Perbandingan Algoritma Deep Q-Network dan Local Outlier Factor Untuk Deteksi Anomali Konsumsi Air Minum Pelanggan PUDAM Kabupaten Banyuwangi

Andhika Ahnaf Daniswara; Basuki Rahmat; Eva Yulia Puspaningrum

doi:10.61132/mars.v2i4.243

Authors

Andhika Ahnaf Daniswara Universitas Pembangunan Nasional “Veteran”
Basuki Rahmat Universitas Pembangunan Nasional “Veteran”
Eva Yulia Puspaningrum Universitas Pembangunan Nasional “Veteran”

DOI:

https://doi.org/10.61132/mars.v2i4.243

Keywords:

Non-Revenue Water, Anomali, Deep Q-Network, Local Outlier Factor, IQR

Abstract

Adequate provision of drinking water in quantity, quality, and continuity is needed to realize a healthy and productive society. A well-managed Drinking Water Supply System (SPAM) is essential to meet this need. Based on Government Regulation Number 122 of 2015, the implementation of SPAM involves the development and management of drinking water which is the responsibility of the local government and PUDAM as the implementer. The main challenges faced by PUDAM include the high level of water loss or Non-Revenue Water (NRW), which reaches 40% in Indonesia. One of the efforts to reduce the NRW level at PUDAM Banyuwangi Regency in the Kalipuro District area is to detect abnormal consumption in customer drinking water consumption. This study uses the Deep Q Network and Local Outlier Factor algorithms to detect anomalies in drinking water consumption, with the aim of comparing the performance of the two algorithms in identifying abnormal consumption patterns at PUDAM Banyuwangi Regency. The results of the study indicate that the Local Outlier Factor algorithm is more suitable for anomaly detection as evidenced by the absence of detection errors and an F1-Score value of 36%.

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