SISTEM INFORMASI GEOGRAFIS PENENTUAN SEBARAN LISTRIK DESA KABUPATEN SINTANG

Deassy Kartika Kresna

Abstract


Rural electric distribution program is
one of the steps that taken by the PT. PLN to
divide the electricity to all the remote areas in
Indonesia. Rural electrification program was
also conducted in the area Sintang, so that all
rural areas in Sintang can be electrified. In rural
electric distribution we calculated the group of
villages that should become a list of priorities.
Determination of the villages should be
prioritized based on several indicators, namely
indicators of the number of potential customers,
power demand, power demand projections, as
well as the distance to the village which will
supply power to villages. Mapping and
geographic distribution of management power is
needed to map the distribution of village with
rural electric into a form of spatial data, and
analysis needed in the process of determining the
priority of rural electric distribution. This
research aims to map and manage the
distribution of electricity in villages Sintang into
spatial data and analyze it so it can produce a
priority list of villages in the rural electric
widening. Analysis of composite prioritization is
done by using PCA (Principal Component
Analysis) and the analysis of Clustering to
reduce the variables / indicators determining
priorities and grouping the results of PCA
analysis that can determine the priority of the
village. This system can manage tabular and
spatial data of rural electric that exist in
Sintang, and display them in the form of maps
(spatial data). The system can analyze and
generate electricity distribution priorities based
on rural electric in the selected villages. The
priority result of rural electric is calculated
based on indicators of potential customers,
power demand, power demand projections and
also the distance to rural electric in the selected
villages , as well as priority by all indicators
(composite) are displayed in tabular and spatial
data. 


Keywords


rural electric, mapping, spatial data, analysis, PCA, clustering, composite, reduction, indicators, identification, priority

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References


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