bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early Detection of Forest and Land Fires Using Hotspots Data from S-NPP VIIRS Satellite Imagery Recording in the New Capital City Location
Authors: Kumalawati R , Yuliarti A , Raharjo J T , Rijanta  , Susanti A , Saputra E , Budiman P W , Pratomo R A , Murliawan K H , Danarto W P , Muhtar G A , Anggraini R N 
Published: December 2023

Abstract
Forest and land fires are a natural disaster whose frequency is increasing annually, especially during dry season, forest and land fires also impact the new capital city in East Kalimantan as well. Early detection is very critical to prevent increasing impact of forest and land fires. Early detection can be done using hotspot data from satellite image recording. Based on this, it is very necessary to conduct research entitled Early Detection of Forest and Land Fires Using Hot Spots from “S-NPP VIIRS Image Records in the New Capital City Location”. The research was conducted in the new capital city location in East Kalimantan Province which consists of several sub-districts and 10 districts / cities. Descriptive quantitative method is used to analysed secondary data consist of hotspot data in each sub-district from satellite imagery data record namely S-NPP VIIRS from 2012-2022. Population number is same as the number of samples, namely hotspots number in each sub-district. Descriptive geostatistical analysis technique in ArcGIS software is used to process hotspot data. It is known that from 2012-2022, Kutai Kartanegara has the highest hotspots number with 38,694 spots, it followed by East Kutai with 36,915 spots, both are viewed from district/city scope. When viewed on the district scope, the highest was in Bengalon district from East Kutai District with 9,027 spots, it followed by Muarakaman District from Kutai Kartanagara District with 7,713 spots. The number of hotspots varies quite a lot amongst sub-district in the new capital city location, whose each have a numerous number that needs serious attention in order to minimize the loss of life and property. From analysis above, early detection is very important as a form of early warning system.
Leveraging Time-Series Imageries and Open Source Tools for Enhanced Land Cover Classification
Authors: Hadi Firman , Sabri Laode Muhammad , Prasetyo Yudo , Sudarsono Bambang 
Published: December 2023

Abstract
The urgency of regularly updated land cover information is crucial for monitoring the dynamics of regional landscapes. Current land cover classification techniques are typically based on snapshots taken on specific dates; therefore, potential variations over time are ignored. This limitation is exacerbated in regions that require land cover classification professionals. To address these challenges, we propose the use of existing innovative and open-source tools. This study presents a comparative analysis of two methods: the first uses machine learning classification using Google Earth Engine and the second uses time-series imagery using R’s sits package. Both methods were tested to classify land cover in Sragen Regency, Central Java, Indonesia, and our results demonstrated the superiority of R’s sits package in terms of accuracy and reproducibility. This facilitated the acquisition of a more precise and comprehensive land cover classification and demonstrated a robust application. The sits package also benefits from being open -source, encouraging wider accessibility and collaboration in the scientific community. The implications of these results underscore the potential of leveraging time-series imagery and open-source tools such as the sits package in R for more accurate and reproducible land cover classification. This approach can significantly improve our ability to monitor and understand the dynamics of regional landscapes, particularly in areas with limited skilled human resources. Future research should refine these techniques and examine their applicability to different geographic contexts.
Monitoring of Air Quality Changes using Landsat 8-9 Data in 2018-2022
Authors: Putri Arimurti Adinda Sitaresmi , Sukojo Bangun Muljo 
Published: December 2023

Abstract
Environmental management is an effort made by the government to preserve environmental functions and prevent environmental pollution and/or damage which includes planning, utilization, control, maintenance, supervision, and law enforcement. The implementation of the Menuju Indonesia Hijau program by the Ministry of Environment aims to utilize vegetation cover and mitigate climate change due to air pollution. Monitoring of air quality cover can be carried out periodically to produce accurate information and take policies or actions to preserve the environment and minimize negative impacts on the environment. Changes in air quality can occur due to land conversion from vegetation land to non-vegetation land. This land conversion can be caused by forest and land fires as well as the construction of new industrial areas or factory areas. The process of mapping air quality changes in the 2018-2022 period will be processed and analysed. Air quality mapping is done by utilizing Landsat 8-9 satellite imagery to calculate SO2, and NO2 concentrations. SO2 and NO2 values can be performed with linear regression with temperature values obtained from LST calculations from Landsat 8-9 imagery. From the two data it is possible to analyse the changes in air quality and produce maps Air Pollutant Standard Index every year in the 2018-2022 period. The use of cloud computing in this study can improve performance effectiveness in the big data processing process. The average value of air quality in Ngawi regency shows good values in 2018 and 2022 and moderate to unhealthy in 2019 to 2021. The decline in air quality is due to forest fires that occurred at the end of 2018 and 2019 and is caused by the density of urban areas centralized in the city centre and will result in a lot of pollution from vehicles which can trigger high concentrations of Air Pollutant. There are 23 industries operating in Widodaren Sub-regency that have the highest scores in the Widodaren Sub-regency in 2019, in 2018, 2020-2022, the Widodaren Sub-regency also occupies the 3 Sub-regencies with the highest Air Pollutant Standard Index scores.
Analysis of Land Cover and Building Damage Due to The 21th November 2022 Cianjur Earthquake using Sentinel-2
Authors: Prayogo Michael Antonie 
Published: December 2023

Abstract
The Cianjur region is an area that has the potential for tectonic earthquakes caused by fault movements. The Cianjur earthquake that occurred on November 21, 2022 was caused by the emergence of a new fault, namely the Cugenang Fault. Judging from the depth of the earthquake, which was around 10km, earthquake activity in Cianjur district was included in the shallow category with a magnitude of 5.6 which claimed around 600 lives and resulted in more than 56,000 damaged housing units.Therefore monitoring is needed to see the impact of the Cianjur earthquake on changes in land cover and changes in vegetation and built-up land in Cianjur district. This monitoring utilizes remote sensing technology or remote sensing using Sentinel-2 satellite imagery data. The data obtained will later be analyzed and classified using the Supervised Classification method to obtain land cover maps before and after the earthquake, and the use of the NDBI index algorithm to see the density of buildings or settlements in areas affected by the earthquake. The data that has been collected from Sentinel – 2 data is in July 2022 and January 2023.After the Supervised Classification was carried out using the Maximum Likelihood method, land cover maps were obtained for the earthquake-affected areas before and after the earthquake. The accuracy test results were also carried out on the results of this classification and the results obtained were 90.11% accuracy. From the results of land cover, it can be seen that there was a decrease in the area of built-up land and vegetation land after the earthquake. An analysis of taking the NDBI index value has been carried out on Sentinel-2 satellite imagery which has been cropped with the area of built-up land. Classification of each NDBI class has been carried out to see the density of buildings in the earthquake affected areas. From the results of the analysis, it was found that there was a decrease in density from before the earthquake and after the earthquake. Then a calculation of the estimated number of damaged buildings has been carried out using the enumeration method on the NDBI value, especially for built-up land. After obtaining the results from the enumeration analysis, they are compared with data on building damage in the field. The comparison results show an accuracy of 76.45%.
Classification Of Earthquake Observation Stations Using Multiple Input Convolutional Neural Network Method
Authors: Hakim Arif Rachman , Saputro Adhi Harmoko , Rohadi Supriyanto , Adi Suko Prayitno , Karnawati Dwikorita 
Published: December 2023

Abstract
Analysis of the data quality of earthquake observation stations becomes very important as quality control. However, determining the quality of earthquake observation stations is done manually by analyzing several existing parameters by an expert. This study proposes a deep learning method approach to classify the quality of earthquake observation stations based on the expert’s ability to analyze the quality of earthquake observation station data. The method used is Multiple Input Convolutional Neural Network (MICNN). The proposed MICNN architecture development is a combination of previous research. The dataset used in this study results from recording 411 seismometers for 12 months with a data set containing three horizontal components (East and North) and the vertical component (Z) with a recording length of 30 days. Each component is transformed from the time domain into the frequency domain form to obtain a spectrogram image used as input in the MICNN model. In addition, each component has a CNN block that functions as an extraction feature on the spectrogram image. Classification in this study used three classes: Classified, Usable, and Unusable. Tests and experiments on the model were carried out to obtain the best accuracy by tuning hyperparameters for model validation and the accuracy of this MICNN model was 99%.
Identification of Active Faults in Lahat and Empat Lawang Region, South Sumatra Province, Based on Seismicity and Focal Mechanism Analysis
Authors: Kurniawan Telly , Daniarsyad Gatut , Gunawan Mohamad Taufik , Sembiring Andry Syaly , Dwi Sriyanto Sesar Prabu , Rohadi Supriyanto , Karnawati Dwikorita 
Published: December 2023

Abstract
The BMKG earthquake catalog data for the 2014-2022 period shows seismic activity in the Gumai Mountains, South Sumatra, which is located southeast of the Ketaun Segment, east of the Musi Segment, and north of the Manna Segment. Seismic activity in this area is important to study as an early indication of the presence of other active fault segments that have not been identified before. Re-analysis on the P and S wave phases picking was carried out for the 26 earthquakes in the period of 2014 to 2022 to ensure the quality of the data used in the processing. The double-difference method was then used to relocate the hypocenter of the earthquake based on a local velocity model to identify an active fault segment lineation in the area. In addition, waveform inversion method was used to obtain the focal mechanism for 7 earthquakes which were successfully analyzed to identify the fault plane direction. By combining the results of seismicity and focal mechanism analysis, we concluded that there are 3 fault segments that have not been identified before. These segments are thought to be strongly responsible for seismic activity in Gumai Mountains in Lahat and Empat Lawang Regencies, South Sumatra, which we determine to be part of the Great Sumatran Fault network.
Optimizing Seagrass Detection in Lampung Selatan: Leveraging Machine Learning Algorithms and Sentinel-2A Imagery
Authors: Simarmata Nirmawana , Wikantika Ketut , Darmawan Soni , Nadzir Zulfikar Adlan 
Published: December 2023

Abstract
Seagrass beds are one of the coastal ecosystems that play an important role in maintaining the stability of blue carbon. However, high community activities threaten the existence of seagrass beds themselves. South Lampung Regency is one of the areas with considerable seagrass potential but the availability of distribution and density data is still minimal. This research aims to identify and map seagrass density as a first step for seagrass management. The data used in this research is Sentinel 2A multispectral image. Machine learning-based classification methods used are random forest (RF) and support vector machine (SVM) because these algorithms have a good ability to distinguish objects based on their features. This study uses a 2-level classification scheme, where level 1 consists of land, shallow sea, and deep-sea classes. Level 2 is the shallow marine bottom benthic habitat class. The type of seagrass found in this area is Enhalus acoroides. Based on the results of the analysis, low, medium, and high-density classes were obtained with an area of low around 20.12 ha, medium around 34.67 ha and high around 320.12ha with a total area of 374.91ha. RF has a higher overall accuracy of 88.00% while SVM accuracy is 84.00% so it can be concluded that Sentinel 2A images can be used to detect seagrass meadows.
Predicting cetacean habitats beyond surveyed regions in Indonesian waters: species distribution model transferability may not always be preferable
Authors: Sahri Achmad , Maters Floor , Putra Mochamad I H , Mustika Putu L K , Kreb Danielle , van Lammeren Ron J 
Published: December 2023

Abstract
Understanding the distribution of cetaceans in Indonesian waters is imperative for their conservation management, however such information is lacking for the country. Our study predicted the species distributions of two cetacean species (common bottlenose dolphin and sperm whale) beyond the surveyed regions in Indonesian Exclusive Economic Zone (EEZ). This was done by using a combination of presence-only data, randomly generated pseudo-absences and environmental predictors variables within the Biomod2 framework in R. Ten potential predictor variables were identified, of which five were selected after correlation tests. Local Random Forest models were built to the extent of four small study regions, and later projected to the whole Indonesian EEZ. The common bottlenose dolphin local models showed preference for areas close to the coast and shallower waters. Sperm whale local predictions were located further into the open waters and at deeper waters. The extrapolated predictions into the Indonesian EEZ, however, showed some unexpected results. The high occurrences for common bottlenose dolphins were not only located close to the islands, but also more into open waters. In contrast, sperm whale distributions have high occurrences near coastal areas and in the vicinity of islands than in the open oceans. This information suggested that the transferability of species distribution models may not always be preferable, because provide low accuracy. Sighting data, choices of variables and model settings influenced the outcome of the extrapolated models. Despite the unpreferable of the extrapolations, the results are still beneficial for cetacean conservation purposes, since the study was able to identify potential habitats in unsurveyed regions.
Seabed Sediment Classification using Multifrequency Backscatter
Authors: Syariz Muhammad Aldila , Fauzy Miftakhul 
Published: December 2023

Abstract
Seabed sediment serves as an important metric for assessing marine ecosystems. Different sectors, including the development and utilization of marine resources, marine engineering, environmental research, marine fisheries, and maritime navigation safety, all depend on the classification and identification of different forms of seabed sediment. Hydrographic surveys employing Multibeam Echosounders provide information that can be used to categorize the types of seabed sediment. The data acquired during the hydrographic surveys using Multibeam Echosounders include bathymetric data and backscatter data. The backscatter data is obtained from the use of Multibeam Echosounder equipment for bathymetric data acquisition. The backscatter data from Multibeam Echosounders can be utilized for determining the classification of seabed sediment as well as for identifying seabed features. This backscatter data represents the types of sediment on the seafloor and can be used for seafloor profiling and acoustic backscatter analysis to determine the several kinds of seafloor sediment. This study uses multi-frequency mosaic backscatter data and an Artificial Neural Network (ANN) approach to classify the different types of seabed sediment in the area of Benoa Port in Bali Province. The research aims to produce a distribution map of sediment types in the study area. A training accuracy of 55% and a testing accuracy of 66% were obtained from the DNN modelling. Three out of six sediment types were identified based on these accuracy results: silty sand, gravelly sand, and sandy silt with gravel corals. According to their distribution, gravely sand had the smallest distribution (5%), while silty sand had the biggest (77%).
Fishing Ground and Primary Production Analysis Based on DNB SNPP-VIIRS and Aqua-MODIS Imagery in Mentawai Island and Nias Waters
Authors: Prayogo Teguh , Harsa Hastuadi , Sulma Sayidah , Yati Emi , Marpaung Sartono , Kushardono Dony 
Published: December 2023

Abstract
The study of fishing grounds in coastal areas is part of fisheries management activities, especially capture fisheries by coastal fishermen. The absence of GPS, AIS and VMS systems on traditional fishing boats causes difficulties in monitoring the fishing grounds (FG) of coastal fishermen in the waters of Nias Island and Mentawai Islands. The objectives of this study to assess the fishing ground area and analyse its seasonal variability based on fishing light boats (FLB) from the DNB SNPP-VIIRS satellite imagery and the primary productivity condition in FG area from Aqua-MODIS imagery in the Nias Island and Mentawai Islands waters. FG is determined based on the clustering point density method, and statistical correlation analysis. Based on the FLB imagery (2015-2020), peak season fishing operation occurred twice a year in January/February and June/July, while the lowest fishing season occurs in April and October. In general, there are four main locations of fishing grounds in the study area, i.e Sibolga waters, Batu Islands, Mentawai Island and West Sumatra coastal. The FLB can describe the location of FG spatial and temporally, and it was correlated with the bathymetry and chlorophyll-a concentration rather than primary production in the waters of Nias Island and Mentawai Islands. Spatial and temporally distribution the fishing grounds and its density change by seasonal and inter-annual variations. The lowest conditions occurred in 2016 and the highest in 2019 which were each influenced by the nIOD and pIOD phenomena.
Seabed Sediment Classification using Multi-Frequency MBES Bathymetric and Its Features
Authors: Khomsin  , Mukhtasor  , Suntoyo  , Pratomo Danar Guruh , Hudaya Ahmad Ilmi 
Published: December 2023

Abstract
Principally, for a few decades, hydrographical surveys have been only to determine the depth of seawater. Sonar measurement tehcnology is the most widely used to conduct hydrographical surveys employing a singlebeam echosounder (SBES) and multibeam echosounder (MBES). In addition to depth information, seafloor sediment distribution maps are essential for port planning and management. In general, the distribution of seafloor sediments is predicted using backscatter data from SBES and MBES at single and multiple frequencies. The bathymetric data generated by the echosounder can be derived into several bathymetric features such as slope, ruggedness, roughness, aspect, bathymetric position index (BPI), and curvature. This study examines the possibility of using bathymetric measurement and bathymetric derivation of multi-frequency MBES to predict the distribution of seafloor sediments, especially in the harbor pond area. The study used a deep neural network (DDN) to classify the distribution of seabed sediments with bathymetric and bathymetric features input, validated with 74 in situ sediment samples (clayey sand, silt, sandy silt, and silty sand). Up to 75% of data sample sediments are used for training and 25% for validation. The classification results by DNN showed 42.6% clayey sand, 7.4% sandy silt, 46.7% silt, and 3.35% silty sand. The overall accuracy (AO) and Kappa classification of seabed sediments with DDN were 59.5% and 0.54 (moderate), respectively.
Identifying Potential Areas for Oil and Gas Well Location Planning Using Support Vector Machine Algorithm
Authors: Dwi Safira Rizka Amelia , Nurwatik Nurwatik , Hariyanto Teguh 
Published: December 2023

Abstract
To fulfill national energy needs for the National Energy Grand Strategy (GSEN), it is necessary to increase the productivity of oil and gas exploration by involving technology that provides alternative solutions, cuts work time, and overcomes the risk of failure. This research aims to identify potential areas for planning new oil and gas well locations using a machine learning algorithm called Support Vector Machine (SVM). This research chooses four splitting ratios of 80:20, 75:25, 60:40, and 50:50 on training and testing data to produce four models and to identify the most robust model for Blora Regency. The algorithm involves fourteen conditioning parameters comprising altitude, slope, aspect, distance from the river network, land cover, distance from the road network, soil type, Normalized Difference Vegetation Index (NDVI), clay mineral index, iron oxide index, surface temperature, complete Bouguer anomaly (CBL), distance from the fault, and rock type. This research uses the confusion matrix and the ROC-AUC to evaluate all models and determine the best one. The result witnesses the best model is SVM 75:25 with an accuracy (Acc), sensitivity (Sen), specificity (Spe), and predictive value (PPV) of 0.8333; Matthew’s correlation coefficient and Cohen’s kappa of 0.6667; and area under the curve (AUC) of 0.9444. In addition, the conditioning parameter contributing the most significant influence on the best model is the slope equal to 100%.