Email this article 
Pendekatan Neural Network pada Gerak Unstretch Bungee Jumping Menggunakan Metode Euler
Abstract
Persamaan gerak unstretch bungee jumping dapat diselesaikan secara numerik menggunakan metode Euler. Hasil penyelesaian secara numerik menggunakan Euler dapat dikatakan valid apabila metode Odeint mendapatkan hasil yang hampir sama dengan metode Euler. Setelah hasil dari metode numerik tervalidasi, sehingga dapat implementasikan pada metode neural network. Neural network dapat divariasi menggunakan berbagai epochs untuk memprediksi hasil penyelesaian secara numerik. Dengan epochs 500 didapatkan hasil yang lebih akurat dan mendekati dari hasil odeint.
Kata kunci: Bungee Jumping, Solusi Numerik, dan Neural Network
Full Text:
PDFReferences
Menz P G 1993 The physics of bungee jumping Phys Teach 31(8) p 483–487 doi: 10.1119/1.2343852.
Heck A and Uylings P 2010 Understanding the physics of bungee jumping [Online] Available: www.iop.org/journals/physed
Heck A and Uylings P 2010 Understanding the physics of bungee jumping [Online] Available: www.iop.org/journals/physed
Kagan D and Kott A 1996 The greater-than- g acceleration of a bungee jumper Phys Teach 34(6) p 368–373
Heck A and Uylings P 2020 A Lagrangian approach to bungee jumping Physics Education 55 0250099
Lawson D A 1995 Potential Perils of Euler’s Method
Biswas B N, Chatterjee S, Mukherjee S P, and Pal S 2013 A Discussion On Euler Method: A Review [Online] Available: http://ejmaa.6te.net/
Abiodun O I, Jantan A, Omolara A E, Dada K V, Mohamed N A, and Arshad H 2018 State-of-the-art in artificial neural network applications: A survey Heliyon 4 p. e00938
Kloeden P E and Pearson R A 1977 The numerical solution of stochastic differential equations The Journal of the Australian Mathematical Society. Series B. Applied Mathematics 20(1) p 8–12
Brown S D, Ratcliff R, and Smith P L 2006 Evaluating methods for approximating stochastic differential equations J Math Psychol 50(4) p 402–410 doi: 10.1016/j.jmp.2006.03.004.
Wu Y C and Feng J W 2018 Development and Application of Artificial Neural Network Wirel Pers Commun 102(2) p 1645–1656
Bulsari A 1993 Some analytical solutions to the general approximation problem for feedforward neural networks Neural Networks 6(7) p 991–996
S. Karsoliya 2012 Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture International Journal of Engineering Trends and Technology [Online] Available: http://www.internationaljournalssrg.org
Balcázar J L R, Gavaldà, and Siegelmann H T 1997 Computational Power of Neural Networks: A Characterization in Terms of Kolmogorov Complexity
Kristal A dan Harintaka 2022 Analisis Kehandalan Ekstraksi Garis Tepi Bangunan dari Data Foto Udara Menggunakan Pendekatan Deep Learning Berbasis Mask R-CNN Journal of Geodesy and Geomatics 17(2) p 273-285
Watanabe S 2010 Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory Journal of Machine Learning Research 11 3571-3594.
Takase T, Oyama S, and Kurihara M 2018 Effective Neural Network Training with Adaptive Learning Rate based on Training Loss Neutral Networks 101 p 68-78
DOI: https://doi.org/10.26877/lpt.v2i3.18128
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Lontar Physics Today
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright of Lontar Physics Today ISSN 2828-0970 (online)
Gedung Utama GU.2.01 FPMIPATI, Universitas PGRI Semarang
Jl. Lontar No. 1-Dr. Cipto, Kampus 1 UPGRIS, Semarang
Email: upgrisphysicstoday@upgris.ac.id