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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
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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Earthquake Hazard Mapping Based on Earthquake Intensity Model in North Maluku Islands Ningrum, Rohima Wahyu; Achmad, Rahim; Aswan, Marwis; Raharjo, Wiji
TECHNO: JURNAL PENELITIAN Vol 13, No 2 (2024): TECHNO JURNAL PENELITIAN
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/tjp.v13i2.9737

Abstract

Earthquake hazard mapping in the North Maluku region is urgent and essential because this area has a very high earthquake hazard potential. The high level of earthquake vulnerability in the North Maluku region makes it very important to focus mitigation activities to reduce the impact of earthquakes that occur. Earthquake hazard mapping that illustrates the effects of earthquakes on an area is one of the disaster mitigation efforts. In this study, earthquake intensity using the Probability Seismic Hazard Analysis (PSHA) method will be used to analyse the level of earthquake hazard in the North Maluku region. The results of the distribution of Peak Ground Acceleration (PGA) values for North Maluku range from 0.67 - 3.75 g. At the same time, the value of the MMI earthquake intensity scale is in the range of IX-XII. The most incredible earthquake intensity is in the Morotai, West Halmahera, Ternate, Tidore, Bacan, and Obi Island areas. The results of this study can have implications as a reference in safer spatial and infrastructure planning, the preparation of more effective mitigation strategies, and can be a medium for educating the public to be more prepared and responsive to earthquake risks.
Subsurface S-type Granitoid Identification Based on Gravity and Seismic Tomography Models in Pacitan, East Java Soesilo, Joko; Palupi, Indriati Retno; Raharjo, Wiji; Sutanto Sutanto; Sulistyohariyanto, Faris Ahad; Ekaristi, Kevin Gardo Bangkit; Stiawan, Fandi Budi
EKSPLORIUM Vol. 39 No. 2 (2018): NOVEMBER 2018
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17146/eksplorium.2018.39.2.4954

Abstract

Granitoid outcrop has been observed in Montongan, Tulakan Subdistrict, Pacitan District, East Java. Geochemically, granitoid shows peralluminous S-type granitoid which consists of comparable plagioclase and potassium feldspar leading to adamelite and granodiorite variety with andalusite, fine size corundum and cordierite inside. These modal minerals are consistent with its bulk chemical analysis result that shows alumina rich rock. Highly weathered spotted pinkish soil with remaining quartz gravels characterizes its surface. Lateritic pink soil up to more than 25 meters thick covers the granitoid body and this feature is indicative to locate its surface distribution, while its subsurface distribution is remain uncertain. The research aimed to identify granitoid subsurface distribution. To identify the subsurface body, gravity and seismic tomography models were used. According gravity model, the pluton body is 5 km wide which is rootless downward and seems extends eastward. Meanwhile, the north-south seismic tomographic model across Pacitan Region indicates dense solid body override the recent Java subduction zone. The body is assumed to have correlation with surface granitic rock. It supports an idea that there is a micro continent trapped beneath Southern Mountain of East Java.
The Spatial Distribution of Petroleum Hydrocarbon Contamination in Groundwater Around Fuel Storage Tank Utami, Ayu; Sahetapi, Calvin Alex; Rahayu, Mey Yani Puji; Kristanto, Wisnu Aji Dwi; Raharjo, Wiji; Isnaini, RR Desi Kumala; Fahri, Ricky Al; Anifah, Eka Masrifatus
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 22, No 3 (2025): November 2025
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/presipitasi.v22i3.782-791

Abstract

Groundwater is vital for domestic, agricultural, and industrial use; however, previous studies have indicated that its quality often fails to meet drinking water standards. The sources of groundwater contaminants can be from domestic, industrial, saltwater intrusion, surface waste ponds, pipelines, mine pits, underground storage tanks, waste pits, etc. This research investigates the spatial distribution of Total Petroleum Hydrocarbons (TPH) contamination in groundwater surrounding fuel storage tanks, using the LeGrand method to assess groundwater vulnerability based on five physical environmental parameters. The study employs a quantitative approach, incorporating primary data from well measurements and secondary data from geological and land use maps. The results reveal that shallow groundwater levels significantly increase vulnerability to contamination, while the type of soil and aquifer permeability also play critical roles in contaminant transport dynamics. In the second research location, the analysis focuses on benzene contamination, with low concentrations below 0.02 ppb. Despite the low levels detected, the potential for contamination remains a concern due to the proximity of the gas station to residential areas. Statistical correlation analysis demonstrates a significant inverse relationship between TPH concentrations and vulnerability scores. The study underscores the importance of preventive measures to mitigate contamination risks, involving collaboration among stakeholders. 
Studi Automatic Picking Waktu Tiba Gelombang P dan S dengan Menggunakan Spektogram pada Obspy Python Indriati Retno Palupi; Wiji Raharjo
Jurnal Teori dan Aplikasi Fisika Vol. 8 No. 1 (2020): Jurnal Teori dan Aplikasi Fisika
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtaf.v8i1.239

Abstract

Application of K-means clustering and B-value algorithms for analysis of earthquake-dangerous zones in Java Island Bentang Anggarajati; Y Yatini; Wiji Raharjo
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp907-915

Abstract

Java Island is an island with a high earthquake vulnerability. Therefore, earthquake mitigation measures are needed to reduce the impact of earthquakes. Earthquake mitigation is done by knowing the zones with a high risk of earthquakes and high levels of rock stress. The methods used to map earthquake-prone zones are K-means clustering and B-value. The K-means clustering method can provide earthquake clusters based on their characteristics and the B-value can produce rock stress conditions in the area. The results of this study are that the K-means clustering method produces 7 earthquake clusters with 5 classifications of very low, low, medium, high, and very high. In contrast, the B-value process has a high B-value with a value of 1.2-1.5 in West Java and a low B-value with a value of 0.9-1.2 in the central to the eastern part of Java.
Estimate Mass Density Value as A Priori Information for Gravity by using Bayesian Markov Chain Monte Carlo (MCMC) Palupi, Indriati Retno; Raharjo, Wiji
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.
Pemodelan Tsunami Sederhana dengan Menggunakan Persamaan Differensial Parsial Palupi, Indriati Retno; Raharjo, Wiji; Wibowo, Eko; Hamdalah, Hafiz
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) Palupi, Indriati Retno; Raharjo, Wiji; Alfiani, Oktavia Dewi; Apriyanti, Dessy; Wahyuningrum, Dwi
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.
Co-Authors Achmad, Rahim Afriliani M Handini Agus Santoso Ajimas Pascaning Setiohadiwibowo Alfiani, Oktavia Dewi Ananda Achlaqul Karimah Anifah, Eka Masrifatus APRIYANTI, DESSY Arif Nor Aminah Aswan, Marwis Ayu Utami, Ayu Bentang Anggarajati Dwi Wahyuningrum Edoarddo Tobing Ekaristi, Kevin Gardo Bangkit Eko Wibowo Fahri, Ricky Al Fandi Budi Stiawan Faris Ahad Sulistyohariyanto Giamboro, Wrego Seno hamdalah, hafiz Indriati Retno Palupi, Indriati Retno Isnaini, RR Desi Kumala Jayawarsa, A.A. Ketut Joko Soesilo Kevin Gardo Bangkit Ekaristi Kristanto, Wisnu Aji Dwi Madona, Madona MUHAMMAD FAHMI Nafian, Muhammad Nela Elisa Dwiyanti Oktavia Dewi Alfiani Rahayu, Mey Yani Puji 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