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All Journal PROSIDING SEMINAR NASIONAL Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Berkala Fisika Indonesia : Jurnal Ilmiah Fisika, Pembelajaran dan Aplikasinya Journal of Aceh Physics Society Science and Technology Indonesia Indonesian Journal of Science and Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Physics: Theories and Applications Prosiding SNFA (Seminar Nasional Fisika dan Aplikasinya) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JPF : JURNAL PENDIDIKAN FISIKA Surya Abdimas Jurnal Pendidikan Fisika dan Teknologi Indonesian Journal on Learning and Advanced Education (IJOLAE) Indonesian Review of Physics (IRiP) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian Kepada Masyarakat Patikala Jurnal Ilmiah Kampus Mengajar Journal of Novel Engineering Science and Technology Bincang Sains dan Teknologi
Azmi Khusnani
IKIP Muhammadiyah Maumere
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Journal : Indonesian Review of Physics (IRiP)

 1 
Utilization of Seismic Data as a Tsunami Vulnerability Review Azmi Khusnani; Adi Jufriansah; Mulya Afriyanto
Indonesian Review of Physics Vol. 5 No. 2 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/irip.v5i2.6706

Abstract

This study aimed to analyze seismic data, which is then made into an infographic to map the level of tsunami hazard in the Sikka District. The research was carried out in Sikka District, East Nusa Tenggara, located between 121°55'40''-122°41'30'' east longitude and 08°22'-08°50' south latitude. The data source comes from the IRIS Earthquake Browser, and the analysis stage was carried out in two phases. The first analysis used seismic data analysis, and EQ Energy used IRIS (Incorporated Research Institutions for Seismology) data. Meanwhile, the second analysis maps the tsunami risk by determining the tsunami hazard in areas with the potential for a tsunami. Based on the analysis of seismicity data showed that Sikka District has the potential for an earthquake accompanied by a tsunami. In contrast, the results of the EQ Energy analysis caused by the December 14, 2021 earthquake were known as the value of Ehf = 6.46 × 1014 J  and EBB = 5.48 × 1015 J. The analysis of the level of tsunami susceptibility based on the tsunami run-up height in Sikka District showed that the northern coastal area of ​​Flores had various potentials, where the highest vulnerability level was in the Alok subdistrict and parts of Talibura. Meanwhile, the area with the lowest potential was the Kewapante subdistrict.
Estimation of Flores Sea Aftershock Rupture Data Based on AI Adi Jufriansah; Azmi Khusnani; Yudhiakto Pramudya; Mulya Afriyanto
Indonesian Review of Physics Vol. 6 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/irip.v6i1.6705

Abstract

The earthquake catalog notes that there have been earthquakes with Mw > 7 that hit the Flores area, three of which occurred in the Flores Sea in 1992, 2015, and 2021. Revealed that the seismic activity of Eastern Indonesia is thought to be influenced by the isolated thrust fault segment of the island of Flores and the island of Wetar. The study of the rising fault segment on Flores Island and Wetar Island helps in further understanding the fault behavior, earthquake pattern, and seismic risk in the Flores Sea region. In earthquakes with giant magneto, an aftershock can occur due to the interaction of ground movements. This research analyzes and compares the data from the evaluation of the classification algorithm and the regression algorithm. The initial stages of this research include requesting IRIS DMC Web Service data. The data is then subjected to a cleaning process to obtain the expected feature extraction. The next stage is to perform the clustering process. This stage is carried out to label dependent data by adding new features as data clusters. The following procedure divides the validation value, which consists of training and test data. The estimation results show that the classification algorithm's evaluation value is better than that of the regression algorithm. The evaluation value of several algorithms indicates this, with an accuracy rate between 80% and 100%.
Co-Authors Ade Anggraini Adi Jufriansah Anis Rohadatul Niehlah Anyela Aprianti Arief Hermanto Arief Hermanto Arief Hermanto Colomeischi Aurora Adina Danurdara Setiamukti Dedi Suwandi Wahab Dian Ahdiany Donuata, Pujianti Bejahida Endang Sulastri Erwin Prasetyo Fitri Nur Hikmah Hamzarudin Hikmatiar hikmatiar, hamzarudin Irfan Miftahul Fauzi Ishafit Ishafit Isma Alip John Ayuba Kartika Dewi Rahmawati Konsenius Wiran Wae Kristina Theresia Leto M Taufiqurrahman Moh. Toifur Mohammad Fitri Mohammad Fitri Mohammad Fitri Muhammad Ihsan Mulya Afriyanto Mulya Afriyanto Mulya Afriyanto Mulya Afriyanto Nurdin H. Abd. Rahman S. Nursina Sya'bania Ota Welly Jenni Thalo Ota Welly Jenni Thalo PRASETYO, ERWIN Rahmawati Husein Rizalul Fiqry Sabarudin Saputra Sabarudin Saputra Saharul Saharul Saharul Tria Puspita Sari Wahyuningsih, Wahyuningsih Yanto Yanto Yoman Ribeta Ratu Yohakim Yudhiakto Pramudya Yudhiakto Pramudya Yudhiakto Pramudya Yunita Kristianti Subandi Zulfakriza Zulfakriza