THE MODELING SEMIVARIOGRAM OF GOLD DATA IN CLUSTERING HIERARKI GROUP (CASE STUDY: GOLD PROSPEK DATA IN ACEH)
Mining process includes many locations. In statistics, the location can be viewed as an index parameter. And in each of these locations can be measured a random variable, so that rows of random variables included in the process of spatial. To measure influenced between two observations separated by...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/21159 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Mining process includes many locations. In statistics, the location can be viewed as an index parameter. And in each of these locations can be measured a random variable, so that rows of random variables included in the process of spatial. To measure influenced between two observations separated by a distance ? can be used semivariogram analysis semivariogram calculated and matched to the experimental semivariogram with theoretical semivariogram. This semivariogram matching process through several models, among others Model Exponential, Gaussian, Spherical, Cubic, and Pentaspherical. In this thesis, the author uses data gold prospects in Aceh, which consists of 202 objects with 34 variable elements including Au, Ag, Cr, and etc. Therefore, the observed variables quite a lot so I reduce the variables using principal component analysis (PCA). At this stage the ten major components obtained by the cumulative proportion of 74.53%. Furthermore, to overcome the number of objects that were observed to use clustering hierarchy (CH). From this process, selected two groups to be semivariogram modeling. From the results of PCA and CH modeling on its main components which have the highest weight of gold. Modeling obtained in Group 1 and Group 4 for the main component ????7. |
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