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Erosion and accretion occuring in coastal areas are indications of changes in the coastline's position. The beach areas in Bali have high economic value as tourist attractions; yet, the condition of the beaches in several places on the island is very concerning. Monitoring shoreline changes can be accomplished using remote sensing technology which is more efficient than the conventional methods. This paper describes the results of changes in the coastline from 2014 to 2021 with remote sensing for all coastal areas of the island of Bali surveyed in the field. This research has successfully detected changes in coastlines throughout the coastal areas of Bali Island by remote sensing method and verified by field survey. The survey results also show that remote sensing method has met the survey from previous studies specifically in Gianyar Regency and Jembrana Regency. Hence, it is applicable for further use in other areas. [ABSTRACT FROM AUTHOR]

The awareness to fulfill the rights of people with disabilities (PWDs) in Indonesia has increased significantly recently. To address concerns regarding the rights of individuals with disabilities, Indonesia's two most prominent Islamic organizations (Nahdlatul Ulama and Muhammadiyah) have released fatwas on Islam and disabilities. NU published Fiqih Penguatan Penyandang Disabilitas, while Muhammadiyah prepared its Fikih Difabel. Previous studies have neglected a critical area of intersection between Islamic law, women, and disability issues. This article explores the contribution of women with disabilities in formulating these fatwas and how they tackle problems relevant to women with disabilities. Based on textual analysis of those fatwa and field research, the paper argues that although women with disabilities have been involved in the drafting, their participation has been limited. Only a few were engaged in making NU's Fiqih Penguatan Penyandang Disabilitas; only limited types of disabilities were involved in Muhammadiyah's Fikih Difabel. As a result, this study found that the fatwas on the issues related to women with disabilities are comparatively less extensive. They leave room for developing a more women-friendly Fikih Difabel in the future. [ABSTRACT FROM AUTHOR]

Several studies have focused on generating seismic vulnerability maps for earthquake-prone areas, particularly in Indonesia. Building typologies are a key factor in determining vulnerability to earthquakes. However, conducting large-scale field surveys to determine the spatial distribution of building typologies in a city is uneconomical. This paper explores the use of a convolutional neural network (CNN) to automatically detect building typologies from diverse regions in Indonesia, utilizing both conventional and automated building image acquisition processes. In this study, datasets from three distinct image acquisition methods are trained with four unique CNN architectures to identify the best-performing model to classify building typologies. The sample size effect on CNN performance is also investigated. The results showed that randomly sampled Google Street View (GSV) images are the most effective dataset for the CNN model, achieving an f1-score of 84.33%. Among the network architectures tested, MobileNet demonstrated superior performance on the majority of evaluated datasets. As the sample size increases by about 350% in the dataset, there is a positive correlation with up to 2.3% f1-score improvement. Using the best-performing CNN model, two building vulnerability models were employed to assess the spatial distribution of building damage in the urban area of Bandung, considering a hypothetical scenario of an M7 earthquake. Incorporating local construction data, one of the generated maps estimated that approximately 55% of buildings in Bandung would experience moderate to severe structural damage. This study showcases the potential of CNN models in automating regional seismic assessments and providing valuable insights for comprehensive seismic mitigation strategies. • A decision tree to label building typology objectively in Indonesia was developed. • CNN model trained with randomly sampled GSV dataset outperforms other models. • The CNN and probability model generated a city-scale building vulnerability map. [ABSTRACT FROM AUTHOR]