanto Purnomo}, title = {Sistem Peringatan Dini Rawan Bencana Longsor Di Kota Ambon menggunakan IoT}, journal = {Jurnal Sistem Informasi Bisnis}, volume = {10}, number = {2}, year = {2020}, keywords = {GIS; IoT; Landslide; Early Warning System.}, abstract = { Landslide disasters not only cause property losses but also have a multidimensional impact, for example people's psychology becomes disrupted, relocation of residential settlements and even disruption of investment as capital for economic development in Ambon City. This research aims to design a early warning system for landslide prone disasters in Ambon city and analysis using IoT . The results of the analysis use satellite landsat imagery as supporting spatial data with overlay indicators for landslide causes, including: rainfall, soil type, slope, population and land cover combined with placing IoT devices in landslide prone areas resulting in an early warning system for landslides. This IoT device uses a soil moisture sensor to read soil moisture and an MPU6050 accelerometer sensor to read soil movements. The results of the two sensors if they meet the criteria for landslide prone will be processed and sent as a notification to the smartphone. This research produces a landslide detection system starting from the analysis of landslide hazard maps to using sensors to detect landslide symptoms and then sending notification as a danger sign. }, issn = {2502-2377}, pages = {220--227} doi = {10.21456/vol10iss2pp220-227}, url = {https://ejournal.undip.ac.id/index.php/jsinbis/article/view/27085} }
Refworks Citation Data :
Landslide disasters not only cause property losses but also have a multidimensional impact, for example people's psychology becomes disrupted, relocation of residential settlements and even disruption of investment as capital for economic development in Ambon City. This research aims to design a early warning system for landslide prone disastersin Ambon city and analysis using IoT.The results of the analysis use satellite landsat imagery as supporting spatial data with overlay indicators for landslide causes, including: rainfall, soil type, slope, population and land cover combined with placing IoT devices in landslide prone areas resulting in an early warning system for landslides. This IoT device uses a soil moisture sensor to read soil moisture and an MPU6050 accelerometer sensor to read soil movements. The results of the two sensors if they meet the criteria for landslide prone will be processed and sent as a notification to the smartphone. This research produces a landslide detection system starting from the analysis of landslide hazard maps to using sensors to detect landslide symptoms and then sending notification as a danger sign.
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