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All Journal Scientific Journal of Informatics JOIV : International Journal on Informatics Visualization International Journal of Healthcare and Information Technology
Pratama, Afis Asryullah
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Computer Science & IT Public Health

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Edge Computing Implementation for Action Recognition Systems Pratama, Afis Asryullah
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.26433

Abstract

Nowadays the deep learning has been improved to many different sectors, including human action recognition system. This system mostly needs a high computing resource to work on. In its implementation, it will be built under cloud computing architecture which requires sensors used to send whole raw data to the cloud which puts a load in the networks. Therefore, edge computing system exists to overcome that weakness. This paper presents a method to recognize human action using deep learning with edge computing architecture. With RGB image as the input, this system will detect all persons in the frame using SSD-Mobilenet V2 model with various threshold values, then recognize every person’s action using our trained model with DetectNet architecture in various threshold too. The output of the system is detected person’s RoI and its recognized action action, which a lot smaller than the whole frame. As a result, our proposed system yields the best accuracy of human detection at 64.06% with a threshold at 0.15 and the best accuracy of action recognition at  37.8% with a threshold at 0.4.
Edge Computing Implementation for Action Recognition Systems Pratama, Afis Asryullah
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.26433

Abstract

Nowadays the deep learning has been improved to many different sectors, including human action recognition system. This system mostly needs a high computing resource to work on. In its implementation, it will be built under cloud computing architecture which requires sensors used to send whole raw data to the cloud which puts a load in the networks. Therefore, edge computing system exists to overcome that weakness. This paper presents a method to recognize human action using deep learning with edge computing architecture. With RGB image as the input, this system will detect all persons in the frame using SSD-Mobilenet V2 model with various threshold values, then recognize every person’s action using our trained model with DetectNet architecture in various threshold too. The output of the system is detected person’s RoI and its recognized action action, which a lot smaller than the whole frame. As a result, our proposed system yields the best accuracy of human detection at 64.06% with a threshold at 0.15 and the best accuracy of action recognition at  37.8% with a threshold at 0.4.
Design of Audio-Based Accident and Crime Detection and Its Optimization Pratama, Afis Asryullah; Sukaridhoto, Sritrusta; Purnomo, Mauridhi Hery; Lystianingrum, Vita; Budiarti, Rizqi Putri Nourma
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1643

Abstract

The development of transportation technology is increasing every day; it impacts the number of transportation and their users. The increase positively impacts the economy's growth but also has a negative impact, such as accidents and crime on the highway. In 2018, the number of accidents in Indonesia reached 109,215 cases, with a death rate of 29,472 people, which was mostly caused by the late treatment of the casualties. On the other hand, in the same year, there were 8,423 mugs, and 90,757 snitches cases in Indonesia, with only 23.99% of cases reported. This low reporting rate is mostly caused by the lack of awareness and knowledge about where to report. Therefore, a quick response surveillance system is needed. In this study, an audio-based accident and crime detection system was built using a neural network. To improve the system's robustness, we enhance our dataset by mixing it with certain noises which likely to occur on the road. The system was tested with several parameters of segment duration, bandpass filter cut-off frequency, feature extraction, architecture, and threshold values to obtain optimal accuracy and performance. Based on the test, the best accuracy was obtained by convolutional neural network architecture using 200ms segment duration, 0.5 overlap ratio, 100Hz and 12000Hz as bandpass cut-off frequency, and a threshold value of 0.9. By using mentioned parameters, our system gives 93.337% accuracy. In the future, we hope to implement this system in a real environment.
High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease Fajrianti, Evianita Dewi; Pratama, Afis Asryullah; Nasyir, Jamal Abdul; Rasyid, Alfandino; Winarno, Idris; Sukaridhoto, Sritrusta
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.793

Abstract

In some cases, image processing relies on a lot of training data to produce good and accurate models. It can be done to get an accurate model by augmenting the data, adjusting the darkness level of the image, and providing interference to the image. However, the more data that is trained, of course, requires high computational costs. One way that can be done is to add acceleration and parallel communication. This study discusses several scenarios of applying CUDA and MPI to train the 14.04 GB corn leaf disease dataset. The use of CUDA and MPI in the image pre-processing process. The results of the pre-processing image accuracy are 83.37%, while the precision value is 86.18%. In pre-processing using MPI, the load distribution process occurs on each slave, from loading the image to cutting the image to get the features carried out in parallel. The resulting features are combined with the master for linear regression. In the use of CPU and Hybrid without the addition of MPI there is a difference of 2 minutes. Meanwhile, in the usage between CPU MPI and GPU MPI there is a difference of 1 minute. This demonstrates that implementing accelerated and parallel communications can streamline the processing of data sets and save computational costs. In this case, the use of MPI and GPU positively influences the proposed system.
Feature Requirement With Human-Centered Design Approach to Developing A Psychological-Based HIV Yuana, Dia Bitari Mei; Pratama, Afis Asryullah; Pratama, Tegar Wahyu Yudha; Puspitasari, Nilam; Aprilya, Nurina; Hikmah, Faiqatul; Fahrizal, Bayu Krisna Dwihadi
International Journal of Healthcare and Information Technology Vol. 3 No. 2 (2026): January
Publisher : P3M Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/ijhitech.v3i2.6746

Abstract

HIV cases among adolescents in Indonesia have been on the rise recently. Moderate to severe depressive symptoms are reported to be experienced by adolescents living with HIV, which coincides with this increase in cases. This suggests that it is essential to initiate strategic initiatives to enhance the efficacy of the initial screening process by implementing application-based HIV risk group identification through the use of psychological approaches. Designing a user interface that is appropriately structured and meets user requirements is crucial to ensuring the effectiveness of the application being developed. This investigation involved the assessment of feature requirements by HIV disease counselors and psychologists in accordance with the Human-Centered Design (HCD) methodology. Counselors who specialize in HIV disease contribute to the query elements utilized in the initial screening assessment. Meanwhile, psychologists contribute to the approval of the questions utilized as an initial screening test, which affects the psychological well-being and accuracy of adolescent users. The stages adopted for the implementation of HCD were modified in accordance with ISO 9241-210. This research generated user personas, use case diagrams, and the User Interface (UI) design for the VitaMind application, which is primarily designed for pre-test screening. This is a multiplatform application that comprises both web-based and mobile components. This application design has been tested using the system usability scale (SUS) method and produced an accuracy score of 86.1%. The primary contribution and novelty of this research lies in the application of Human-Centered Design principles to the development of a mental health-focused, pre-test HIV screening application tailored for students. Usability testing, employing the System Usability Scale (SUS) method, yielded results demonstrating a high degree of usability.
Co-Authors Aprilya, Nurina Budiarti, Rizqi Putri Nourma Fahrizal, Bayu Krisna Dwihadi Fajrianti, Evianita Dewi Hikmah, Faiqatul Idris Winarno Lystianingrum, Vita Mauridhi Hery Purnomo Nasyir, Jamal Abdul Pratama, Tegar Wahyu Yudha Puspitasari, Nilam Rasyid, Alfandino Yuana, Dia Bitari Mei