Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection
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 effectivene...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Udayana University, Institute for Research and Community Services
2024-07-01
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| Series: | Lontar Komputer |
| Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/109995 |
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| author | Hanna Arini Parhusip Suryasatriya Trihandaru Bambang Susanto Adrianus Herry Heriadi Petrus Priyo Santosa Yohanes Sardjono Johanes Dian Kurniawan |
| author_facet | Hanna Arini Parhusip Suryasatriya Trihandaru Bambang Susanto Adrianus Herry Heriadi Petrus Priyo Santosa Yohanes Sardjono Johanes Dian Kurniawan |
| author_sort | Hanna Arini Parhusip |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c226e12a52f5459286b8343d6f6a01a8 |
| institution | Kabale University |
| issn | 2088-1541 2541-5832 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Udayana University, Institute for Research and Community Services |
| record_format | Article |
| series | Lontar Komputer |
| spelling | doaj-art-c226e12a52f5459286b8343d6f6a01a82025-01-31T23:56:26ZengUdayana University, Institute for Research and Community ServicesLontar Komputer2088-15412541-58322024-07-011502758610.24843/LKJITI.2024.v15.i02.p01109995Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly DetectionHanna Arini Parhusip0Suryasatriya Trihandaru1Bambang Susanto2Adrianus Herry Heriadi3Petrus Priyo SantosaYohanes Sardjono4Johanes Dian Kurniawan5Master of Data Science Departement, FSM /UKSWMaster of Data Science FSM UKSWMaster of Data Science FSM UKSWPT Artha APuncak Semesta Indonesia (APSI)BRIN JogjakartaMaster of Data Science FSM UKSWThis 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.https://ojs.unud.ac.id/index.php/lontar/article/view/109995 |
| spellingShingle | Hanna Arini Parhusip Suryasatriya Trihandaru Bambang Susanto Adrianus Herry Heriadi Petrus Priyo Santosa Yohanes Sardjono Johanes Dian Kurniawan Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection Lontar Komputer |
| title | Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection |
| title_full | Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection |
| title_fullStr | Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection |
| title_full_unstemmed | Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection |
| title_short | Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Anomaly Detection |
| title_sort | density based spatial clustering of applications with noise dbscan and principal component analysis pca for anomaly detection |
| url | https://ojs.unud.ac.id/index.php/lontar/article/view/109995 |
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