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All Journal Majalah Ilmiah Pengkajian Industri Automotive Experiences
Rosyidi, M.
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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

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Journal : Automotive Experiences

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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.
The Road Safety: Utilising Machine Learning Approach for Predicting Fatality in Toll Road Accidents Mutharuddin, Mutharuddin; Rosyidi, M.; Karmiadji, Djoko Wahyu; Fitri, Hastiya Annisa; Irawati, Novi; Waskito, Dwitya Harits; Mardiana, Tetty Sulastry; Subaryata, Subaryata; Nugroho, Sinung
Automotive Experiences Vol 7 No 2 (2024)
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.11082

Abstract

Road safety is one of the critical government transportation concerns, especially on the toll roads. With the increasing number of toll roads as part of infrastructure planning, road traffic accidents are significantly escalating. Developing a system that predicts accidents on toll roads will benefit to reduce the harm that is caused by traffic accidents. This study will propose a method for analysing toll road accidents in Indonesia using historical toll road accident data as a dataset to become a pattern to examine the frequency of accidents. This dataset consists of various parameters from three main factors that cause accidents: human, environmental, and road infrastructure factors. Machine learning technique will be mainly used to determine the most influencing factors by employing classifiers such as Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN) can construct the prediction model. Fourteen subfactors from the data were used to predict the future fatalities caused by accidents, which allowed the system to forecast the accident fatality. The results show accuracy performance on the test set with LR, DT, KNN, and GNB models, 85.3%, 79.4%, 87.1%, and 77.1%, respectively. The KNN Classifier model has the most minor error value of 0.6 compared to the other models. The study’s findings will help analyse the causal factors involved in toll road accidents and could be utilised by road authorities to employ risk control options to mitigate the ramifications.
Co-Authors Adityo Suksmono, Adityo Bismantoko, Sahid Chasanah, Umi Fiantika, Thiya Fitri, Hastiya Annisa Harris Zaenal, Harris Haryono, Asep Hidayat, Sofwan Kadir, Andi M. Karmiadji, Djoko Wahyu Mardiana, Tetty Sulastry Mutharuddin, Mutharuddin Novi Irawati Nugroho, Sinung Nurdam, Nofriyadi Pramana, Nurhadi Subaryata, Subaryata Tri Widodo Waskito, Dwitya Harits Winarno Winarno