ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING
This study aims to address the imbalance in determining priority units for implementing smart meter Advanced Metering Infrastructure (AMI) at PT PLN (Persero) UIW Bangka Belitung. Current demographic and geographic-based methods often fail to reflect actual operational needs, leading to inefficie...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/86943 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | This study aims to address the imbalance in determining priority units for
implementing smart meter Advanced Metering Infrastructure (AMI) at PT PLN
(Persero) UIW Bangka Belitung. Current demographic and geographic-based
methods often fail to reflect actual operational needs, leading to inefficiencies in
resource allocation and budgeting.
This research proposes a machine learning-based approach using K-Means for
clustering and Analytic Hierarchy Process (AHP) for cluster prioritization. The data
analyzed includes the number of clear tampers, meter replacements, and customer
violations from 2016 to 2023, divided into four periods: overall data, pre-COVID19 (2016–March 2020), during COVID-19 (March 2020–March 2022), and postCOVID-19 (April 2022–December 2023).
The results demonstrate that this method provides more accurate, data-driven
recommendations for prioritizing units across different periods. This approach is
expected to enhance resource allocation efficiency, accelerate AMI implementation,
and improve customer trust in digital transformation within the energy sector.
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