Tito Latif Indra

Urban water resilience has become an essential component of environmental education, yet students often struggle to understand the complex relationships among hydrological processes, spatial vulnerability, and mitigation strategies using conventional instructional media. This study investigated the effectiveness of interactive geospatial learning in enhancing students’ urban water resilience awareness. A quasi-experimental pretest–posttest control group design was conducted with 120 eleventh-grade students from two intact classes in Jakarta. Students in the experimental group learned through an interactive geospatial platform that enabled exploration of hydrological layers, spatial risk visualization, and scenario-based environmental analysis, whereas the control group received conventional instruction using static visual materials. Urban water resilience awareness was assessed using a situational judgment test measuring hydrological understanding, spatial vulnerability, and mitigation decision-making. The findings indicate that students who learned with interactive geospatial media demonstrated substantially greater improvement than those receiving conventional instruction. Most students in the experimental group achieved high levels of learning improvement and successfully met the expected learning mastery criteria. These findings suggest that interactive geospatial learning provides an effective technology-enhanced approach for strengthening spatial reasoning, environmental literacy, and urban water resilience awareness in secondary education.

In Kupang Regency, Indonesia, drought occurs almost every year, affecting rice production, which requires a significant amount of water. Despite its frequent occurrence, limited studies have focused on agricultural drought in the region. Therefore, this study aims to analyse the spatial and temporal patterns of agricultural drought and their relationship with rice yield using the Vegetation Health Index (VHI). Spatial and temporal patterns were analysed based on physical conditions, while the relationship with rice yield was examined using the Spearman correlation test. The results showed that the most severe droughts occurred in 2015 and 2019, affecting 15,063 ha and 14,187 ha, respectively. The Receiver Operating Characteristics (ROC) analysis revealed that VHI had an Area Under the Curve (AUC) of 0.732. In addition, agricultural drought had a positive correlation with elevation. The majority of drought occurred in areas with an elevations of 0–25 m a.s.l., alluvial soil types, slopes of 0–8%, and within 0–229 m of water sources. The results also showed that patterns in Kupang Regency closely followed precipitation trends, with a onemonth lag due to soil moisture. However, agricultural drought did not significantly impact productivity, as shown by significance values greater than 0.05.

Lahan merupakan sumberdaya penting yang kebutuhannya terus meningkat. Namun, ketersediaannya tidak bertambah yang memicu alih fungsi lahan di perkotaan, dan berefek pada perluasan lahan permukimannya ke pinggiran kota, yang disebut peri-urbanisasi. Jabodetabekjur adalah wilayah metropolitan terpadat di Indonesia dengan Kota Jakarta sebagai kota inti dan peri-urban, khususnya Kabupaten Bekasi, Kabupaten Tangerang dan Kabupaten Bogor. Penelitian ini bertujuan mengevaluasi daya dukung lahan permukiman berbasis kemampuan lahan berdasarkan dinamika fisik, sosial dan ekonomi wilayah peri-urban Kota Jakarta sebagai salah satu upaya perencanaan, pemanfaatan dan pengawasan lahan berkelanjutan. Metode yang digunakan dalam penelitian ini adalah studi literatur dengan data-data yang dikumpulkan dari sumber terkait. Hasil menunjukkan bahwa kemampuan pengembangan lahan permukiman di Kabupaten Bekasi, Kabupaten Tangerang, dan Kabupaten Bogor masih cukup tinggi, tepatnya masing-masing sebesar 97%, 98% dan 45,96% dan memiliki status daya dukung permukiman surplus masing-masing sebesar 2,6, 2,7, dan 1,87. Lahan permukiman potensial yang dihasilkan membentuk pola menyebar menuju dataran rendah dan wilayah studi kasus memiliki wilayah yang sudah bersifat kekotaan.

Pekalongan is located in the northern part of Java Island, directly adjacent to the sea in the north. Natural disasters that often occur in Pekalongan are floods, especially in the north of the area, which has a height of 0 meters above sea level. In addition, Pekalongan also has a relatively low land slope of around 0 – 5%, which makes it challenging to distribute water and construct drainage. This study aims to be able to perform predictive modeling of flood-prone areas for the next five years. This study used eight parameters: rainfall, elevation, slope, distance to the river, distance to the sea, groundwater table to surface, soil type, and land use. This research used the Long Short-Term Memory (LSTM) method to predict rainfall parameters using the Python programming language with Jupyter Notebook software. Later, the data will be used as training and test data. Training data testing and tests are conducted to find the minimum failure or error value. The weight scoring method is carried out on each parameter to indicate areas with a high flood vulnerability level. The results showed that Pekalongan has a medium to very high vulnerability level, with a dominant high vulnerability level. The very high level of vulnerability is prevalent in the northern part of the research area, which is directly adjacent to the sea or in the North Pekalongan District. Floods that occur in the northern part of the study area are not only due to high levels of rainfall but can also occur due to the inflow of seawater towards the mainland resulting from high tides and high sea waves. The southern region of the study area has a smaller vulnerability level than the northern region, which has a medium to high vulnerability level.Keywords: Flood ∙ Hazard ∙ Precipitation ∙ LSTM ∙ Rainfall

Eels (Anguilla spp.) are highly nutritious fish that are widely consumed. Ciletuh Bay, situated opposite the Indian Ocean, is a natural habitat for eel larvae. The migration of eel larvae is influenced by various oceanographic factors, including salinity, sea surface temperature, and chlorophyll-a. In order to support fisheries activities in Sukabumi Regency, it is important to identify the optimal habitat for eel larvae and obtain information about their abundance. This study used remote sensing techniques and two algorithms, the Cilamaya Algorithm to estimate salinity and the Wibowo Algorithm to estimate chlorophyll-a. The study was conducted during the wet, dry, and transitional months of 2019 and 2020 in Ciletuh Bay. The potential areas for eel larvae were found to be concentrated at the mouth of the river during the wet month, at the estuary and the shore during the dry month and the first transitional month, and tended to spread during the second transitional month. The largest potential area was found during the dry month of 2019, covering an area of 3.44 km2, but the potential was more stable during the wet months.

Coastal mangrove forests play a crucial role in balancing carbon emissions in the atmosphere as they are a significant carbon store. Previous studies have shown that mangroves can absorb carbon four times more efficiently than terrestrial tropical forests. Unfortunately, the massive development and land use changes in Teluknaga District's coastal areas threaten these ecosystems' existence. To address this concern, efforts are being made to increase conservation, including estimating carbon stock. The aim of this study is to analyze the spatial distribution of biomass and carbon stock of mangrove forests in Teluknaga between 2016-2022 based on vegetation indices such as ARVI, EVI, and SAVI. Sentinel-2 was calculated into ARVI, EVI, and SAVI vegetation indices to model biomass. Statistical correlation analysis was also used to determine the best vegetation index to model biomass in the coastal area of Teluknaga District. This study found that the ARVI vegetation index had the best correlation (R = 0.60) for modeling biomass, with an RMSE value of 36.67 kg/pixel. Most mangrove forests in the coastal area of Teluknaga District showed an increase in biomass and carbon stock between 2016-2022, with significant growth in Muara and Lemo villages' mangrove forests, which is in line with an increase in the area and density of mangrove forests.

The coastal area has experienced significant changes of waste problems over the past few years. To resolve the waste problems in coastal areas, an understanding of community perception is needed to support government efforts. Therefore, this study aims to review people’s perspectives on the dynamics of waste in the coastal areas. Community perception data were compiled through semi-structured interviews with the surrounding communities in coastal areas. ArcGIS and load count analysis were used to analyze the waste density. Waste was collected from the coastal area in Ambon Bay and analyzed using waste density calculation and spatial analysis. The results show that the total waste density obtained at the coastal area of Ambon Bay is 0.249 kg/m², of which 0.078 kg/m² is the density of plastic waste, and 0.171 kg/m² is the density of non-plastic waste. Communities in coastal areas have made efforts to deal with waste problems, but the efforts made are still ineffective in overcoming these problems. That problem happens because there is a lack of knowledge of the community and lack of infrastructure in coastal areas. The research results have the potential for replication in other coastal areas and are used as the basis of decision making for waste management improvement.

Large-scale mining activity is the major environmental issue, including water pollution caused by Acid Mine Drainage (AMD). Samarinda, which is located in the province of East Kalimantan, Indonesia, has open pits and acid contributing land as a source of AMD pollutants. The potential AMD pollution can be assessed by utilizing Geographic Information System (GIS) and Remote Sensing (RS), which are considered reliable tools for measuring, mapping, monitoring, and model making for an area. The variables used in this research are void distribution, land cover, soil type, rainfall, topography, water body, and groundwater. The integration of these variables is used to analyze the potential of AMD pollution to water bodies by acid contributing land. Meanwhile, the void distribution and groundwater integration data are used to analyze the potential of AMD pollution to groundwater by voids. The overlay method is employed to analyze the potential spatial patterns of AMD pollution in the study area. The results show the high potential of AMD pollution to water bodies, specifically in the districts of Samarinda Utara, Palaran, and Sungai Kunjang. The high potential of AMD pollution to groundwater is found in the south delineation area, namely Palaran, Loa Janan Hilir, and Samarinda Seberang districts, with low and medium groundwater depth categories (20 - 70 and 50 - 150 MBGL). The spatial pattern of AMD pollution was random with the geometric arrangement of AMD pollution in the form of clusters.

Jakarta as the country’s capital, has experienced a series of floods that crippled cities in 2002, 2007 and 2013 and extreme rainfall has always been the main cause of major flood with casualties and property. Given the increasing impact of flooding in Jakarta, methods for assessing current and future flood risks is needed. A model is needed that can predict and determine which areas have the potential to be affected by future floods. Several models have been carried out for prediction and assessment of flood risk in Jakarta and several other major cities, namely GIS based on SVM models, hierarchical process analysis models (AHP), machine learning with ensemble models, and multivariate discriminant, classification, and regression trees. However, from several methods there are still some weaknesses, one of which is the time to get a predictive model that is quite long, which is 3-4 hours depending on the amount of data. To overcome this problem, genetic algorithm rule-set production (GARP) and quick unbiased efficient statistical tree (QUEST) models can create a prediction model with a shorter time with the same results. Several factors that influence flooding are used as input model data: precipitation, slope, distance to river, distance to channel, depth to groundwater, land use, elevation, and flood data for 2002 - 2019. The Area under receiver-operator characteristic curve (AUC-ROC) and root mean square error (RMSE) were used as evaluations for model performance. The results showed that the GARP model (AUC-ROC = 94%, RMSE = 0.2) had higher performance accuracy than the QUEST model (AUC-ROC = 84%, RMSE = 0.4). The results also indicated that the distance to channel, land use, and elevation as important parameters in determining flood hazards.

Probolinggo District is one of the centers of tobacco producer in Indonesia, paiton tobacco as local varieties included in dry season type of tobacco (Voor Oogst). Rainfall as well as dry season dynamics in 2015 and 2016 which had already been global, is affected tobacco cultivation and additional values to farmers in Probolinggo district. This research has purpose to identify the exposure patterns of tobacco cultivation places in Probolinggo district related to climate change. Based on daily rainfall data during 1990–2016 from 50 rain gauge stations, parameters of exposure due to climate change are processed by using de Boer method and mapped by using Thiessen polygon method. Determination of level of exposure places are done by scoring method and map overlay technique, then associated with landuse. The analysis results showed that exposure pattern with low level tend to be in the coastal areas and lowlands, stretching from West to East. The higher elevation is, the higher level of exposure places.