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All Journal International Journal of Electrical and Computer Engineering Jurnal Pembangunan Wilayah dan Kota
Adam, Khalid
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering

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Keterkaitan Pola Guna Lahan dengan Pola Pergerakan Layanan Transportasi Online di Kawasan Universitas Diponegoro Adam, Khalid; Manullang, Okto Risdianto
Jurnal Pembangunan Wilayah dan Kota Vol 16, No 3 (2020): JPWK Vol 16. No. 3 September 2020
Publisher : Universitas Diponegoro, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/pwk.v16i3.22077

Abstract

Online transportation has become popular among the public because of its different character than public transportation that was previously available. Therefore, the question in this study is how the influence of land use patterns on the existence of online transportation in the Education Area of Diponegoro University ?. The expected results are in the form of information regarding the effect of land use on online transportation so as to be able to issue policy recommendations related to online transportation. The analysis was carried out by overlaying the pattern of use of the exiting land with the movement pattern of the motorcycle taxi service. The data used in the analysis is divided into two based on time, namely at weekends and weekdays. Based on observation, it was found that 759 movements and the results of overlays carried out with existing land use patterns showed that the highest pattern of demand occurred in residential and boarding areas, educational areas, and commercial areas. This explains that these areas have a large influence on the movement of online transportation facilities. 
Analyzing the instructions vulnerability of dense convolutional network on GPUS Adam, Khalid; Mohd, Izzeldin I.; Ibrahim, Younis
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4481-4488

Abstract

Recently, deep neural networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury or death. Due to the high computations of the DNN models, DNNs are often executed on the graphics processing units (GPUs). However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. In this paper, we show how the fault injection can provide a deeper understanding of DenseNet201 model instructions vulnerability on the GPU. Then, we analyze vulnerable instructions of the DenseNet201 on the GPU. Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation strategy.
Co-Authors Ibrahim, Younis Mohd, Izzeldin I. Okto Risdianto Manullang