Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Mechatronics, Electrical Power, and Vehicular Technology Jurnal Ilmiah Kursor Jurnal technoscientia Journal of Engineering and Technological Sciences Journal of ICT Research and Applications Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding of the Electrical Engineering Computer Science and Informatics JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Nasional Teknik Elektro dan Teknologi Informasi Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Bambang Riyanto Trilaksono
Institut Teknologi Bandung
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Design and application of models reference adaptive control (MRAC) on ball and beam Muhammad Zakiyullah Romdlony; Muhammad Ridho Rosa; Edwin Muhammad Puji Syamsudin; Bambang Riyanto Trilaksono; Agung Surya Wibowo
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 13, No 1 (2022)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2022.v13.15-23

Abstract

This paper presents the implementation of an adaptive control approach to the ball and beam system (BBS). The dynamics of a BBS are non-linear, and in the implementation, the uncertainty of the system's parameters may occur. In this research, the linear state-feedback model reference adaptive control (MRAC) is used to synchronize the states of the BBS with the states of the given reference model. This research investigates the performance of the MRAC method for a linear system that is applied to a non-linear system or BBS. In order to get a faster states convergence response, we define the initial condition of the feedback gains. In addition, the feedback gains are limited to get less oscillation response. The results show the error convergence is improved for the different sets of the sinusoidal reference signal for the MRAC with modified feedback gains. The ball position convergence improvement of MRAC with modified feedback gains for sinusoidal reference with an amplitude of 0.25, 0.5, and 0.75 are 35.1 %, 36 %, and 52.4 %, respectively.
Implementation of recurrent neural network for the forecasting of USD buy rate against IDR Lady Silk Moonlight; Bambang Riyanto Trilaksono; Bambang Bagus Harianto; Fiqqih Faizah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4567-4581

Abstract

This study implements a recurrent neural network (RNN) by comparing two RNN network structures, namely Elman and Jordan using the backpropagation through time (BPTT) programming algorithm in the training and forecasting process in foreign exchange forecasting cases. The activation functions used are the linear transfer function, the tan-sigmoid transfer function (Tansig), and the log-sigmoid transfer function (Logsig), which are applied to the hidden and output layers. The application of the activation function results in the log-sigmoid transfer function being the most appropriate activation function for the hidden layer, while the linear transfer function is the most appropriate activation function for the output layer. Based on the results of training and forecasting the USD against IDR currency, the Elman BPTT method is better than the Jordan BPTT method, with the best iteration being the 4000th iteration for both. The lowest root mean square error (RMSE) values for training and forecasting produced by Elman BPTT were 0.073477 and 122.15 the following day, while the Jordan backpropagation RNN method yielded 0.130317 and 222.96 also the following day. 
Sistem Penilaian Kondisi Jembatan Menggunakan Respons Dinamik dengan Wireless Sensor Network Seno Adi Putra; Gede Agus Andika Sani; Adi Trisna Nurwijaya; Abikarami Anandadiga; Pratama Budi Wijayanto; Bambang Riyanto Trilaksono; Muhammad Riyansyah
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 3: Agustus 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1127.736 KB)

Abstract

Wireless Sensor Network (WSN) is small embedded devices deployed in large scale network and has capability to sense, compute, and communicate with others. It combines modern sensor, microelectronic, computer, communication, and distributed processing technology. It takes into account in several aspects of live especially in structural health monitoring systemof bridge. Due to environmental circumstance, a bridge should be monitored to make sure that it can perform its service safely. Therefore, it is necessary to develop WSN application to monitor bridge condition and send warning message to control room if the bridge is under abnormal condition. This paper proposes the development of automatic WSN system for measuring the level of bridge structural health based on its dynamic responses. The main contribution of this work is developing WSN system for vibration-based bridge condition assessment in which identifies the bridge’s fundamental frequency and mode shape. Experimental result shows that the fundamental frequency measured by our proposed system is close to the value analyzed using finite element analysis (FEA) and according to Modal Assurance Criteriation (MAC), our proposed measurement system has correlation with FEA.
Node Classification on The Citation Network Using Graph Neural Network Irani Hoeronis; Bambang Riyanto Trilaksono
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i1.49

Abstract

Research on Graph Neural Networks has influenced various current real-world problems. The graph-based approach is considered capable of effectively representing the actual state of surrounding data by utilizing nodes, edges, and features. Consider the feedforward neural network and the graph neural network approaches, we determine the accuracy of each method. In the baseline experiment, training and testing were performed using the NN approach. The resulting accuracy of FNN was 72.59 % and GNN model has increased by 81.65 %. There is a 9.06 % increase in accuracy between the baseline model and the GNN model. The new data utilized in the model predictions showcases the probabilities of each class through randomly generated examples.
Co-Authors Abdurrahman - Abikarami Anandadiga Adi Trisna Nurwijaya Adit Kurniawan Agung Surya Wibowo Ali Sadiyoko Anandadiga, Abikarami Arief Syaichu-Rohman Arief Syaichu-Rohman Armein Z.R. Langi Arry Akhmad Arman Bambang Bagus Harianto Bomo S. Wibowo Bomo S. Wibowo Budi Rahardjo Dwi H. Widyantoro Dwi Hendratmo Widyantoro Dwi Hendratmo Widyantoro E. Aminudin Aziz E. Aminudin Aziz E. Aminudin Aziz Edvin Ramadhan Edvin Ramadhan Edwin Muhammad Puji Syamsudin Eko Marpanaji Enda Esyudha Pratama, Enda Esyudha Fadjar Rahino Triputra Fadjar Rahino Triputra, Fadjar Rahino Fiqqih Faizah Gede Agus Andika Sani Iping Supriana Suwardi Irani Hoeronis Kusprasapta Mutijarsa Lady Silk Moonlight Langi, Armein ZR Masayu L. Khodra Masayu Leylia Khodra Modestus Oliver Asali Mohamad Dahsyat Mohamad Dahsyat, Mohamad Mohammad Dimas Muhammad Adi Khairul Anshary Muhammad Nur Kholish Abdurrazaq Muhammad Ridho Rosa Muhammad Riyansyah Muhammad Zakiyullah Romdlony Nurwijaya, Adi Trisna Pratama Budi Wijayanto Rajesri Govindaraju Ramadhan, Edvin Rianto Adhy Sasongko Rianto Adhy Sasongko, Rianto Adhy Rila Mandala Rila Mandala Riyansyah, Muhammad Sani, Gede Agus Andika Saripudin Saripudin Seno Adi Putra Tjong Wan Sen Toto Indriyanto Trio Adiono Widyawardana Adiprawita Widyawardana Adiprawita, Widyawardana Wijayanto, Pratama Budi Wrastawa Ridwan