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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Telematika : Jurnal Informatika dan Teknologi Informasi Alhadharah: Jurnal Ilmu Dakwah Jurnal Sains dan Informatika Applied Technology and Computing Science Journal Technologia: Jurnal Ilmiah Journal of Applied Data Sciences Alhadharah: Jurnal Ilmu Dakwah
Munsyi, Munsyi
UIN Antasari Banjarmasin
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : Journal of Applied Data Sciences

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Urban Heat Island Spatial Model for Climate Village Program Planning Munsyi, Munsyi; Nugroho, Agung; Jauhari, Ahmad; Faisal, Moh Reza
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.223

Abstract

Global warming and climate change are critical issues impacting ecosystems, human habitats, and the overall environment. Urban Heat Island (UHI) is a significant phenomenon resulting from increased urban temperatures due to dense urban development, the use of heat-absorbing materials, and reduced vegetation. This study focuses on analyzing the UHI effect in Banjarmasin, Indonesia, using spatial regression and descriptive spatial analysis methods. By employing Land Surface Temperature (LST) data from Landsat 9 and Sentinel-2 satellite imagery, combined with data from wireless sensor networks (WSN), this research aims to develop a comprehensive UHI spatial model to inform climate village program planning. The results reveal substantial temperature variations within Banjarmasin, with urban areas showing significantly higher LST values compared to vegetated outskirts. The integration of satellite data with real-time WSN measurements provides a robust validation method, ensuring accurate environmental monitoring. This study underscores the importance of enhancing green spaces and implementing sustainable spatial planning to mitigate UHI effects. The proposed UHI spatial model offers a valuable tool for urban planners and policymakers in developing strategies to improve urban environmental quality and resilience to climate change.
Realtime and Spatial Data Analysis-based Monitoring System for Proboscis Monkey Habitat Health to Enhance Conservation Area Management Effectiveness Nurliani, Anni; Krisdianto, Krisdianto; Rezeki, Amalia; Munsyi, Munsyi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.334

Abstract

The conservation of proboscis monkeys (Nasalis larvatus), an endemic primate species of Borneo, faces significant threats due to habitat degradation and declining populations. This study aims to develop a real-time and spatial data analysis-based monitoring system to improve the management of conservation areas for the species’ natural habitats. Conducted in the wetland ecosystems of Curiak Island, South Kalimantan, the research integrates remote sensing, Geographic Information Systems (GIS), and Internet of Things (IoT) technologies to monitor key environmental parameters such as vegetation health, land surface temperature (LST), and others. Indices like the Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Environmental Critical Index (ECI) are utilized to assess habitat conditions. Initial results showed poor vegetation health, with an NDVI of 0.6085, high LST of 20.41°C, and considerable environmental stress, reflected by an ECI of 74. Restoration efforts, however, improved conditions, with the NDVI rising to 0.7288, LST decreasing to 20.75°C, and the ECI lowering to 53 in the restoration area, signaling recovery. Though the ECI still suggests moderate environmental stress, the trend is positive. IoT sensors provided continuous real-time data, including CO levels at 0.2 PPM, CO2 at 34,045 PPM, O2 at 20.4% Vol, temperatures ranging from 33.155°C to 33.185°C, humidity between 67.45% and 67.65%, and pH at 6.8. Data on dissolved oxygen, total dissolved solids (TDS), and turbidity were also collected, providing dynamic insights into environmental conditions. The integration of community-based approaches ensures sustainable conservation efforts through local participation. This comprehensive monitoring system supports both proboscis monkey conservation and broader ecological objectives like biodiversity preservation, climate change mitigation, and ecosystem service provision, emphasizing adaptive management in conservation strategies.
Biophysical Model of Mount Babaris for Predicting Carbon Potential using Remote Sensing Jauhari, Ahmad; Syauqiah, Isna; Taati, La; Munsyi, Munsyi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.321

Abstract

The biophysical model of Mount Babaris aims to predict carbon potential using remote sensing technology to address high levels of greenhouse gases, particularly CO2. This study combines satellite data with field measurements to create a validated model analyzing Forest Canopy Height (FCH), Normalized Difference Vegetation Index (NDVI), Vegetation Density (VD), and Land Surface Temperature (LST). A multiple regression analysis shows a strong correlation between these parameters and VD, with an R² value of 0.8673, indicating that 86.73% of the variation in vegetation density can be explained by these variables. Field validation, including drone photographs, crown and stem base density measurements, and plant size, ensures the accuracy of the satellite-derived data. The model uses the equation VD = 123.295486 x NDVI - 0.413961 x LST - 0.410253 x FCH - 3.173195, validated through field data. For processing field measurements, the equation LBDstemCor = 0.007645 x LBDcrown + 0.034348 x VD - 1.575439, with an R² value of 0.9564, further demonstrates its accuracy. To estimate carbon potential in kilograms per pixel (CPP), the equation CPP = LBDstemCor x FCHcor x 0.7 x 680 x 1.34 x 0.47 was used. The predicted carbon potential for Mount Babaris (1,576 ha) ranges from 607,767.55 to 607,829.54 tons, reflecting the model's precision in estimating carbon storage. This model plays a crucial role in monitoring and predicting carbon potential, supporting environmental management and climate change mitigation efforts. By integrating GIS and remote sensing, the model offers a scalable, replicable methodology adaptable to other regions with similar characteristics. It enhances the accuracy of carbon stock estimations and provides essential data for developing strategies to increase carbon sequestration, contributing to global climate change mitigation. The combination of satellite data, field measurements, and statistical analysis makes this model an invaluable tool for effective ecosystem conservation and restoration.
Co-Authors Ahmad Jauhari Amalia Rezeki Amrullah Amrullah Anita Ariani, Anita Anni Nurliani Arum Murdianingsih M.Pd Aslamiah, Rabiatul Aulia Aziza Aulia Aziza, Aulia Fahriansyah Fahriansyah Faisal, Moh Reza Hasanah, Risqiatul Herlita, Jumi Isna Syauqiah Juairiah Juairiah, Juairiah Kiftiyah, Mariatul Krisdianto Sugiyanto Mariatul Kiftiyah Mariyatul Norhidayati Rahmah, Mariyatul Norhidayati Mulyadi Mulyadi Nahed Nuwairah Nugroho, Agung Raden Yani Gusriani Raden Yani Gusriani, Raden Yani Rif'at, Muhammad Samsul Rani Samsul Rani, Samsul Santoso Santoso SANTOSO SANTOSO Taati, La