bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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

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

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

The 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.
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

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

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

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

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

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

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

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

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

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

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

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

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%.
GIS-driven Hydro Energy Potential Estimation for Technoeconomic Assessment of Hydro Power Plant in Bawean, Gresik
Authors: Maftuh Eep Saeful , Nst Zul Khoiron , Wahyuono Ruri Agung , Julian Miga Magenika
Published: December 2023

This study aims to assess the spatial distribution of hydropower capacity on Bawean Island using a Geographic Information System (GIS). The objective of this study is to provide a valuable contribution to the progress of sustainable energy transition initiatives in Indonesia. The primary objective of the hydropower map is to enhance the process of strategic planning and implementation of hydropower infrastructure, with a particular emphasis on supplying electricity to rural regions. The essential components required for the successful implementation of hydropower are the head, which represents the vertical distance between the water source and the water outflow, which refers to the rate at which water is discharged from the system. Bawean Island, located in the eastern region of Java, possesses a unique topographical structure distinguished by an abundance of rivers, making it a promising site for the establishment of hydropower infrastructure. The utilization of ArcGIS was applied for the aim of hydropower mapping. The estimation of water discharge was conducted using the Soil Conservation Service-Curve Number (SCS-CN) approach. The current investigation utilized the Multi-Criteria Decision Analysis (MCDA) framework, which incorporated the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) to ascertain the most advantageous choice of hydropower plant types for deployment in the Bawean region. The classifications comprise pico-hydro, micro-hydro, mini-hydro, and large-scale hydropower installations.The Multi-Criteria Decision Analysis (MCDA) framework incorporates various criteria, including cost, policy, social concerns, technology assistance, integration, and natural catastrophe considerations. The results indicate that the hydropower potential on Bawean Island peaks in January, coinciding with the rainy season, while it is at its lowest in August, corresponding to the dry season. Moreover, the spectrum of hydropower installations suitable for Bawean Island includes pico-hydro systems, which have a power generation capacity of up to 5 kW. Additionally, mini-hydro plants are categorized into two classes: class A, with a power generation capacity ranging from 5 to 100 kW, and class B, with a power generation capacity ranging from 100 to 200 kW. The utilization of Multi-criterion Decision Analysis (MCDA) reveals the results of the techno-economic assessment, emphasizing the importance of cost, social factors, and policy as the key criterion for decision-making. The factors mentioned above are allocated weights of 0.44, 0.17, and 0.17, correspondingly. Pico-hydro is a suggested category of hydropower plants that are being considered for the development of hydropower infrastructure in the Bawean region.
Classification Analysis of Industrial Buildings Using Orthophoto and NDSM Data with Deep Learning Approach (Case Study: Kali Rungkut Village, Surabaya)
Authors: Raihan Muhammad Anis , Rachmadi Reza Fuad , Bimanjaya Alfian , Handayani Hepi Hapsari
Published: December 2023

Information of industrial estates is important because it may increase the economy level, industrial goods production, and export activities. In addition, information of industrial areas needs to be identified so that the area does not interfere with agricultural productivity, natural resources, and cultural heritage. To obtain this information, extraction and classification of building footprints using orthophoto data with a deep learning approach is carried out. However, this has the challenge that the condition of the building is highly diverse in both shape and size, so it requires additional data such as height data form (NDSM) to facilitate its identification. The Mask Region-based Convolutional Neural Network (Mask R-CNN) method used for extraction produces 88.49% precision accuracy; 66.82% completeness (recall); and 76.15% F1-score. The classification model performed with the Deep Neural Network (DNN) method, produced excellent accuracy with average values of precision, recall, and F1-score of 0.94; 0.90; and 0.92, respectively.
A review on the performance of non-additive blended fuel (ethanol-gasoline) in spark ignition engines
Authors: Alimin A.J , Babalola O.K
Published: December 2023

The use of effective fuel additives can improve fuel and engine efficiency, but some non-additive or incompatible additives can cause engine problems. Ethanol-gasoline blends offer advantages as an alternative to regular gasoline, but high-dose blends with non-additives can lead to issues such as deposits in the engine, increased combustion temperature, emissions, and power loss. This paper reviews various fuel additives and their impacts on engine performance. Early anti-knock additive based on organometallic compounds were used in the past but faced concerns regarding cost, legislation, health effects, and environment. Ethanol and other oxygenated additives have been used to increase research octane numbers, improve combustion and fuel economy. While ethanol has benefits of improved efficiency and reduced emissions, it is not considered an additive. Therefore, finding a chemical additive that enhances fuel efficiency, combustion, and reduces greenhouse gas emissions is crucial. This research aims to explore and provide solution in this area.