Mangroves that live in ecotone areas have a fairly significant role in the economy and ecology. This strategic role requires spatial data to facilitate the management and development of mangrove areas. The mangrove mapping process usually uses a manual method, namely through software, and has shortcomings and limitations in image management that require massive data storage. Cloud computing-based Google Earth Engine (GEE) mapping platform can manage images with an extensive scope and spatiotemporal data processing. However, this platform requires index formulas or combinations to help classify and increase accuracy in mapping the earth’s surface. The innovation with the combined VWB-IC (Vegetation-Water-Built-up Index Combined) formula is projected to classify the characteristics of mangrove areas in Jakarta Bay. The combination consists of three types of indices, namely vegetation index (NDVI, GNDVI, ARVI, EVI, SLAVI, and SAVI), water (NDWI, MNDWI, and LSWI), and buildings (IBI and NDBI). This combination is used to translate the classification of mangroves using the Random Forest (RF) machine learning algorithm method with the Sentinel-2 MSI (Multispectral Instrument) satellite image source and through the GEE platform. This platform generates raster data for land use classification (including mangroves), and then the analysis is continued using ArcMap software. The obtained mangrove area is 220.43 ha, located in Jakarta Bay and divided into the Angke Kapuk Nature Tourism Park and the Pantai Indah Kapuk Mangrove Ecotourism Area. The data from this research is expected to provide a recommendation for a combination index formula for mapping mangrove areas in urban areas. The spatial distribution area can be used as an evaluation material in mangrove areas in Jakarta Bay

Mangrove ecosystem is a very potential area, generally located in ecoton areas (a combination of intertidal and supratidal areas), where there is interaction between waters (sea, brackish water, and rivers) with land areas. Indonesia, especially the Banten and West Java regions, have vast mangrove areas and are currently under threat of land conversion. Moreover, mapping the distribution of mangrove forests using the Google Earth Engine platform based on Cloud Computing is less published. Therefore, this research was conducted by introducing the distribution of mangrove forests which involved the Random Forest (RF) classification algorithm method, and looking for the best modification of the index. The combination test was carried out by involving the NDVI, EVI, ARVI, SLAVI, IRECI, RVI, DVI, SAVI, IBI, GNDVI, NDWI, MNDWI, and LSWI indexes. There is a distribution of mangroves in three provinces (West Java, Banten, and Jakarta) which are 933.54 ha (8.372%), 1,537.89 ha (18.231%), and 8,184.82 ha (73.397%). Of the 70 combination tests, the LSWI index (K13, Type-A) is the combination with the lowest accuracy rate of 58.45% (Overal Accuracy) and 39.59 (Kappa statistic), and the combination of K23 (SAVI-MNDWI-IBI) is a combination the best are 96.48% and 92.79. The results and recommendations in this study are expected to be used as a reference in determining policies for the protection of mangrove areas and a reference for further research

The Ujung Kulon National Park (UKNT) is one of the national parks on the island of Java and has an essential role in saving endemic species in Indonesia. As a form of national park conservation effort, the completeness of LULC spatial data is a primary database that is indispensable in determining national park management policies. Therefore, this research was conducted to map the LULC (Land Use - Land Cover) in the forest landscape with high heterogeneity in UKNT. Sentinel-2 MSI (Multispectral Instrument) image data were classified using the Random Forest (RF) classification algorithm and tested using 11 index algorithms. The classification process takes place on a cloud computing-based geospatial platform, Google Earth Engine (GEE). This test resulted in 10 LULC classes; water had the broadest percentage of 45.44%. Meanwhile, the primary forest has an area of 21,868.41 or about 19.53% of the total area of the national park. However, there are some discrepancies in the spatial information generated by this classification process, so it is considered necessary to evaluate the combination of indexes, training data, and classification algorithms to limit the classification area. Therefore, this study is expected to be considered for further research related to LULC in high-heterogeneity landscapes.

ABSTRAKStunting dapat menyebabkan gangguan pertumbuhan dan perkembangan pada anak. Angka stunting di Indonesia masih cukup tinggi. Salah satu solusi untuk permasalahan stunting di Indonesia adalah pemberian ASI eksklusif. Kandungan dalam ASI dapat berperan sebagai zat pembangun, zat pengatur, dan zat tenaga. Desa Sukajadi, Soreang menjadi salah satu lokasi stunting pada tahun 2022 dengan persentase bayi yang mendapatkan ASI eksklusif 42%. Pentingnya edukasi mengenai ASI eksklusif menjadi salah satu cara pencegahan stunting pada anak. Pelaksanaan edukasi ASI eksklusif pada ibu hamil Desa Sukajadi, Soreang menggunakan metode penyuluhan dengan media flyer. Pengukuran pengetahuan sebelum dan sesudah diberikan edukasi diukur menggunakan pre dan post-test. Terjadi peningkatan pengetahuan ibu hamil mengenai ASI eksklusif dari 80,88% menjadi 92,7%. Edukasi ASI eksklusif memberikan pengaruh bermakna dan meningkatkan kesadaran ibu tentang pentingnya pemberian ASI eksklusif sebagai langkah pencegahan stunting pada anak.