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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

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 personâ€™s action using our trained model with DetectNet architecture in various threshold too. The output of the system is detected personâ€™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 atÂ 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

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 personâ€™s action using our trained model with DetectNet architecture in various threshold too. The output of the system is detected personâ€™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 atÂ 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

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

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

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.

Rancang Bangun Aplikasi VitaMind untuk Skrining Awal HIV Pada Remaja Sebagai Kelompok Risiko Yuana, Dia Bitari Mei; Pratama, Afis Asryullah; Pratama, Tegar Wahyu Yudha; Puspitasari, Nilam; Aprilya, Nurina; Hikmah, Faiqatul
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 1 (2026): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i1.196

Human Immunodeficiency Virus (HIV) remains a public health problem, particularly among adolescents who are in the social exploration phase and at risk of experiencing psychological distress. Social stigma, anxiety, and fear of diagnosis often prevent HIV-risk groups from accessing mental health services. Unfortunately, psychological screening has not been optimally integrated into primary health care. This study aims to develop VitaMind, a conversation-based psychological screening application that integrates a rule-based system and generative artificial intelligence (AI) to support early detection of psychological conditions in adolescents at risk of HIV. The research method used a Research and Development (R&D) approach with a Waterfall software development model, including needs analysis, system design, implementation, and testing. The results show that VitaMind is able to provide interactive, safe, and easily accessible self-psychological screening, equipped with sexual health education features and psychologist consultation registration. The integration of the rule-based system and generative AI produces adaptive and empathetic responses, thereby increasing user comfort. The VitaMind application has the potential to become a digital innovation to strengthen mental health services and support HIV prevention efforts among adolescents.

Integrasi Digital Twin Real-Time untuk Kendali Perangkat IoT di Lingkungan Smart Campus Pradana, Reza Putra; Pratama, Afis Asryullah; Rosyady, Ahmad Fahriyannur; Kurniasari, Arvita Agus; Afriansyah, Faisal Lutfi
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3367

The increasing demand for intelligent and sustainable energy management within higher education institutions has encouraged the adoption of IoT-based solutions; however, traditional IoT dashboards typically rely on text-based device lists and non-intuitive identifiers that lack spatial context. As a result, users often struggle to understand which physical devices they are controlling, leading to confusion, poor user experience, and a higher risk of operational errors when managing smart campus facilities. This study aims to develop and validate a Digital Twin–based Smart Campus system capable of synchronizing physical electronic devices with an interactive 3D virtual environment in real time, providing a spatially accurate digital representation of the lecturer room that mirrors the real-world layout. The research employs a systematic workflow that includes problem identification, literature analysis, installation of IoT devices such as Zigbee smart switches and ESP32 IR blasters, creation of a web-based Digital Twin interface, and development of optimized 3D room models using Blender. System testing was conducted to evaluate physical-to-digital and digital-to-physical synchronization performance, and FPS benchmarking was performed to assess usability across high-end, mid-range, and entry-level devices. The results show that the Digital Twin maintains 100% synchronization accuracy with millisecond-level response times and runs smoothly on diverse hardware. By enabling users to interact with devices directly through a virtual environment that visually matches the real room, the system reduces operational mistakes, improves user experience, and enhances awareness of energy usage. The study concludes that the proposed Digital Twin approach effectively overcomes key limitations of traditional IoT dashboards and offers a scalable, practical framework for Smart Campus implementations.

Smart Campus: Desain dan Implementasi Sistem Monitoring dan Kontrol Lampu dan AC Pratama, Afis Asryullah; Pradana, Reza Putra; Kurniasari, Arvita Agus; Rosyady, Ahmad Fahriyannur; Setyohadi, Dwi Putro Sarwo
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3406

The rapid growth of information and communication technology (ICT) has improved many aspects of community life, including access to information, productivity, and innovation. However, the widespread use of digital devices also increases energy consumption due to technological infrastructure and inefficient user behavior, such as leaving equipment powered on when not in use. While technological development can support energy efficiency, developing new energy systems requires complex research. Automation through the Internet of Things (IoT) offers a more practical solution for energy management. In the educational sector, the smart campus concept represents the digital transformation of campus infrastructure to improve operational efficiency and user comfort. This study aims to design and implement a practical, localized, secure, highly interconnected, and scalable monitoring and control system for lights and air conditioners within a campus environment. The system was developed by reviewing previous studies, evaluating available hardware, selecting appropriate network architectures and communication protocols, implementing IoT devices, and integrating them with a server platform. The system utilizes Zigbee communication and a local MQTT broker with authentication to ensure secure and reliable connectivity. Using devices from multiple manufacturers enables interoperability and vendor independence, while scalability is achieved through simple device installation and pairing. Experimental results show reliable performance with response times of 1–3 seconds without errors. Automation features allow lights and air conditioners to activate before working hours and turn off automatically at night if left on, improving energy efficiency and convenience in a smart campus environment.

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