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All Journal Jurnal Mediasosian : Jurnal Ilmu Sosial dan Administrasi Negara PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL)
Imam Fachruddin
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Electrical & Electronics Engineering Energy Physics Social Sciences Other

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STRATEGI PENGEMBANGAN PARIWISATA DI KABUPATEN TULUNGAGUNG Ida Gemawati Monda; Imam Fachruddin
Jurnal Mediasosian : Jurnal Ilmu Sosial dan Administrasi Negara Vol 2, No 2 (2018): September 2018
Publisher : Universitas Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (322.58 KB) | DOI: 10.30737/mediasosian.v2i2.209

Abstract

This study aims to analyze the strategies that have been implemented by the local government of Tulungagung District in developing tourism. This study uses a qualitative approach to interpret the experiences of informants related to tourism development activities. At least, the strategy that has been used is to synergize all related institutions and optimize the role of tourism villages. The inhibiting factor that emerges is the geographical location of tourist attractions that are difficult and are disaster-prone areas, while a significant supporting factor is the presence of social media that can bring tourists without requiring substantial resources.
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
Joint Prosiding IPS dan Seminar Nasional Fisika Vol. 14 No. 1 (2026): Joint Prosiding IPS dan Seminar Nasional Fisika
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1401.FA14

Abstract

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.
Co-Authors Ahmad Kadarisman Benyamin Heryanto Rusanto Hanif Andi Nugraha Ida Gemawati Monda Martarizal Santoso Soekirno