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All Journal International Journal of Advances in Applied Sciences Al-Fiziya: Journal of Materials Science, Geophysics, Instrumentation and Theoretical Physics Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan BERKALA FISIKA IJOG : Indonesian Journal on Geoscience Techno: Jurnal Penelitian Eksplorium : Buletin Pusat Pengembangan Bahan Galian Nuklir JLBG (Jurnal Lingkungan dan Bencana Geologi) (Journal of Environment and Geological Hazards) INDONESIAN JOURNAL OF APPLIED PHYSICS Jurnal Pengabdian Masyarakat MIPA dan Pendidikan MIPA Jurnal Meteorologi Klimatologi dan Geofisika Journal of Physics and Its Applications Jurnal Teori dan Aplikasi Fisika Journal of Engineering and Scientific Research (JESR) Jurnal Geosaintek Eksplorium : Buletin Pusat Pengembangan Bahan Galian Nuklir
Wiji Raharjo
Geophysical Engineering, Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : indonesian journal of applied physics

 1 
Pemodelan Tsunami Sederhana dengan Menggunakan Persamaan Differensial Parsial Indriati Retno Palupi; Wiji Raharjo; Eko Wibowo; Hafiz Hamdalah
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 8, No 1 (2018): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v8i1.16284

Abstract

One way to solve fluid dynamics problem is using partial differential equation. By using Taylor expansion, fluid dynamics can be applied simply. For the example is tsunami wave. It is include to hyperbolic partial differential equation, tsunami wave propagation can describe in space and time function by using Euler FTCS (Forward Time Central Space) formula.
Estimate The Focal Mechanism of Earthquake in Indonesia By Using 1-D Convolutional Neural Network (CNN) Indriati Retno Palupi; Wiji Raharjo; Oktavia Dewi Alfiani; Dessy Apriyanti; Dwi Wahyuningrum
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 15, No 2 (2025): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v15i2.84593

Abstract

Indonesia is located between three collisions of active plate tectonics (Pacific, Eurasia, and Australia), resulting in a high seismicity zone, especially along the subduction zone. Besides the subduction zone, there are also many faults as a result of these collisions. As the earthquake source, both are controlled by focal mechanisms. Focal mechanism is the geometry of fault movements. Unfortunately, Indonesia's earthquake catalog data is not complete. There is missing information in some focal mechanism data, especially the data with more than 6 Magnitudes between January 1st, 1973, and February 1st, 2023. To complete the focal mechanism data, 1-D Convolutional Neural Network (CNN) is applied as the common and powerful method of Machine Learning. Started by grouping the earthquake catalog data with clear focal mechanism information as the training data with its training label and otherwise as the test data with the unknown label, then applied these training and label data to convolutional layer with some neurons, CNN can estimate focal mechanism (label) of the test data. This process is done iteratively, and a good model is observed with little loss value in the L curve.
Estimate Mass Density Value as A Priori Information for Gravity by using Bayesian Markov Chain Monte Carlo (MCMC) Indriati Retno Palupi; Wiji Raharjo
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 2 (2024): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i2.77622

Abstract

In the gravity method, information about mass density value is very important because it will influence the characteristic of the 1-D gravity acceleration graph. However, it is quite difficult to guess the mass density value so that is suitable to the 1-D acceleration graph. This is called “a priori” information. Trial and error way is one way to solve this problem. It is a very random value guess also. To make sure that the initial guess of mass density is a good parameter, Bayesian Markov Chain Monte Carlo (MCMC) can be used. It generates many possibilities from the guess value and then these possibilities will be selected to the best one by likelihood way. The validation is expressed by the random graph as a consequence of the iteration number step of the possibilities. This research is started by using certain values of mass density to create a synthetic model for the field data in Banggai Sula because the area has a complex geology. The synthetic model is used because the gravity forward modelling equation has the sinusoidal form. After Bayesian MCMC is applied to the initial mass density value, it will produce a new mass density value or the estimation value with its response to the 1-D gravity acceleration synthetic graph. Finally, this information will be very useful to create the 2D or 3D inverse modelling in Gravity.
Co-Authors Achmad, Rahim Afriliani M Handini Agus Santoso Ajimas Pascaning Setiohadiwibowo Alfiani, Oktavia Dewi Ananda Achlaqul Karimah Anifah, Eka Masrifatus Arif Nor Aminah Aswan, Marwis Ayu Utami, Ayu Bentang Anggarajati Dessy Apriyanti Dwi Wahyuningrum Edoarddo Tobing Ekaristi, Kevin Gardo Bangkit Eko Wibowo Fahri, Ricky Al Fandi Budi Stiawan Faris Ahad Sulistyohariyanto Giamboro, Wrego Seno Indriati Retno Palupi Indriati Retno Palupi Indriati Retno Palupi Indriati Retno Palupi, Indriati Retno Intan Paramita Haty Isnaini, RR Desi Kumala Jayawarsa, A.A. Ketut Joko Soesilo Kevin Gardo Bangkit Ekaristi Kristanto, Wisnu Aji Dwi Madona, Madona Marwis Aswan MUHAMMAD FAHMI Muhammad Nafian Nela Elisa Dwiyanti Oktavia Dewi Alfiani Oktavia Dewi Alfiani Oktavia Dewi Alfiani Oktavia Dewi Alfiani Rahayu, Mey Yani Puji Rahim Achmad Rohima Wahyu Ningrum Rohima Wahyu Ningrum, Rohima Wahyu Sahetapi, Calvin Alex Soesilo, Joko Stiawan, Fandi Budi Sulistyohariyanto, Faris Ahad Supriyono Supriyono Sutanto Sutanto Sutanto Sutanto Syamputra, Dhani Nur Indra Tri Wulan Sari, Tri Wulan Vira Irnanda, Elleona Septi N Y Yatini