One hundred seventeen landslides occurred in Malang Regency throughout 2021, triggering the need for practical hazard assessments to strengthen the disaster mitigation process. In terms of providing a solution for investigating the location of landslides more precisely, this research aims to compare machine learning algorithms to produce an accurate landslide susceptibility model. This research applies three machine learning algorithms composed of RF (random forest), NB (naïve Bayes), and KNN (k-nearest neighbor) and 12 conditioning factors. The conditioning factors consist of slope, elevation, aspect, NDVI, geological type, soil type, distance from the fault, distance from the river, river density, TWI, land cover, and annual rainfall. This research performs seven models over three ratios between the training and testing dataset encompassing 50:50, 60:40, and 70:30 for KNN and NB algorithms and 70:30 for the RF algorithm. This research measures the performance of each model using eight parameters (ROC, AUC, ACC, SN, SP, BA, GM, CK, and MCC). The results indicate that RF 70:30 generates the best performance, witnessed by the evaluation parameters ACC (0.884), SN (0.765), GM (0.863), BA (0.857), CK (0.749), MCC (0.876), and AUC (0.943). Overall, seven models have reasonably good accuracy, ranging between 0.806 and 0.884. Furthermore, based on the best model, the study area is dominated by high susceptibility with an area coverage of 51%, which occurs in the areas with high slopes. This research is expected to improve the quality of landslide susceptibility maps in the study area as a foundation for mitigation planning. Furthermore, it can provide recommendations for further research in splitting ratio scenarios between training and testing data.

This paper proposes to identify the variation of accessibility to healthcare facilities based on vulnerability assessments of floods by using open source data. The open source data comprises Open Street Map (OSM), world population, and statistical data. The accessibility analysis is more focused on vulnerable populations that might be affected by floods. Therefore, a vulnerability assessment is conducted beforehand to identify the location where the vulnerable population is located. A before and after scenario of floods is applied to evaluate the changes of healthcare accessibility. A GIS Network Analyst is chosen as the accessibility analysis tool. The results indicate that the most vulnerable population lives in the Asemrowo district. The service area analysis showed that 94% of the West of Surabaya was well-serviced in the before scenario. Otherwise, the decrement of service area occurs at the city center in the after scenario. Thus, the disaster manager can understand which vulnerable area is to be more prioritized in the evacuation process.

Mangrove grows in coastal areas with the soil is resulted from the accumulation of mud substrate from the sedimentation process. Gresik Regency is a downstream area where the Bengawan Solo River flows and there is the Java Sea that carries a lot of sediment material to the coast. As a result, there is sedimentation that is forming new land increasingly that can be a place for mangroves to live. Therefore in this study, the calculation of suspended sediment concentration and mangrove area in Gresik Regency during 2016-2019 is using Sentinel-2A satellite imagery. The purpose of these calculations is to determine the effect of sedimentation as suspended sediment on changes in the mangrove area. Mangrove area is obtained from the Maximum Likelihood supervised classification. While suspended sediment concentrations estimated from remote sensing data are obtained using four prior algorithms but those do not meet the specified accuracy requirement. This research shows that there have been changes in the mangrove area in the form of increasing and decreasing during 2016-2019. The largest addition of area occurred in the period 2016-2017 which is 479.347 Ha and the most reduction in the area occurred in the period 2018-2019 which is 534.087 Ha. The statistical test result proves that the suspended sediment as sedimentation affects the mangrove by 64.9% in the significance level of 5% or 95% confidence level.

Desa Banturejo adalah sebuah desa di wilayah Kecamatan Ngantang, Kabupaten Malang, Provinsi Jawa Timur. Desa Banturejo memiliki berbagai macam pariwisata yang mampu menarik wisatawan. Desa Banturejo juga memberikan beberapa fasilitas umum tambahan yang akan mempermudah para wisatawan untuk memenuhi kebutuhan, seperti fasilitas kesehatan, tempat ibadah, rumah makan, dan kantor pelayanan. Fasilitas yang telah disediakan ini merupakan sarana penunjang bagi kegiatan pariwisata di Desa Banturejo. Pada kegiatan pengabdian kepa masyarakat ini dibuat peta tematik dengan memanfaatkan informasi geografis untuk pemetaan rute menuju fasilitas umum. Pembuatan peta tematik ini bertujuan untuk mengidentifikasi sebaran fasilitas penunjang wisata yang terdapat di Desa Banturejo serta membuat rekomendasi rute efisien menuju fasilitas penunjang wisata berdasarkan jangkauannya terhadap objek wisata guna mengusulkan Desa Banturejo sebagai desa wisata. Kegiatan ini dilakukan menggunakan metode GeoTagging untuk menandai posisi dan Network Analysis untuk menganalisis rute paling efisien. Kegiatan ini juga melibatkan aspirasi warga setempat untuk menunjukkan lokasi setiap fasilitas umum serta jalan yang dapat dilalui mobil ataupun dnegan berjalan kaki. Hasil dari kegiatan pengabdian kepada masyarakat ini berupa peta rute dari Waduk Selorejo menuju fasilitas umum di Desa Banturejo, karya cipta video pelaksanaan kegiatan, dan berita pada media massa. Hasil tersebut telah dimanfaatkan oleh pemerintah setempat sebagai sumber informasi kepada pengunjung objek wisata Waduk Selorejo mengenai rute ke fasiltas umum terdekat.

International Journal of GEOINFORMATICS