Early Detection of Seismic Signal Anomalies Using Raspberry Pi 5 and Lightweight Machine Learning Models - Ahmad Kadarisman; Imam Fachruddin; Santoso Soekirno; Hanif Andi Nugraha; Benyamin Heryanto Rusanto; Martarizal (Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI, 2026)
Data integrity is crucial for seismic monitoring systems, but is often compromised by anthropogenic or instrumental anomalies. This paper proposes a lightweight edge computing framework using Raspberry Pi 5 for real-time anomaly detection. MiniSEED data from the high-noise TOJI station were processed through segmentation, statistical or spectral feature extraction, and unsupervised models (isolation forest and autoencoder). The results show a detection latency of 78-113 ms with minimal resource consumption (<35% CPU, <200 MB RAM) and 82% correlation with ground-truth anomalies. This framework can be used on networked seismographs with limited resources such as those of the BMKG.