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All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya Aceh International Journal of Science and Technology
Ferdiyan, Akmal
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Physics

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CROSS SECTION OF ELECTRON ANTINEUTRINO INTERACTION WITH 40AR AND 84KR AND ITS RELEVANCE TO GEONEUTRINO DETECTION Ferdiyan, Akmal; Prabowo, Urip Nurwijayanto
Jurnal Neutrino:Jurnal Fisika dan Aplikasinya Vol 13, No 1 (2020): October
Publisher : Department of Physics, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/neu.v13i1.10602

Abstract

Neutrino can carry information from places that cannot be reached by the usual detection mechanism because it has a very weak interaction with matter. This can be utilized to study the heat flow process inside the earth by using information carried by geoneutrino (electron antineutrino). In this sense, it is important to know the characteristics of neutrino interaction with materials. In this study, the cross-section calculation of the electron antineutrino interaction with Ar-40 and Kr-84 was carried out using computational methods with the help of GENIE software. In the energy range of 0-10 MeV, the dominant interaction between the two materials is the interaction of QES NC and MEC types with an energy threshold of 5,09 MeV. Both Ar-40 and Kr-84 cannot be used as a scintillator material for geoneutrino detection because in the energy range 0-4,4 MeV the cross-sectional value of the CC interaction  is 0.
Comparison Of Facies Estimation Using Support Vector Machine (SVM) And K-Nearest Neighbor (KNN) Algorithm Based on Well Log Data Prabowo*, Urip Nurwijayanto; Ferdiyan, Akmal; Raharjo, Sukmaji Anom; Sehah, Sehah; Candra, Arya Dwi
Aceh International Journal of Science and Technology Vol 12, No 2 (2023): August 2023
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.2.28428

Abstract

Facies classification is the process of identifying rock lithology based on indirect measurements such as well log measurements. Usually, the facies are classified manually by experienced geologists, so it takes a long time and is less efficient. In this paper, two machine learning (Support vector machine and K-Nearest Neighbor) were adopted to increase the effectiveness and shorten the time process of facies classification in Z Field, Indonesia. The machine learning algorithm was carried out in 4 steps, i.e. data selection, training phase, verification, and validation stage. The machine learning input data are density log, gamma ray log, resistivity log, SP log; and the output facies target are Sandstone, Siltstone, Claystone, and Limestone. The data is divided into train data for the training process and test data to validate the machine learning output. In Support vector machine results, the training accuracy is 70.1% and the testing accuracy is 47.4%, while in KNearest Neighbor results, the training accuracy is 70.1% and the testing accuracy is 63.3%. This result showed K-Nearest Neighbor has better accuracy than the support vector machine in facies classification in the Z field.
Co-Authors Arya Dwi Candra Prabowo*, Urip Nurwijayanto Sehah Sehah Sukmaji Anom Raharjo Urip Nurwijayanto Prabowo, Urip Nurwijayanto