Dede Tarwidi
@telkomuniversity.ac.id
Telkom University
Scopus Publications
- Embedded wave generation technique for two-layer non-hydrostatic models
Dede Tarwidi, Sri Redjeki Pudjaprasetya, Didit Adytia
Wave Motion, 2026 - A two-dimensional non-hydrostatic numerical model for dispersive waves generated by submerged landslides
Dede Tarwidi, Sri Redjeki Pudjaprasetya, Didit Adytia
Advances in Water Resources, 2025 - A deep learning approach for wind downscaling using spatially correlated global wind data
Didit Adytia, Arnida L. Latifah, Deni Saepudin, Dede Tarwidi, Sri Redjeki Pudjaprasetya, Semeidi Husrin, Ardhasena Sopaheluwakan, Gegar Prasetya
International Journal of Data Science and Analytics, 2025 - Deep neural network-based prediction of tsunami wave attenuation by mangrove forests
Didit Adytia, Dede Tarwidi, Deni Saepudin, Semeidi Husrin, Abdul Rahman Mohd Kasim, Mohd Fakhizan Romlie, Dafrizal Samsudin
Methodsx, 2024
The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3 %. When comparing the DNN model with three alternative machine learning models— support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)— the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost.•High-accuracy DNN models require hyperparameter optimization and neural network architecture selection.•The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3 %.•DNN can serve as an alternate predictive model to empirical formulas or classical numerical models. - An efficient two-dimensional non-hydrostatic model for simulating submarine landslide-generated tsunamis
Dede Tarwidi, Sri Redjeki Pudjaprasetya, Didit Adytia, Nugrahinggil Subasita
Ocean Engineering, 2024 - The effects of dispersion and non-linearity on the simulation of landslide-generated waves using the reduced two-layer non-hydrostatic model
Dede Tarwidi, Sri Redjeki Pudjaprasetya, Didit Adytia
Computational Geosciences, 2024 - A Non-Hydrostatic Model for Simulating Weakly Dispersive Landslide-Generated Waves
Dede Tarwidi, Sri Redjeki Pudjaprasetya, Sugih Sudharma Tjandra
Water Switzerland, 2023
The aim of this study is to develop an efficient numerical scheme that is capable of simulating landslide-generated waves. The numerical scheme is based on the one-layer non-hydrostatic (NH-1L) model, a phase-solving model that can account for weakly dispersive waves. In this paper, the model is extended to include a time-varying solid bed. This NH-1L scheme is very efficient because, at each time step, only a tridiagonal Poisson pressure matrix needs to be solved. In this study, the capability of the NH-1L scheme to simulate landslide-generated waves is demonstrated by executing two types of landslide motion: constant speed and with acceleration and deceleration. Validation was performed using analytical solutions of the linear weakly dispersive (LWD) model, as well as experimental data. The NH-1L model was capable of describing the generation and propagation of water waves by a submarine landslide from relatively intermediate water to shallow water depths. - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach
Dede Tarwidi, Sri Redjeki Pudjaprasetya, Didit Adytia, Mochamad Apri
Methodsx, 2023
) of 0.98675, a mean absolute percentage error (MAPE) of 6.635%, and a root mean squared error (RMSE) of 0.03902. Compared to empirical formulas, which are often limited to a fixed range of slopes, the XGBoost model is applicable over a broader range of beach slopes and incident wave amplitudes.•The optimized XGBoost method is a feasible alternative to existing empirical formulas and classical numerical models for predicting wave run-up.•Hyperparameter tuning is performed using the grid search method, resulting in a highly accurate machine-learning model.•Our findings indicate that the XGBoost method is more applicable than empirical formulas and more efficient than numerical models. - Modelling of Deep Learning-Based Downscaling for Wave Forecasting in Coastal Area
Didit Adytia, Deni Saepudin, Dede Tarwidi, Sri Redjeki Pudjaprasetya, Semeidi Husrin, Ardhasena Sopaheluwakan, Gegar Prasetya
Water Switzerland, 2023
Wave prediction in a coastal area, especially with complex geometry, requires a numerical simulation with a high-resolution grid to capture wave propagation accurately. The resolution of the grid from global wave forecasting systems is usually too coarse to capture wave propagation in the coastal area. This problem is usually resolved by performing dynamic downscaling that simulates the global wave condition into a smaller domain with a high-resolution grid, which requires a high computational cost. This paper proposes a deep learning-based downscaling method for predicting a significant wave height in the coastal area from global wave forecasting data. We obtain high-resolution wave data by performing a continuous wave simulation using the SWAN model via nested simulations. The dataset is then used as the training data for the deep learning model. Here, we use the Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) as the deep learning models. We choose two study areas, an open sea with a swell-dominated area and a rather close sea with a wind-wave-dominated area. We validate the results of the downscaling with a wave observation, which shows good results. - A reduced two-layer non-hydrostatic model for submarine landslide-generated tsunamis
Dede Tarwidi, Sri Redjeki Pudjaprasetya, Didit Adytia
Applied Ocean Research, 2022 - Tourism Destination Recommendation Using Ontology-based Conversational Recommender System
Z.K.A. Baizal, D. Tarwidi, B. Wijay
International Journal of Computing and Digital Systems, 2021 - Macroscopic Modelling of Pedestrian Flows Based on Conservation Law
Finna Windyani, P. H. Gunawan, Dede Tarwidi
Journal of Physics Conference Series, 2020 - Classification of Road Surface Quality Based on SVM Method
Adhelinia Afenika, P. H. Gunawan, D. Tarwidi
Journal of Physics Conference Series, 2020 - Sea Level Prediction by Using Seasonal Autoregressive Integrated Moving Average Model, Case Study in Semarang, Indonesia
Ronald Tulus, Didit Adytia, Nugrahinggil Subasita, Dede Tarwidi
2020 8th International Conference on Information and Communication Technology Icoict 2020, 2020 - Modeling of wave run-up by using staggered grid scheme implementation in 1D Boussinesq model
D. Adytia, S. R. Pudjaprasetya, D. Tarwidi
Computational Geosciences, 2019 - QSAR modeling of PTP1B inhibitor by using Genetic algorithm-Neural network methods
Isman Kurniawan, Dede Tarwidi, Jondri
Journal of Physics Conference Series, 2019 - Parallelization of elliptic solver for solving 1D Boussinesq model
D Tarwidi, D Adytia
Journal of Physics Conference Series, 2018 - Smoothed particle hydrodynamics method for simulating waterfall flow
M G Suwardi, Jondri, D Tarwidi
Journal of Physics Conference Series, 2018 - Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models
Rian Febrian Umbara, Dede Tarwidi, Erwin Budi Setiawan
Journal of Physics Conference Series, 2018 - Staggered grid implementation of 1D Boussinesq model for simulating dispersive wave
D Adytia, D Tarwidi, S A Kifli, S R Pudjaprasetya
Journal of Physics Conference Series, 2018 - 3D GPU-based SPH simulation of water waves impacting on a floating object
A. R. Priyambada, D. Tarwidi
Iccrec 2017 2017 International Conference on Control Electronics Renewable Energy and Communications Proceedings, 2017 - Optimization of traffic signal timing on road network using cellular automata and fuzzy inference system
Journal of Theoretical and Applied Information Technology, 2017 - Performance evaluation of various phase change materials for thermal energy storage of a solar cooker via numerical simulation
Dede Tarwidi, Danang Triantoro Murdiansyah, Narwan Ginanjar
International Journal of Renewable Energy Development, 2016 - Optimization of multiprobe placement for computerized cryosurgery planning using force-field analogy
R. Fajar, D. Tarwidi, E. B. Setiawan
2016 4th International Conference on Information and Communication Technology Icoict 2016, 2016 - Godunov method for multiprobe cryosurgery simulation with complex-shaped tumors
D. Tarwidi
Aip Conference Proceedings, 2016 - Godunov method for computerized lung cancer cryosurgery planning with efficient freezing time
D. Tarwidi
2015 3rd International Conference on Information and Communication Technology Icoict 2015, 2015 - Modeling and numerical simulation of solar cooker with PCM as thermal energy storage
D. Tarwidi
2015 3rd International Conference on Information and Communication Technology Icoict 2015, 2015 - Godunov method for Stefan problems with enthalpy formulations
D. Tarwidi & S.R. Pudjaprasetya
East Asian Journal on Applied Mathematics, 2013
RECENT SCHOLAR PUBLICATIONS
- Embedded wave generation technique for two-layer non-hydrostatic models
D Tarwidi, SR Pudjaprasetya, D Adytia
Wave Motion, 103665 , 2025
2025
Citations: 1 - A two-dimensional non-hydrostatic numerical model for dispersive waves generated by submerged landslides
D Tarwidi, SR Pudjaprasetya, D Adytia
Advances in Water Resources, 105100 , 2025
2025 - A deep learning approach for wind downscaling using spatially correlated global wind data
D Adytia, AL Latifah, D Saepudin, D Tarwidi, SR Pudjaprasetya, S Husrin, ...
International Journal of Data Science and Analytics 20 (3), 2721-2735 , 2025
2025
Citations: 8 - Deep neural network-based prediction of tsunami wave attenuation by mangrove forests
D Adytia, D Tarwidi, D Saepudin, S Husrin, ARM Kasim, MF Romlie, ...
MethodsX 13, 102791 , 2024
2024
Citations: 6 - An efficient two-dimensional non-hydrostatic model for simulating submarine landslide-generated tsunamis
D Tarwidi, SR Pudjaprasetya, D Adytia, N Subasita
Ocean Engineering 310, 118750 , 2024
2024
Citations: 9 - The effects of dispersion and non-linearity on the simulation of landslide-generated waves using the reduced two-layer non-hydrostatic model
D Tarwidi, SR Pudjaprasetya, D Adytia
Computational Geosciences 28 (1), 43-64 , 2024
2024
Citations: 6 - A non-hydrostatic model for simulating weakly dispersive landslide-generated waves
D Tarwidi, SR Pudjaprasetya, SS Tjandra
Water 15 (4), 652 , 2023
2023
Citations: 5 - Modelling of deep learning-based downscaling for wave forecasting in coastal area
D Adytia, D Saepudin, D Tarwidi, SR Pudjaprasetya, S Husrin, ...
Water 15 (1), 204 , 2023
2023
Citations: 27 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach. MethodsX, 10 (December 2022), 102119
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
2023
Citations: 6 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach, MethodsX 10 (2023) 102119
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
2023
Citations: 7 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach. MethodsX. 2023; 10: 102119
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
This article is licensed under a Creative Commons Attribution 3 , 2023
2023
Citations: 13 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach. MethodsX 10: 102119
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
2023
Citations: 23 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
MethodsX 10, 102119 , 2023
2023
Citations: 204 - A reduced two-layer non-hydrostatic model for submarine landslide-generated tsunamis
D Tarwidi, SR Pudjaprasetya, D Adytia
Applied Ocean Research 127, 103306 , 2022
2022
Citations: 15 - Tourism destination recommendation using ontology-based conversational recommender system
ZKA Baizal, D Tarwidi, B Wijaya
International Journal of Computing and Digital Systems 10 , 2021
2021
Citations: 37 - Classification of road surface quality based on svm method
A Afenika, PH Gunawan, D Tarwidi
Journal of Physics: Conference Series 1641 (1), 012064 , 2020
2020
Citations: 7 - Macroscopic modelling of pedestrian flows based on conservation law
F Windyani, PH Gunawan, D Tarwidi
Journal of Physics: Conference Series 1641 (1), 012031 , 2020
2020
Citations: 2 - Penerapan Aplikasi Pembelajaran Berbasis Multimedia untuk Menunjang Proses Belajar Mengajar di SD Negeri Kertasari 01 Kabupaten Bandung
D Tarwidi, PH Gunawan, D Adytia
Charity: Jurnal Pengabdian Masyarakat 3 (2) , 2020
2020
Citations: 2 - Sea level prediction by using seasonal autoregressive integrated moving average model, case study in semarang, indonesia
R Tulus, D Adytia, N Subasita, D Tarwidi
2020 8th International Conference on Information and Communication … , 2020
2020
Citations: 13 - Tourism recommender system using item-based hybrid clustering method (case study: bandung raya region)
QR Arvianti, ZKA Baizal, D Tarwidi
Journal of Data Science and Its Applications 2 (2), 95-101 , 2019
2019
Citations: 9
MOST CITED SCHOLAR PUBLICATIONS
- An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
MethodsX 10, 102119 , 2023
2023
Citations: 204 - Tourism destination recommendation using ontology-based conversational recommender system
ZKA Baizal, D Tarwidi, B Wijaya
International Journal of Computing and Digital Systems 10 , 2021
2021
Citations: 37 - Godunov method for Stefan problems with enthalpy formulations
D Tarwidi, SR Pudjaprasetya
East Asian Journal on Applied Mathematics 3 (2), 107-119 , 2013
2013
Citations: 28 - Modelling of deep learning-based downscaling for wave forecasting in coastal area
D Adytia, D Saepudin, D Tarwidi, SR Pudjaprasetya, S Husrin, ...
Water 15 (1), 204 , 2023
2023
Citations: 27 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach. MethodsX 10: 102119
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
2023
Citations: 23 - Performance Evaluation of Various Phase Change Materials for Thermal Energy Storage of A Solar Cooker via Numerical Simulation.
D Tarwidi, DT Murdiansyah, N Ginanjar
International journal of renewable energy development 5 (3) , 2016
2016
Citations: 20 - QSAR modeling of PTP1B inhibitor by using Genetic algorithm-Neural network methods
I Kurniawan, D Tarwidi, J Jondri
Journal of Physics: Conference Series 1192 (1), 012059 , 2019
2019
Citations: 18 - Modeling of wave run-up by using staggered grid scheme implementation in 1D Boussinesq model
D Adytia, SR Pudjaprasetya, D Tarwidi
Computational Geosciences 23 (4), 793-811 , 2019
2019
Citations: 16 - A reduced two-layer non-hydrostatic model for submarine landslide-generated tsunamis
D Tarwidi, SR Pudjaprasetya, D Adytia
Applied Ocean Research 127, 103306 , 2022
2022
Citations: 15 - Numerical Modeling of Heat Transfer in Gun Barrel with Experimental Validation
KA Suyadnya, D Tarwidi, EB Setiawan, RF Umbara
International Journal of Engineering and Technology 8 (1.9), 62-66 , 2019
2019
Citations: 15 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach. MethodsX. 2023; 10: 102119
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
This article is licensed under a Creative Commons Attribution 3 , 2023
2023
Citations: 13 - Sea level prediction by using seasonal autoregressive integrated moving average model, case study in semarang, indonesia
R Tulus, D Adytia, N Subasita, D Tarwidi
2020 8th International Conference on Information and Communication … , 2020
2020
Citations: 13 - Staggered grid implementation of 1D Boussinesq model for simulating dispersive wave
D Adytia, D Tarwidi, SA Kifli, SR Pudjaprasetya
Journal of Physics: Conference Series 971 (1), 012020 , 2018
2018
Citations: 11 - Modeling and numerical simulation of solar cooker with PCM as thermal energy storage
D Tarwidi
2015 3rd international conference on information and communication … , 2015
2015
Citations: 11 - An efficient two-dimensional non-hydrostatic model for simulating submarine landslide-generated tsunamis
D Tarwidi, SR Pudjaprasetya, D Adytia, N Subasita
Ocean Engineering 310, 118750 , 2024
2024
Citations: 9 - Tourism recommender system using item-based hybrid clustering method (case study: bandung raya region)
QR Arvianti, ZKA Baizal, D Tarwidi
Journal of Data Science and Its Applications 2 (2), 95-101 , 2019
2019
Citations: 9 - An enthalpy-based finite element method for solving two-phase Stefan problem
D Tarwidi
Indonesian Journal on Computing (Indo-JC) 4 (1), 43-56 , 2019
2019
Citations: 9 - A deep learning approach for wind downscaling using spatially correlated global wind data
D Adytia, AL Latifah, D Saepudin, D Tarwidi, SR Pudjaprasetya, S Husrin, ...
International Journal of Data Science and Analytics 20 (3), 2721-2735 , 2025
2025
Citations: 8 - An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach, MethodsX 10 (2023) 102119
D Tarwidi, SR Pudjaprasetya, D Adytia, M Apri
2023
Citations: 7 - Classification of road surface quality based on svm method
A Afenika, PH Gunawan, D Tarwidi
Journal of Physics: Conference Series 1641 (1), 012064 , 2020
2020
Citations: 7
