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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Yohanes Sardjono
BRIN Jogjakarta
Computer Science & IT

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Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection Hanna Arini Parhusip; Suryasatriya Trihandaru; Bambang Susanto; Adrianus Herry Heriadi; Petrus Priyo Santosa; Yohanes Sardjono; Johanes Dian Kurniawan
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 02 (2024): Vol. 15, No. 2 August 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i02.p01

Abstract

This research addresses a critical issue in industrial environments: air quality, specifically regarding PM 1.0 and PM 2.5. High concentrations of these particles pose significant health risks. The study measures temperature, humidity, pressure, altitude, PM 1.0, and PM 2.5 and shows the effectiveness of using AIOT-Particle devices to analyze these features with Principal Component Analysis (PCA). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to detect anomalies during the observation period. Anomalies occur when the altitude ranges from 65 to 70 units, according to PM 1.0 and PM 2.5 values. The positions where anomalies occur are illustrated based on altitude, temperature, pressure, and concentration. The results demonstrate that altitude dominates as the first feature. Finally, the research concludes that altitude, PM 1.0, and PM 2.5 are the dominant features. The study confirms the effectiveness of PCA and recommends using these three features for anomaly detection in DBSCAN. Overall, the research highlights the novelty and success of AIOT-Particle in industrial environments.
Co-Authors Adrianus Herry Heriadi Bambang Susanto Hanna Arini Parhusip Johanes Dian Kurniawan Petrus Priyo Santosa Suryasatriya Trihandaru