ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING

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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Main Author: Zaky Erdiansyah, Muhammad
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/86943
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:86943
spelling id-itb.:869432025-01-07T10:15:42ZANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING Zaky Erdiansyah, Muhammad Indonesia Theses Smart Meter, Advanced Metering Infrastructure (AMI), K-Means, AHP, Machine Learning, PT PLN (Persero), COVID-19. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/86943 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. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description 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.
format Theses
author Zaky Erdiansyah, Muhammad
spellingShingle Zaky Erdiansyah, Muhammad
ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING
author_facet Zaky Erdiansyah, Muhammad
author_sort Zaky Erdiansyah, Muhammad
title ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING
title_short ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING
title_full ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING
title_fullStr ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING
title_full_unstemmed ANALYSIS OF CLASSIFICATION AND PRIORITIZATION OF SMART METER AMI IMPLEMENTATION AREAS USING MACHINE LEARNING
title_sort analysis of classification and prioritization of smart meter ami implementation areas using machine learning
url https://digilib.itb.ac.id/gdl/view/86943
_version_ 1822999739334918144

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