Web System for Online and Onsite Usage of Geoinformation by Surveying Sector in Kosovo. Case Study: Ferizaj Municipality

Idrizi, Bashkim; Kurteshi, Mirdon

doi:10.19184/geosi.v4i3.13469

Cited by 2 publications

(4 citation statements)

References 11 publications

(12 reference statements)

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“…On the other hand, according to chang, it means a computer system consisting of hardware, software, data, human, organizational components used to capture, store, search, analyze, and display geospatial data. Meanwhile, geospatial data describes the location and attributes of spatial features [6] [18]. There are some of GIS applications that have been carried out, especially in the agricultural sector, include land suitability, solar agriculture suitability detect critical land [19] [20].…”

Section: Gographic Information System (Gis)mentioning

confidence: 99%

“…This is what motivated us to create a suitability superior commodities base on Geographic Information System (GIS), specifically for agriculture and plantations. GIS is a system designed to capture, store, and manipulate, analyze, and manage all types of geographic data or in other words a combination of cartography, statistical analysis, and data-based system technology [6] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

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Georaphic Information System of Land Suitablility for Commodities Featured in Sawang District North Aceh

Saptari

Trisna

2021

Int. J. Eng. Scie. and Inform. Technology.

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Actually, Sawang is the largest sub-district in North Aceh, ±38,465 ha or 11.67% of the total area of North Aceh. Consists of thirty nine villages. 3,449 ha are paddy fields and 35,016 ha are non-paddy fields. The use of non-rice field land is usually used by the surrounding community for gardens, forests, and ponds of 27,997 ha. In order to support government programs in improving people's welfare and creating job opportunities, it can be done by developing the potential of agriculture and plantations. Therefore, integrated and complete information is needed regarding the characteristics of land in North Aceh, especially Sawang Sub - District, which is suitable for planting certain types of plants in support of optimal and sustainable land use policies. The purpose of this study is to analyze the nature of the land that is suitable for use as an agricultural area or plantation of superior commodities. The specific purpose of the research is to provide recommendations to local governments to determine policies for the use of vacant land with appropriate plant types. The research steps include preliminary studies, identification of superior commodities, mapping of vacant land, testing of land suitability parameters, determination of land suitability, policy recommendations. Based on the results of the research that has been carried out, the researchers conclude that cocoa is considered the most appropriate plant among several other options, referring to the results of several parameters and observations that have been made, either through spatial studies based on mapping techniques, using special software, or the study of chemical elements, as a support for plant growth, through the results of laboratory tests that have been carried out by the research team.

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Section: Gographic Information System (Gis)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Georaphic Information System of Land Suitablility for Commodities Featured in Sawang District North Aceh

Saptari

Trisna

2021

Int. J. Eng. Scie. and Inform. Technology.

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“…K-Means Clustering Algorithm is a technique in Data Mining that can partition data into several clusters so that data with the same characteristics will be grouped into one cluster while data with different characteristics will be grouped into other clusters. K-Means performs two processes, namely the process of determining the center of the cluster (centroid) and the process of finding members from each cluster and requires k input parameters or the number of clusters [14] [11] [15]. Previous research using the K-Means Clustering Algorithm was carried out by Miftahul (2021) who examined the implementation of the K-Means Algorithm for Clustering of Participants in the National Science Olympiad at the High School Level which resulted in C1 there were 12 highly competent students to take part in OSN, in C2 there were 14 competent students.…”

Section: Introductionmentioning

confidence: 99%

“…but have not been able to take part in OSN and in C3 2 students are less competent to take part in OSN. Ari Sulistiyawati's research (2021) examines the implementation of the Kmeans Clustering Algorithm in the Determination of Superior Class Students, the result is that the implementation of the K-Means Clustering algorithm into the clustering information system provides the results of an effective data grouping classification and the process of each iteration of the centroid distance rotation, point determination clusters are formed, student data as a reference object saves more time on clustering superior classes [16] [17] [15]. Surohman's research (2019) on the use of the K-Means Algorithm in measuring the correlation between student profiles and grades using data on the 2019 National Examination scores at one of the Vocational High Schools in Jakarta obtained the results of the K-Means algorithm that can be used in grouping students based on social aspects.…”

Section: Introductionmentioning

confidence: 99%

Information and Communication Technology Competencies Clustering For Students For Vocational High School Students Using K-Means Clustering Algorithm

Faisal

Nurdin

Fajriana

et al. 2022

Int. J. Eng. Scie. and Inform. Technology.

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The k-Means Clustering algorithm is intended to partition data into one or more groups, where data that has similarities in one group and data has differences in another. Information and Communication Technology (ICT) Competency data clustering in educational units is considered necessary to facilitate educational facilitation based on the differences in student abilities, determine advanced ICT guidance groups and become a reference in determining the place of Industrial Work Practices (Prakerin). This study aims to find out how the K-Means Clustering algorithm can be applied in clustering the ICT competencies of students at the State Vocational High School (SMK) 3 Lhokseumawe. The benefits generated in this study are in the form of visualization of data clustering that can help teachers and school management in formulating ICT policies at SMKN 3 Lhokseumawe. The data used in this study is the Information and Communication Technology (ICT) competency test score data for the 2021/2022 academic year. The data was obtained through a competency test process that refers to the Minister of Education and Culture Regulation Number 45 of 2015 concerning the Role of ICT/KKPI Teachers in the Implementation of the 2013 Curriculum where ICT competence includes the skills to search, store, process, present and disseminate data and information. Data processing in this study uses the K-means Clustering method and the RapidMiner application. Data processing using the RapidMiner application starts with data preparation, determining the number of clusters, and configuring the method. This study uses 3 (three) cluster configurations, namely the Very Competent, Competent, and Less Competent clusters. Testing data processing using the RapidMiner application resulted in 80 (eighty) students in cluster_0 with a Very Competent rating, 64 (sixty-four) students in cluster_1 with a Competent rating, and 10 (ten) students in cluster_2 with a Less Competent rating.

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Web System for Online and Onsite Usage of Geoinformation by Surveying Sector in Kosovo. Case Study: Ferizaj Municipality

Cited by 2 publications

References 11 publications

Georaphic Information System of Land Suitablility for Commodities Featured in Sawang District North Aceh

Georaphic Information System of Land Suitablility for Commodities Featured in Sawang District North Aceh

Information and Communication Technology Competencies Clustering For Students For Vocational High School Students Using K-Means Clustering Algorithm

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