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...
Saved in:
Main Authors: | Hanna Arini Parhusip, Suryasatriya Trihandaru, Bambang Susanto, Adrianus Herry Heriadi, Petrus Priyo Santosa, Yohanes Sardjono, Johanes Dian Kurniawan |
---|---|
Format: | Article |
Language: | English |
Published: |
Udayana University, Institute for Research and Community Services
2024-07-01
|
Series: | Lontar Komputer |
Online Access: | https://ojs.unud.ac.id/index.php/lontar/article/view/109995 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning
by: Ratu Anggriani Tangke Parung, et al.
Published: (2024-10-01) -
Principal component analysis and its generalizations for any type of sequence (PCA-Seq)
by: V. M. Efimov, et al.
Published: (2020-01-01) -
PRINCIPAL COMPONENT ANALYSIS (PCA) OF THE ACTIVITIES OF INFORMAL CONSTRUCTION WORKERS/ARTISANS IN NIGERIA
by: Sunday Julius Odediran, et al.
Published: (2014-03-01) -
Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection
by: Lu Haowen
Published: (2025-01-01) -
Penentuan Dua Lokasi Lumbung Padi dengan Menggunakan Metode Grid di Provinsi Kalimantan Tengah
by: Yunita Puput Wijayanti, et al.
Published: (2019-12-01)