Article Per Year (5 Year)
p-Index From 2020 - 2025
0.817
P-Index
This Author published in this journals
All Journal Majalah Ilmiah Pengkajian Industri Automotive Experiences
Bismantoko, Sahid
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
VEHICLE IDENTIFICATION IN PARKING AREAS USING ADAPTIVE BRIGHTNESS THRESHOLDING Rosyidi, Mohammad; Bismantoko, Sahid; Widodo, Tri
Majalah Ilmiah Pengkajian Industri Vol 14, No 1 (2020): Majalah Ilmiah Pengkajian Industri
Publisher : BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (398.488 KB) | DOI: 10.29122/mipi.v14i1.3973

Abstract

The problem of parking systems on the street is a classic problem that occurs from year to year, manysolutions are offered in solving the parking problem on the street. The problem is not only related to trafficjams due to in and out of vehicles from the parking spaces but also the parking management issues becomepolemic at this time. A prototype of parking management monitoring system tries to provide solution inmanaging parking by using image processing based smart camera. In a prototype the system test performedon day and night conditions, to anticipate the very contrast difference intensity of pixels during the day or night so as to develop vehicle detection program using adaptive brightness thresholding. The results showthat the program has been running quite well to identify vehicles during day and night timeframe
CHARACTER RECOGNITION FOR INDONESIAN LICENSE PLATE BY USING IMAGE ENHANCEMENT AND CONVOLUTIONAL NEURAL NETWORK Bismantoko, Sahid; Rosyidi, M.; Chasanah, Umi; Suksmono, Adityo; Widodo, Tri
Majalah Ilmiah Pengkajian Industri Vol. 14 No. 2 (2020): Majalah Ilmiah Pengkajian Industri
Publisher : Deputi TIRBR-BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/mipi.v14i2.4198

Abstract

Many Intelligent Transport System technology have been applied in real world problems such as traffic monitoring, parking management, toll collection, law enforcement. ALPR system is one of the ITS technologies that is widely applied, however this ALPR system can not produce faultless recognition yet, especially for Indonesia license plate. In this research, image enhancement and Convolution Neural Network are proposed to the character recognition. The dataset used in this research are Indonesia license plate. The first step is train dataset to recognize character and evaluate the model with recall, precision, and f-1 score from test dataset. The model achieves accuracy and loss just over 0.99 and just below 0.01 on validation dataset respectively.Key Words : ALPR; ITS; Recall; Precision; F-1 Score; Accuracy; Loss.
A COMPARISON PRE-TRAINED MODELS FOR AUTOMATIC INDONESIAN LICENSE PLATE RECOGNITION Bismantoko, Sahid; Rosyidi, M.; Chasanah, Umi; Haryono, Asep; Widodo, Tri
Majalah Ilmiah Pengkajian Industri Vol. 15 No. 1 (2021): Majalah Ilmiah Pengkajian Industri
Publisher : Deputi TIRBR-BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Automatic License Plate Recognition is related to the Intelligent Transportation System (ITS) that supports the road's e-law enforcement system. In the case of the Indonesian license plate, with various colour rules for font and background, and sometimes vehicle owners modify their license plate font format, this is a challenge in the image processing approach. This research utilizes pre-trained of AlexNet, VGGNet, and ResNet to determine the optimum model of Indonesian character license plate recognition. Three pre-trained approaches in CNN-based detection for reducing time for a build if model from scratch. The experiment shows that using the pre-trained ResNet model gives a better result than another two approaches. The optimum results were obtained at epoch 50 with an accuracy of 99.9% and computation time of 26 minutes. This experiment results fulfil the goal of this research. Keywords : ALPR; ITS; CNN; AlexNet; VGGNet; ResNet
Theoretical Experiments on Road Profile Data Analysis using Filter Combinations Karmiadji, Djoko Wahyu; Rosyidi, M.; Widodo, Tri; Zaenal, Harris; Nurdam, Nofriyadi; Kadir, Andi M.; Hidayat, Sofwan; Bismantoko, Sahid; Pramana, Nurhadi; Winarno, Winarno
Automotive Experiences Vol 6 No 3 (2023)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.9901

Abstract

Identification of road profiles is needed to provide the input of automotive simulation and endurance testing. The analysis with estimation methods is mostly done to identify road profiles. The main goal of analysis methods is to obtain the data of vertical displacements due to road profile measurement. The acceleration data is obtained from measuring road profile by using 4 sensors of accelerometer placed on each car wheel. The measuring data is converted to be vertical displacement data by using a "double integrator", however, it is not easy to get accurate results since the signal obtained carries a lot of noise and it is necessary to design the right filter reduce the noise. In this study, the signal filtering methods reducing the noise were used Fast Fourier Transform (FFT) and Kalman Filter (KF) combination. Experiments were carried out by combining Fast Fourier Transform and Kalman Filters using an input signal with unit (volt) in the time domain. In addition, this research focused on preparing the survey data that has been obtained by eliminating the noise to convert becoming the displacement input data for providing the loads of automotive simulation testing.
Co-Authors Adityo Suksmono, Adityo Chasanah, Umi Harris Zaenal, Harris Haryono, Asep Hidayat, Sofwan Kadir, Andi M. Karmiadji, Djoko Wahyu Nurdam, Nofriyadi Pramana, Nurhadi Rosyidi, M. Rosyidi, Mohammad Tri Widodo Winarno Winarno