Deteksi Anomali Menggunakan Local Outlier Factor (LOF) pada Kerusakan Sensor Suhu Air Conditioner (AC)
DOI:
https://doi.org/10.67712/ilka.v3i3.25Keywords:
Anomaly Detection, Temperature Sensor, Air Conditioner (AC), Local Outlier Factor (LOF), Sensor Malfuncti.Abstract
The temperature sensor in an air conditioning (AC) system plays a key role in
maintaining performance and efficiency when the cooling function is activated.
However, this sensor can sometimes fail or experience problems, leading to
inaccurate temperature readings and, consequently, improper system operation.
Therefore, having an early detection mechanism is very important to identify
potential issues. This study attempts to apply the Local Outlier Factor (LOF)
algorithm to improve anomaly detection capabilities in AC temperature sensor
data. LOF is a density-based method that identifies data points with density
significantly different from their neighbors. The dataset used in this study
consists of temperature readings collected from AC sensors over a specific period.
The process includes data preprocessing, LOF score calculation, and evaluation
using metrics such as accuracy, precision, recall, and F1-score. The results show
that LOF can detect anomalies with fairly high accuracy, making it a promising
solution for automatic AC monitoring systems, allowing faulty sensors to be
detected without the need for manual inspection.
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