K-means clustering visualization of web-based OLAP operations for hotspot data

Sitanggang, Imas Sukaesih; Fuad, Tsamrul; Annisa, Annisa

doi:10.1109/itsim.2010.5561296

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Data warehouse yang diintegrasikan dengan aplikasi Online Analytical Processing (OLAP) telah dibangun untuk mengelola data histori titik panas [6]. Selanjutnya modul clustering titik panas dibangun pada aplikasi Web-Based OLAP [7]. Data sosial ekonomi diintegrasikan pada aplikasi Spatial OLAP untuk titik panas [8].…”

Section: Pendahuluanunclassified

Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan

Ramdhany¹,

Sitanggang²,

Kurniawan³

et al. 2021

RESTI

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To prevent and handle forest and land and forest fire (karhutla), the Ministry of Environment and Forestry assembled a patrol team that conducts a daily task to observe directly to the hotspot location as an indication for land fire. Currently, the patrol team reported the investigation result into a group chat. This method consumed many storage spaces and not suitable for formal reporting. This study aims to develop a front-end module for a web GIS application that visualizes the patrol team's daily report. The application has its data recapitulation method and able to create a formal report. The data used in this study are a set of the report that collected in 2016 by Sumatera and Kalimantan patrol team. The steps to build this application include communication, integrate with the API from the back-end system, developing functional needs, software testing, and the last is software release. The application was build using HTML and CSS for its interface and Javascript and API from the back-end module for its content management. The system uses Google Maps services and library to support the functionalities of the application. The unit testing method's test result shows that the module runs well and can afford all of the required functionality. In addition, the system testing result that the ratio between actual error and expected error is equal to 1. This result indicates the functions of the system are working properly according to the use cases of the system.

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Section: Pendahuluanunclassified

Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan

Ramdhany¹,

Sitanggang²,

Kurniawan³

et al. 2021

RESTI

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show abstract

“…These techniques have been applied on different domains. For example, in [16] authors applied K-means as indicator for forest fires occurrence in Indonesia. Also, DBSCAN has been used to detect changes in the distribution pattern of hotspots and land cover of peat land in Sumatra.…”

Section: Related Workmentioning

confidence: 99%

“…The information is incomplete, because it is what witnesses remember from the moment they were kidnapped; so any data about the addresses are important to be stored. For example, for the secuestro table of the ASQ system, in Figure 2 we can see the lugar secuestro 16 The other tables selected, also contains missing information and/or wrong formats to be analyzed. So, the output of this phase involved the specific attributes of each table that must be considered, together with the associated problems.…”

Section: Application Of the Data Analysis Processmentioning

confidence: 99%

“…In this work, we only show the design needed to clean and reformat the information of the secuestro tables; however more designs for the other tables have been also performed. The design can be observed in Figure 3, in which we apply filters, 15 captivity 16 kidnap place Figure 2: Data stored in the ASQ system about the lugar secuestro attribute aggregations, joins and loads for formatting and improving addresses. For the designs we use the approach defined in [5].…”

Section: Application Of the Data Analysis Processmentioning

confidence: 99%

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Experiences from a Data Analysis of Crimes against Humanity

Buccella

Manrique

Troncoso

et al. 2021

JCS&T

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Data analysis is a widely researched field, where innumerable applications allow to discover domain particularities that are specially useful. In this paper, we introduce the data analysis process that we applied to two different systems storing information about statements and testimonies of crimes against Humanity. We describe the activities, design decisions and lessons learned from implementing a specific goal, which involves transforming text data into georeferenced information.

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Spatial Online Analytical Processing for Hotspots Distribution Based on Socio-economic Factors in Riau Province Indonesia

Thariqa

Sitanggang

2015

Procedia Environmental Sciences

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K-means clustering visualization of web-based OLAP operations for hotspot data

Cited by 5 publications

References 7 publications

Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan

Modul Front-End Sistem Informasi Geospasial Patroli Terpadu Kebakaran Hutan dan Lahan

Experiences from a Data Analysis of Crimes against Humanity

Spatial Online Analytical Processing for Hotspots Distribution Based on Socio-economic Factors in Riau Province Indonesia

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