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All Journal Jurnal Buana Informatika ELINVO (Electronics, Informatics, and Vocational Education) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Journal of Engineering and Applied Technology Jurnal Pengabdian Masyarakat Bhinneka
Setialana, Pradana
Universitas Negeri Yogyakarta
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Public Health Social Sciences Other

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Journal : Journal of Engineering and Applied Technology

 1 
Development of crowd detection warning system based on deep convolutional neural network using CCTV Muhammad Nurwidya Ardiansyah; Marifa Kurniasari; Muhammad Dzulfiqar Amien; Danang Wijaya; Pradana Setialana
Journal of Engineering and Applied Technology Vol 3, No 1 (2022): (March)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v3i2.43771

Abstract

The 2019 corona virus (Covid-19) pandemic is a global problem for now. One way to deal with the spread of the corona virus is to maintain a distance of at least one meter and stay away from crowds. Therefore, a crowd detection warning system based on a deep convolutional neural network (deep CNN) was developed using CCTV. The development of this system was carried out using the NVIDIA Jetson Nano microcontroller as the computing hardware. Crowd object detection uses the OpenCV library, the YOLOv3-Tiny algorithm, and the euclidean distance method to calculate the distance between 'person' objects. Based on the tests carried out on function and performance, the results obtained that this crowd detection warning system can detect 'person' objects with an accuracy rate of 92.79. In addition, this system has also been able to detect several types of colors from objects so that warning messages can be given more specifically on the color of the clothes of the 'person' in the detected crowd.
Portable Construction Maps (PCM) using location fingerprint positioning algorithm for construction worker safety Annurdien Rasyid; Ahsan Firdaus; Dwi Setiawan; Hajidah Salsabila Allissa Fitri; Pradana Setialana
Journal of Engineering and Applied Technology Vol 3, No 1 (2022): (March)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v3i1.43666

Abstract

The existence of a work from home policy does not seem to have a substantial impact on construction workers. Cases of work accidents on infrastructure projects during the Covid-19 pandemic experienced a significant increase. As reported by BPJamsostek data that the number of work accident insurance claims in the first semester (January-June) 2020 reached 108,573 cases. An increase of 128% over the same period in the previous year.  One of the steps from the construction side to minimize the occurrence of work accidents is by marking dangerous locations or limiting them with a yellow line. The supervision is carried out by the Occupational Health and Safety division which supervises every worker by using hearing and sight senses. However, this supervision is deemed less effective and efficient considering the number of work accidents that continue to increase over time. Therefore, a worker location monitoring system based on An indoor positioning system called Portable Construction Maps (PCM) Using Location Fingerprint Positioning Algorithm for Construction Worker Safety was developed. The development of this system uses the algorithm Location Fingerprint as a method for estimating the location of workers in a construction building. Each worker will bring a device called the Worker Tag, in which there is a microcontroller (Espressif 32) with WiFi and Bluetooth module, which is used to capture wifi and bluetooth signals and calculate the Received signal strength indication(RSSI) which will be sent to the server to be processed using the location fingerprint algorithm. In addition, in the worker tag there is also a Passive Buzzer that is used to alert workers if they enter a dangerous area.
Development and performance analysis of the Gunungkidul cultural potential application based on progressive web apps Pradana Setialana; Muhammad Nurwidya Ardiansyah; Nova Suparmanto
Journal of Engineering and Applied Technology Vol 2, No 1 (2021): (March)
Publisher : Faculty of Engineering, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jeatech.v2i1.39525

Abstract

Gunungkidul has various cultural potentials that make it a tourist destination. To make it easier for tourists to visit and get tourist destination information, several researchers developed a mobile application-based information system. However, mobile applications have several drawbacks as such as the user must install the application and can only be used on specific operating systems. The purpose of this research is to develop and analyze applications regarding the potential of Gunungkidul culture based on progressive web apps and which can be used without having to install applications and can run in all operating systems. The application development method uses Scrum and Ionic Framework as a framework for the application. The performance analysis method was tested on runtime performance (loading, scripting, rendering, painting, system) and memory usage (min JS Heap and max JS Heap) by using Chrome Developer Tools for 30 tests. The results of the development show that there are 7 features in the application, namely (1) Peta; (2) Geoheritage; (3) Daerah; (4) Cagar Budaya; (5) Kuliner; (6) Seni Adat Tradisi; (7) Agenda. Runtime performance and memory usage test results show the average value on aspects (1) Loading: 33.60 ms; (2) Scripting: 1069.20 ms; (3) Rendering: 84.90 ms; (4) Painting: 22.33; (5) System: 429.67 ms; (6) Min JS Heap: 8.05 MB; and (8) Max JS Heap: 19.51 MB.
Co-Authors Ahsan Firdaus Al-Khawarizmi, Andi Hakim Ani Widayati Annurdien Rasyid Ardiansyah, Dhista Dwi Nur Ardiansyah, Muhammad Nurwidya Ardiyanto, Widya Ardy, Satya Adhiyaksa Arif Rahman Hakim Danang Wijaya Dwi Setiawan Eka Ary Wibawa Elviana Hajidah Salsabila Allissa Fitri Handaru Jati Hasbi Igi Ardiyanto Mahali, Muhamad Izzudin Marifa Kurniasari Muhammad Dzulfiqar Amien Muhammad Nurwidya Ardiansyah Nova Suparmanto Nurkhamid, N. Ratna Wardani Rizqi Ilyasa Aghni Teguh Bharata Adji Tiala, Maria Bernadetha Charlotta Wonda Wijaya, Danang Yuniar Indrihapsari