bluuebunny/crossref_metadata_2025_split huggingface dataset:
Silver nanoparticle-based biosensor as a rapid test for the detection 3-monochloropropane-1,2-diol (3-MCPD) in cooking oil
Authors: Mahardika R G , Putri F A , Syarmila  , Rizal F , Guskarnali  , Henri  
Published: December 2023

Abstract
One of the toxic compounds that are carcinogenic (cancer causing) in food is chloropropanol compound. These compounds are currently of great concern considering the increasing number of cancer and kidney failure sufferers in the world apart from degenerative diseases. Derivatives of these compounds are widely found in food ingredients, such as vegetable oils, baby food, smoked fish, infant formula, processed soybeans, margarine, cereals, soy sauce, and others. ). There are several types of chloropropanol compounds with one or two chlorine atoms in glycerol at position 1 or 2 such as 3-monochloropropane-1,2-diol (3-MCPD), 2-monochloropropane-1,3-diol (2-MCPD). Therefore, early prevention is needed for further treatment through the detection of 3-MCPD in cooking oil. The development that will be carried out is a silver nanoparticle-based biosensor combined with colorimetric. The method used to obtain a suitable biosensor is by synthesizing silver nanoparticles (AgNPs) coated with cystein. The content of 3-MCPD in the new oil is 2,828 µg/Kg. Meanwhile, used cooking oil has a higher content of 3-MCPD, that is as much 19,042 µg/Kg. This value exceeds the maximum limit set by the European Commission (EC), which is 1,250 µg/Kg for oils/fats sourced from coconut, corn, sunflower, palm kernel oil and olive oil.
Modelling above-ground biomass estimation using combination of vegetation height and forest canopy density at secondary peat swamp forest of Kalimantan
Authors: Nurhidayat M Z , Aditya T , Zannah A L , Firdausia S 
Published: December 2023

Abstract
Rapid estimation of carbon stock, particularly carbon from Above-Ground Biomass (AGB) in a high-potential carbon forest, is required to assess potential carbon emission projects. Satellite imagery has been widely used as the main data source to rapidly estimate AGB. Canopy Height (CH) data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and Forest Canopy Density (FCD) data from satellite Landsat 8 imagery are widely used to estimate AGB. An allometric model with multiple linear regression equations has been used to estimate the AGB value using both parameters of the FCD value and the CH value. 43 values of calculated AGB by non-destructive field survey located in a peat swamp forest area use as a training dataset to develop an allometric model. The CH and FCD indices performed several tests, and the test result met the requirements for developing allometric equations. The coefficient correlation (r) of CH and AGB from 43 sample plots is 0.546, indicating a moderate correlation. The coefficient correlation (r) for FCD and AGB is 0.267 which indicates a weak correlation. The validation test was carried out to assess AGB from the developed allometric model and AGB of field survey with a non-destructive method. The validation result of the allometric model has the value of coefficient correlation (r), RMSE, and R2, respectively 0.569; 22.235; and 0.324. In terms of allometric model validation, another forest with typical conditions, particularly basal area, the model’s AGB result, and the validated area have RMSE 46.506 and r value 0.664.
Predicting the Distribution of Mangrove Forests through Random Forest Algorithm by Combining Spectral Features and DEM Data
Authors: Purwanto Anang D. , Wikantika Ketut , Darmawan Soni , Deliar Albertus 
Published: December 2023

Abstract
Using satellite data and machine learning-based classification methods for mangrove forest identification has gained popularity due to their effectiveness in producing high accuracy. Therefore, this research developed a random forest classification model using various dataset combinations representing spectral and topographical aspects to identify mangrove forests in Segara Anakan, Cilacap. We used the Sentinel-2 imagery acquired in 2022. Two types of digital elevation model (DEM) data were utilized, namely the National Digital Elevation Model (DEMNAS) and Multi-Error-Removed Improved-Terrain DEM (MERIT DEM). Another critical dataset used is the Normalized Difference Moisture Index (NDMI), derived from the ratio between near-infrared (NIR) and shortwave-infrared (SWIR) bands. The classification method used is the random forest algorithm on six different dataset combinations, including IMAGE, IMAGE+NDMI, IMAGE+DEMNAS, IMAGE+MERIT DEM, IMAGE+NDMI+DEMNAS, and IMAGE+NDMI+ MERIT DEM. The results showed that the combination of Image+DEMNAS and Image+NDMI+DEMNAS datasets was able to identify the mangrove forests more optimally. Incorporating DEM data alongside IMAGE and NDMI datasets resulted in a remarkable level of accuracy in mangrove forest mapping, exceeding 90%. DEM data has a very important role in increasing the classification accuracy of mangrove forests using the random forest algorithm.
Aero Mapping Application for Affected Area Detection by the Semeru Volcano Eruption in 2022
Authors: Irawan Listyo Yudha , Prasetyo Widodo Eko , Yusuf Alfariz Maulana , Farihah Siti Nur , Hartono Rudi , Mapa Mohammad Tahir 
Published: December 2023

Abstract
The development of geospatial technology, especially Unmanned Aerial vehicles (UAV) or drones, has been growing rapidly. One of the uses of UAV is to detect disaster hazards, the extent of the affected area, and the element of risks. The latest UAV technology in the form of Aero Mapping is beneficial for mapping disaster-affected areas, in this case, post-volcanic eruption lava flows. Aero mapping with the rotary wing type is low-cost (<$ 2000/Rp.29.500.000) but a potent tool for collecting spatial data quickly and accurately. This application is applied to detect areas affected by volcanic eruptions. The photo image produced by the UAV can be used to analyze lava flows, lava deposits, changes in landforms, and changes in land cover due to volcanic eruptions. This study aims to detect the area affected by the eruption of Semeru Volcano post-eruption in 2022. In the research study area, there is no detailed scale mapping can detect the direction/path of post-eruptive lahars. This research seeks to detail the description of the affected areas. The results of aerial photographs show that low-cost UAVs are capable of producing high-resolution data of up to 2.86 cm/px (Orthomosaic), 6.94 cm/px (DSM), and 3.94 cm/px (GSD) with a total area of up to 755 Ha. This area is obtained from 34 flight processes at an altitude of 120 meters with 70% front and side overlap. These findings prove that aero mapping is a low-cost technique that can be used to map new lava flows after the 2022 eruption at Semeru Volcano.
Analysis of Determining Geoid Height Values in Urban Areas
Authors: Sari D N I , Shodiq A M , Syauqi U , Fathulhuda M S , Novianto M T 
Published: December 2023

Abstract
Floods have a significant impact on human life. To reduce the risk due to flooding, mitigation is needed. Mitigation can be done when information about the flood model has been mapped. Flood modeling requires primary data in height values with reasonable accuracy. The essential data can be obtained through INAGEOID2020. INAGEOID2020 cannot be used optimally due to a lack of information regarding its accuracy. The validation process has been carried out on 5 (five) significant islands. One is the island of Borneo, which focuses on the Kalimantan Barat and Kalimantan Timur Provinces. In the two provinces, the accuracy of 5.88 to 6.43cm. There is no information about the accuracy of INAGEOID2020 in the Kalimantan Selatan Province. So, it is necessary to validate INAGEOID2020 in that province. So, validation activities are needed to get the accuracy of INAGEOID2020 in Kalimantan Selatan. Research requires two types of data, namely primary and secondary data. Preliminary data was obtained by carrying out GNSS observations. In addition, measurements of height differences between validation points were also carried out. In addition, terrestrial gravity data is needed at the validation point as a corrector value for height difference measurements. Secondary data was obtained from the Geospatial Information Agency as validation point gravimetric undulation values. Before carrying out the INAGEOID2020 validation, a geometric undulation calculation must be carried out by utilizing the leveling and GNSS data calculation results. This article shows that the geometric undulation standard deviation value results are 0.368 m, and the RMS value is 0.336 m.
Spatial Analysis of Causative Factors for Landslide Susceptibility on Java Island
Authors: Kusuma R J , Meilano I , Sadisun I A , Fitri I H 
Published: December 2023

Abstract
Landslide is the most frequent natural disaster on Java Island with around 7300 occurrences in 1998-2023 (BNPB), causing 1807 fatalities and 45770 infrastructure damages. Landslide susceptibility modeling can be implemented as a basis for landslide risk modeling in Java Island for mitigation purposes. This research aims to model landslide susceptibility in Java Island and validate it based on historical landslide occurrence data. The methodology used for the landslide susceptibility model is based on the relationship between the distribution of landslide events and the causative factors in each of their classification class. Meanwhile, the methodology used for validation is Receiver Operating Characteristic (ROC) curve analysis, conducted by comparing the landslide susceptibility model with landslide occurrence data to obtain the area under the curve (AUC) value that shows the performance of the model. In this study, historical landslide occurrence data from two different sources, NASA and BNPB, were used to generate two separate models to see the difference of performance between both models. Each landslide occurrence data from both sources is divided into two parts; 70% of it is used to develop landslide susceptibility map and the rest is used for validation process. As many as ten causative factors were used to generate the model; elevation, slope, aspect, lithology, land cover, rainfall, river density, PGA (Peak Ground Acceleration), and distance to fault and river. The results show that based on landslide susceptibility map from BNPB data, Java Island is dominated with low susceptibility, that is about 58447 km2. Meanwhile, based on landslide susceptibility map from NASA data, Java Island is dominated by medium susceptibility, that is around 76731 km2. The performance for the models based on AUC values are 0.98-0.99 successful for NASA’s dataset and 0.91-0.92 successful for BNPB’s dataset in assessing landslide susceptibility in Java Island.
Long term subsidence rate in Pekalongan city observed by SAR Interferometry
Authors: Sidiq Teguh Purnama , Adiwijaya Raihan Fajar , Nugroho Eka Oktariyanto , Gumilar Irwan , Abidin Hasanuddin Z. 
Published: December 2023

Abstract
Land subsidence occur in some large cities in Java Island, Indonesia. Among those cities, Pekalongan suffer relatively large and consistent rate of land subsidence compared to other cities. Impact of this long-term land subsidence manifested as worsen of flood, infrastructure damage, and decline of ground water quality. To see the continuation of land subsidence in Pekalongan, we use Sentinel-1 data from 2016 to 2023 from both ascending and descending direction to spatio-temporaly mapped both magnitude and rate of the land subsidence. Persistent Scatterer InSAR (PS-InSAR) method is used to process the SAR data, also use DEMNAS and Generic Atmospheric Correction Online Service (GACOS) to reduce topographic and atmospheric error consecutively. We use CPKL CORS station, which managed by Badan Informasi Geospasial (BIG) for control point for the final subsidence rate calculation. Our result show that the area of subsidence does not change significantly for the last 7 years, with the maximum rate reach 16 cm/year of subsidence occur in the western side of Pekalongan City. However, the land subsidence also occurs in the coastal part with lower rate of 5 cm/year. The subsidence rate in most of the places are constant, with exception during 2020-2021 period.
2.5D Time Series of Postseismic Deformation Following the 2018 Mw 7.5 Palu Earthquake Using Sentinel-1 InSAR
Authors: Yusiyanti Irma , Khasanah Fina Alfi , Trihandaru Kautsar Rahtandi , Pratama Cecep 
Published: December 2023

Abstract
The observed Displacement of Line of Sight (LOS) InSAR can only measure the displacement towards and away from the satellite sensor, making it difficult to provide an intuitive depiction of the deformation source. In the previous studies, research on the characteristics of postseismic deformation mechanisms was conduct by InSAR but only in the vertical component. Therefore, our study attempts to complement this deformation picture by considering both the horizontal and vertical components. It is hoped that this will provide a more comprehensive understanding of the postseismic deformation mechanism of the 2018 Palu earthquake. In this study, we attempted to estimate the 2.5-D surface deformation resulting from the 2018 Palu Earthquake using Sentinel-1 data. The data was then modeled using exponential and logarithmic functions to understand the characteristics of the postseismic deformation mechanism. The 2.5-D surface deformation exhibited variations in horizontal and vertical motions. The East-West (EW) displacement values showed a maximum value of -66.19 mm in the east direction, while the maximum value in the west direction is 75.23 mm. On the other hand, for the Up-Down (UD) displacement, there was a maximum subsidence of -73.40 mm and a maximum uplift of 67.28 mm. To study the characteristics of the transient postseismic deformation, observations were made at 14 points. Postseismic deformation was observed at all locations in the study area. During the period 2018-2021, the time series of the north and east components of the postseismic transients were analyzed using logarithmic and exponential functions. The modeling results showed that the Root Mean Square Error (RMSE) value for the exponential model is 81.80 mm, while for the logarithmic model is 81.38 mm. Therefore, the logarithmic model demonstrated a better fit, indicating that the postseismic deformation mechanism is influenced by afterslip.
Alternative of Terrestrial Data Processing for Precise Point Positioning
Authors: Nurwinas Saepudin Muhammad Dzun , Sidiq Teguh Purnama , Prijatna Kosasih 
Published: December 2023

Abstract
Terrestrial mapping is performed on the topographic surface, but control points are typically defined using a Projected Coordinate System (PCS). Using PCS directly as control points in terrestrial data processing can cause problems in final result. They must be corrected before, due to the discrepancies between the projection plane and the ellipsoid, and between the ellipsoid and the topographic surface. To address this, we propose an alternative approach that involves defining a custom ellipsoid that intersect with the topographic surface. In practice, this approach utilizes a control point whose coordinate is defined using PCS on the custom ellipsoid. To test this concept, we conducted a calculation of an open traverse survey spanning 10 km east-west and measuring an elevation of 1000 m above the reference ellipsoid. The traverse survey included two control points at the beginning and end, and two check points every 3 km. The traverse calculation was done using four different coordinate systems, including a combination of WGS84 and custom ellipsoid for the datums, and Universal Transverse Mercator zone 48 south (UTM48S) and Transverse Mercator (TM) with custom central meridian for the projections. After the calculation, we found that the lowest linear misclosure value was produced by the combination of custom datum-custom TM. The coordinate at the check point was then evaluated and the result showed that the total linear misclosure of the combination of custom datum-custom TM was not significantly different from WGS84-custom TM, with values of only differ by less than 1mm.While the difference of linear misclosure with standard data processing differ by 8 mm. Based on this result, we can conclude that the use of proper projection system in control point coordinate definition or combine with custom datum is the best option for open traverse without control at the end.
Identification of land cover change and spatial distribution based on slope variation in java island
Authors: Mufidah S I , Virtriana R , Harto A B 
Published: December 2023

Abstract
The phenomenon of land cover change in Java Island is an important issue today because of its impact on the environment and human life. Rapid population growth in Java is one of the causes of land conversion. However, industrialization, urbanization and agricultural intensification are also major factors in land cover change. Java Island has land slope characteristics that vary from 0° to 30°. The large amount of land conversion that occurs in areas with steep slopes causes many landslides and floods in Java. The methodology used in this research is spatial analysis to identify land cover change in Java Island in 2000-2018 based on its land slope. Meanwhile, to determine the pattern of land cover change in Java Island, identification was carried out by dividing it into 3 periods with an interval of 6 years. Each land cover class that changes is determined by the land slope threshold with average central tendency statistical analysis. Each area of change that occurs in a class within its land slope class is averaged to get a reference value for its land slope threshold. Land cover change in Java Island was visualized in 3D based on land slope using Rstudio. The results showed that the phenomenon of land cover change on the island of Java is dominant on a gentle slope, which is around 0°-3°. Forest and wetland agriculture experienced the largest decrease in area. While the built-up area class experienced the largest area increase of 404300 Ha or 35.37%. Overall accuracy of this research is 73% of 400 sample points randomly scattered on the island of Java.
Analysis of Flood Inundation Mapping Using the Weighting Method in Urban Floods
Authors: Saputra A N , Nikmah  , Fitria R Wardini , Nathania W , Misna K Azizah , Ariyadi  
Published: December 2023

Abstract
Floods are one of the most destructive natural disasters globally and are projected to increase in many regions of the world. One of the areas affected by the floods is Barito Kuala Regency in South Kalimantan. Flood hazard maps and flood inundation maps are one way to identify areas prone to flooding and to find out the area of inundated areas due to flooding. Methods of scoring and weighting the parameters that cause flood inundation to map an area that has the potential for flooding. Flood inundation maps can be generated using the Normalized Difference Water Index (NDWI) method. The purpose of these two analyzes is to determine the distribution of flood hazard levels in Barito Kuala District, to evaluate flood-prone areas with flood-prone maps, and to determine the effectiveness of the two methods in identifying flood-prone areas and to determine the accuracy of the resulting flood inundation maps. Flood-prone areas were identified using the scoring method and parameter weighting to determine the distribution of flood vulnerability levels in Barito Kuala District. Parameters of flood vulnerability level used include land cover, slope, rainfall, drainage density, and soil type. Flood vulnerability level parameters were obtained from NDWI analysis on ArcGIS and ENVI. Image data during the dry season and rainy season are thresholded at <0.3 to eliminate data that is considered non-water resulting in flood inundation. The results of processing the level of flood hazard in Barito Kuala Regency are divided into 4 classes, namely the safe class with 0.5%, the less prone class is 10.11%, the moderate class is 89.37 and the vulnerable class is 0.00% of the area of Barito Kuala Regency with the majority of the areas in the moderate class. Savings District. The results of the analysis of the flood inundation map and the flood hazard map show that the moderate class of flood inundation is 72.06% of the total area of flood inundation
Identification of Land Cover Changes and its Impact on Biodiversity in Kalimantan Island
Authors: Yasminnajla Freija Maharani , Virtriana Riantini , Harto Agung Budi 
Published: December 2023

AbstractThe land cover change that happens on Kalimantan Island has the biggest contribution to forest area reduction. High population density, clean water crisis, and land carrying capacity degradation in Java Island are among the reasons for the Indonesian National Capital City relocation from DKI Jakarta to East Kalimantan. New capital city development is inseparable from land acquisition activities which can escalate land conversion. The land cover change that is already occurring, coupled with the National Capital City (IKN) development plan, are threats to biodiversity in Kalimantan. To minimize the negative impacts, it is necessary to plan for flora and fauna conservation programs in Kalimantan. This study aims to identify land cover change patterns on biodiversity on Kalimantan and analyze suitable areas for conservation sites based on ecosystem services index, topography, and accessibility. In this study, biodiversity is represented by the ecosystem services index, while the land cover change pattern on biodiversity is represented by changes in the ecosystem services index for biodiversity. Analysis of suitable areas for conservation is determined based on several criteria, (1) very high and high class of ecosystem service indexes, (2) located at more than 9 km from arterial and collector roads, and (3) considering the adjacent grid area. The ecosystem services index in high, and moderate classes has decreased since 1990 – 2020, and land cover changes that dominate this change occur in the conversion of forest class to dryland agriculture, plantations, and other vegetation with lower ecosystem service values.