Hind Lahlali, A. Wirasatriya, E. Gensac, M. Helmi, Kunarso, Ratu Almira Kismawardhani
{"title":"Environmental aspects of tuna catches in the Indian Ocean, southern coast of Java, based on satellite measurements","authors":"Hind Lahlali, A. Wirasatriya, E. Gensac, M. Helmi, Kunarso, Ratu Almira Kismawardhani","doi":"10.1109/ISYG.2018.8612020","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8612020","url":null,"abstract":"The present work seeks to assess the relationship between daily Sea Surface Temperature (SST) and thermal front (Gradient Magnitude method) using AMSR-E, daily Sea Surface Chlorophyll-a (SSC) using MODIS, and daily long line (LL) bigeye (BG), albacore (ALB), yellowfin (YF), and southern bluefin (SBF) tuna catch data expressed as catch per unit effort (CPUE), which was calculated as the number of fish caught by 1000 hooks in 1° latitude by 1° longitude square grid, and integrated into a month of fishing activity for the period of March–December 2010 in South Java (Indian Ocean). Results obtained showed evidences of non-linear relationships between catch yields and environmental data. BG and ALB show largest CPUE values which occurred mainly in the area with SSTs of 26 – 27 °C and SSCs of 0.15 – 0.3mg/m3. CPUEs appear to be “randomly” dispersed and have a slight positive (negative) correlation with SSC (SST) i.e., 0.33 (−0.38) but not with GM values i.e., −0.02. The weak correlation between CPUE, SST, SSC and GM lead to assume that CPUEs might be linked to other influential parameters that are not assessed in the present study but are needed to give complete prediction of potential fishing ground areas, knowing that the length of observation period is less than 1 year.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115452581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Verifying Haze Dispersion from Peatland Fires using Classified Landsat 8","authors":"I. S. Sitanggang, D. Pertiwi, M. A. Agmalaro","doi":"10.1109/ISYG.2018.8611806","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8611806","url":null,"abstract":"Haze dispersion from peatland fire that influence human health can be recognized by analyzing haze trajectory pattern. Previous studies have successfully analyzed haze dispersion as the result of the simulation using HYSPLIT. However the haze dispersion have not verified and compared with the real situation. This study aims to verify haze dispersion from peatland fires in Riau Province using Landsat 8. The Landsat 8 was classified using the C5.0 decision tree algorithm. There were eight classes used in the classification which are cloud, smoke/haze, water body, shadow, built up area, burning area, burned area, and vegetation. The best classification model has the accuracy of 97.58% and kappa coefficient of 0.97. This study has successfully verified 58 haze locations in the study area. As many 98.28% of those locations were found on smoke/haze and cloud. In addition, about 41.38% of 58 haze points are located at height and pressure that are harmful for human health.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121280269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Alifdini, Ratu Almira Kismawardhani, A. Wirasatriya, D. Sugianto, O. Yaakob, A. B. Widodo
{"title":"Identification of Tidal Energy Resources using Satellite Altimetry Data for Indonesian Seas","authors":"I. Alifdini, Ratu Almira Kismawardhani, A. Wirasatriya, D. Sugianto, O. Yaakob, A. B. Widodo","doi":"10.1109/ISYG.2018.8611798","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8611798","url":null,"abstract":"FES 2014 tidal satellite altimetry data has been used to know the distribution of regions in Indonesia that have the potential to develop tidal energy. Based on the value of tidal range obtained in each region, calculation estimates the amount of energy that can be produced. Calculation of tidal energy potential has been done using tidal barrage tool design. Based on this research, Southwest of Papua Island has the highest tidal range in Indonesia of more than 5 meters, while west of Sumatera Island (in the north) and southwest of Kalimantan Island have the lowest tidal range less of than 1 meter. Based on our analysis, Bagan Siapi-api (near Halang Island), Bagan Siapi-api (near Rupat Island), North Tembilahan, South Tembilahan, Gulf of Bintuni, South East of Papua (near Arafura Sea) and South of Papua (near Arafura Sea) are the potential locations for development of tidal energy with tidal barrage device. The prediction of electrical power that can be harnessed ranged from 60.45 MW to 3719.13 MW. The highest power that can be obtained is in the area of Gulf of Bintuni and the lowest power is in the area of Bagan Siapi-api (near Halang Island). Southwest of Papua and Northeast of Sumatera are the most reliable regions to develop tidal energy.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132526462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Real-Time Landslide Monitoring System Based on 3D WebGIS and Embedded System","authors":"D. Wardana, F. Ramdani, F. Pradana","doi":"10.1109/ISYG.2018.8612028","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8612028","url":null,"abstract":"Batu City has a high potency of landslides disaster. According to data from disaster management agency of Batu City, Badan Penanggulangan Bencana Daerah (BPBD), in a range time from 1th of January, 2016 to 31th of July, 2016, it was revealed that 39 landslides gained material loss up to 2,8 billion rupiahs. The early detection system of landslide disaster installed by BPBD of Batu City currently cannot work optimally so that the decision outputs produced by the system were inaccurate. In order to solve these problems, in this study, we propose a Real Time Landslide Monitoring System Based on 3D WebGIS and Embedded System, to anticipate the landslides disaster. A prototyping-based software model development was employed to build the software model. The initial step of this study was marked by DEM data extraction and processing spatial data such as digitizing, survey data extraction, manual digitation of the building, building 3D maps, and visualizing 3D Maps into WebGIS. We employed unit testing of white-box testing and sensor accuracy as a testing method. According to unit testing results, it was shown that all components of the program unit have the desired output results by mean all features can run properly. Whereas, the sensor accuracy test results show a high degree of accuracy. In another word, the data published by the system is valid.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115980521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Ramdani, Budi Darma Setiawan, A. Rusydi, M. Furqon, Andy Gusty Rangga, I Wayan Merta Yoga
{"title":"Analysis on Spatial Distribution of Check Points for Vertical Accuracy Assessment of DTM derived from UAV","authors":"F. Ramdani, Budi Darma Setiawan, A. Rusydi, M. Furqon, Andy Gusty Rangga, I Wayan Merta Yoga","doi":"10.1109/ISYG.2018.8612002","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8612002","url":null,"abstract":"The geospatial data acquisition becomes easier than before. It is due to the availability of Unmanned Aerial Vehicle (UAV) or more familiar known as a drone. However, the accuracy is still in doubt. Especially data acquired using an optical sensor mounted on the UAV. The American Society of Photogrammetry and Remote Sensing (ASPRS) standards were used for the quality guidance. Fixed-wing type of UAV was employed with the mounted optical sensor. The final result of vertical elevation generated from the optical sensor then compared with the data of terrestrial laser scanner from two different spatial distribution of checkpoint (that is even and uneven) of two different locations (that is active and inactive) of high confidence area. The accuracy assessment found that the even spatial distribution and a higher number of checkpoints of active high confidence area lead to higher vertical elevation error, while the uneven distribution and lower number of checkpoints of inactive confidence area lead to lower vertical elevation error. This study proposed that for vertical accuracy assessment, the user need to find the inactive bare-earth confidence area for quality control of DTM derived from the mounted optical sensor on UAV.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116400198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comparative Analysis of K-Means and Isodata Algorithms for Clustering of Fire Point Data in Sumatra Region","authors":"Edo Fadila Sirat, B. Setiawan, F. Ramdani","doi":"10.1109/ISYG.2018.8611879","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8611879","url":null,"abstract":"Forest, land, or residential fire is a familiar phenomenon in Indonesia for last decade. The high number of fire incidents in Indonesia requires attention from the government so that any natural disasters such as forest fires can be resolved. These fire incidents can be analyzed since the data has already been obtained and recorded from satellite. Unfortunately, the data is too large to be analyzed as it was. Based on data obtained from the EOSDIS website, recorded as many as 289,256 fire spots occur in the region of Sumatra in the timeframe between 2001 and 2014. It needs an algorithm to segment the data or clusters the data so that large data can be processed into good information for the user. In this study, a comparative study of clustering algorithms between the K-Means and the Isodata was conducted. Both algorithms used in this study were assessed based on the quality of the clusters produced, which is calculated using Silhouette Coefficient (SC). The final result value of Silhouette Coefficient the K-Means method is 0.999997187, and the Isodata method is 0.999957161. so in this case, K-Means algorithm has a higher SC value compared to the Isodata algorithm in clustering the data of fire spots with a small SC value difference.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116978391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adhi Isti Febriandhika, Cendi Tito Rahman, F. Ramdani, Mochamad Chandra Saputra
{"title":"Tangible Landscape: Simulation of Estimation of Wildfire Spread In Arjuno Mountain Tahura R. Soerjo Region","authors":"Adhi Isti Febriandhika, Cendi Tito Rahman, F. Ramdani, Mochamad Chandra Saputra","doi":"10.1109/ISYG.2018.8611830","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8611830","url":null,"abstract":"Forest fires is a condition in which forests are hit by flames, causing damage of forests and or forest products that cause economic losses and or environmental value. May occur naturally or prescribed, and until now the spread of fire during forests fires has not been predicted with certainty, because there is much variability in fire triggering factors. East Java is a province in Indonesia that often occurs forest fires. One of the forest areas that often occur forest fires in East Java Province is Tahura R. Soerjo area. Moreover, forest fires are one of the most challenging issues handled by Tahura R. Soerjo in managing forest areas. Moreover, until now the technology to overcome the problem of the forest is still very minimal. The Tangible Landscape method can be used to simulate the possibility of spreading a fire that occurs in real-time. By using Tangible Landscape and GRASS GIS, multiple fire simulation scenarios can be performed, and experimenting the determination of making different fire breaks on physical models to evaluate their effectiveness in overcoming the spread of fires","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114323325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oki Ari Saputra, F. Ramdani, Mochamad Chandra Saputra
{"title":"Comparison Analysis of Google Maps, Wisepilot, and Here Wego With User-Centered Design (UCD) Approach & Cartography","authors":"Oki Ari Saputra, F. Ramdani, Mochamad Chandra Saputra","doi":"10.1109/ISYG.2018.8611815","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8611815","url":null,"abstract":"Application testing is measured from the user’s point of view by highlighting five aspects of testing, which are functionality, effectiveness, efficiency, reliability, usability, and cartography. This research aims to make a comparison between Google Maps, Here Wego, and Wisepilot applications on the Android operating system as a better alternative that should be used in searching locations on maps and navigation routes through user satisfaction testing. Applications in this test are used in the recommended navigation routes and locations in Malang which requiring several types of testing on each application. For each application is tested and evaluated by five aspects of testing using Web Quality Evaluation Method (WebQEM) method and cartography rules. The writer has analysed and made a comparison of test results that were conducted on each aspect based on the standard assessment categories in each aspect tested. The Google Maps application is superior to other three applications based on four of the five aspects tested: functionality, effectiveness, efficiency, reliability, usability.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115315679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Erosion Risk Modelling Using Geographic Information System in Beringin Watershed, Semarang City","authors":"A. Nugraha, H. S. Firdaus, B. Sasmito","doi":"10.1109/ISYG.2018.8611949","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8611949","url":null,"abstract":"One of the main environmental problems that can have an impact on natural, agricultural and environmental resources is soil erosion. During the last years there has been a growing awareness of the importance of problems directly related to erosion in almost Indonesian watershed region. Beringin Watershed is a watershed that became the center of the hydrological cycle in Ngaliyan and Mijen districts. Approach in watershed management is related to erosion threat that can be done by doing in handling and management of watershed. Management of soil conservation, erosion control and management of water catchment areas can be presented in geospatial information that contains spatial information and quantitative data from information on soil erosion. In this study, the risk map model of erosion in Beringin watershed mainly based in expert’s knowledge (AHP) and the RUSLE model which is considered to be a contemporary simple and widely used approach of soil erosion assessment. The risk mapping resulted in high class threatening 1.309%, medium grade 48.183%, and 50.508% of the total area of Beringin Watershed. The high-risk areas are located in Subdistrict of Beringin, Kedungpane, Pesantren, and Ngaliyan.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132985263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Calculation of Atlantic Waters Inflow and Polar Front Position in the Barents Sea with Long-Term Data on Kola Transect","authors":"I. Zaporozhtsev, D. Moiseev","doi":"10.1109/ISYG.2018.8611861","DOIUrl":"https://doi.org/10.1109/ISYG.2018.8611861","url":null,"abstract":"In paper the developing of geoinformation technology for estimation of Atlantic-origin waters inflow and polar front parameters is presented. It is based on common issues, best practices, modern theoretical studies, heuristics and software development tools applied to research in the Barents Sea. Main hydrographic datasets are long-term in situ measurements values stored in World Ocean Database and in database of Murmansk Marine Biological Institute. Spatial distribution is investigated in details in the areas of Kola Transect. Some results concerning seasonal and inter-annual variability of salinity are described and prospects of research are formulated.","PeriodicalId":310717,"journal":{"name":"2018 4th International Symposium on Geoinformatics (ISyG)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133997571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}