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All Journal Jurnal Buana Informatika ELINVO (Electronics, Informatics, and Vocational Education) Journal of Engineering and Applied Technology
Setialana, Pradana
Universitas Negeri Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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Perbandingan Performa Relational, Document-Oriented dan Graph Database Pada Struktur Data Directed Acyclic Graph Setialana, Pradana; Adji, Teguh Bharata; Ardiyanto, Igi
Jurnal Buana Informatika Vol 8, No 2 (2017): Jurnal Buana Informatika Volume 8 Nomor 2 April 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v8i2.1079

Abstract

Abstract.Directed Acyclic Graph (DAG) is a directed graph which is not cyclic and is usually employed in social network and data genealogy. Based on the characteristic of DAG data, a suitable database type should be evaluated and then chosen as a platform. A performance comparison among relational database (PostgreSQL), document-oriented database (MongoDB), and graph database (Neo4j) on a DAG dataset are then conducted to get the appropriate database type. The performance test is done on Node.js running on Windows 10 and uses the dataset that has 3910 nodes in single write synchronous (SWS) and single read (SR). The access performance of PostgreSQL is 0.64ms on SWS and 0.32ms on SR, MongoDB is 0.64ms on SWS and 4.59ms on SR, and Neo4j is 9.92ms on SWS and 8.92ms on SR. Hence, relational database (PostgreSQL) has better performance in the operation of SWS and SR than document-oriented database (MongoDB) and graph database (Neo4j).Keywords: database performance, directed acyclic graph, relational database, document-oriented database, graph database Abstrak.Directed Acyclic Graph (DAG) adalah graf berarah tanpa putaran yang dapat ditemui pada data jejaring sosial dan silsilah keluarga. Setiap jenis database memiliki performa yang berbeda sesuai dengan struktur data yang ditangani. Oleh karena itu perlu diketahui database yang tepat khususnya untuk data DAG. Tujuan penelitian ini adalah membandingkan performa dari relational database (PostgreSQL), document-oriented database (MongoDB) dan graph database (Neo4j) pada data DAG. Metode yang dilakukan adalah mengimplentasi dataset yang memiliki 3910 node dalam operasi single write synchronous (SWS) dan single read (SR) pada setiap database menggunakan Node.js dalam Windows 10. Hasil pengujian performa PostgreSQL dalam operasi SWS sebesar 0.64ms dan SR sebesar 0.32ms, performa MongoDB pada SWS sebesar 0.64ms dan SR sebesar 4.59ms sedangkan performa Neo4j pada operasi SWS sebesar 9.92ms dan SR sebesar 8.92ms. Hasil penelitian menunjukan bahwa relational database (PostgreSQL) memiliki performa terbaik dalam operasi SWS dan SR dibandingkan document-oriented database (MongoDB) dan graph database (Neo4j).Kata Kunci: performa database, directed acyclic graph, relational database, document-oriented database, graph database
Perbandingan Performa Relational, Document-Oriented dan Graph Database Pada Struktur Data Directed Acyclic Graph Pradana Setialana; Teguh Bharata Adji; Igi Ardiyanto
Jurnal Buana Informatika Vol. 8 No. 2 (2017): Jurnal Buana Informatika Volume 8 Nomor 2 April 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v8i2.1079

Abstract

Abstract.Directed Acyclic Graph (DAG) is a directed graph which is not cyclic and is usually employed in social network and data genealogy. Based on the characteristic of DAG data, a suitable database type should be evaluated and then chosen as a platform. A performance comparison among relational database (PostgreSQL), document-oriented database (MongoDB), and graph database (Neo4j) on a DAG dataset are then conducted to get the appropriate database type. The performance test is done on Node.js running on Windows 10 and uses the dataset that has 3910 nodes in single write synchronous (SWS) and single read (SR). The access performance of PostgreSQL is 0.64ms on SWS and 0.32ms on SR, MongoDB is 0.64ms on SWS and 4.59ms on SR, and Neo4j is 9.92ms on SWS and 8.92ms on SR. Hence, relational database (PostgreSQL) has better performance in the operation of SWS and SR than document-oriented database (MongoDB) and graph database (Neo4j).Keywords: database performance, directed acyclic graph, relational database, document-oriented database, graph database Abstrak.Directed Acyclic Graph (DAG) adalah graf berarah tanpa putaran yang dapat ditemui pada data jejaring sosial dan silsilah keluarga. Setiap jenis database memiliki performa yang berbeda sesuai dengan struktur data yang ditangani. Oleh karena itu perlu diketahui database yang tepat khususnya untuk data DAG. Tujuan penelitian ini adalah membandingkan performa dari relational database (PostgreSQL), document-oriented database (MongoDB) dan graph database (Neo4j) pada data DAG. Metode yang dilakukan adalah mengimplentasi dataset yang memiliki 3910 node dalam operasi single write synchronous (SWS) dan single read (SR) pada setiap database menggunakan Node.js dalam Windows 10. Hasil pengujian performa PostgreSQL dalam operasi SWS sebesar 0.64ms dan SR sebesar 0.32ms, performa MongoDB pada SWS sebesar 0.64ms dan SR sebesar 4.59ms sedangkan performa Neo4j pada operasi SWS sebesar 9.92ms dan SR sebesar 8.92ms. Hasil penelitian menunjukan bahwa relational database (PostgreSQL) memiliki performa terbaik dalam operasi SWS dan SR dibandingkan document-oriented database (MongoDB) dan graph database (Neo4j).Kata Kunci: performa database, directed acyclic graph, relational database, document-oriented database, graph database
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.
Traversal Struktur Data Bipartite Graph dalam Graph Database menggunakan Depth-First Search Pradana Setialana; Muhammad Nurwidya Ardiansyah
Elinvo (Electronics, Informatics, and Vocational Education) Vol 5, No 2 (2020): November 2020
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (513.69 KB) | DOI: 10.21831/elinvo.v5i2.28326

Abstract

Bipartite graph merupakan satu bentuk graph yang dapat digunakan dalam membentuk sebuah strukur data yang saling berelasi namun memiliki karakteristik dengan dua jenis node yang berbeda seperti data hubungan keluarga atau data pohon keluarga. Dalam menyimpan struktur data bipartite graph ke sebuah database dapat digunakan graph database dengan konsep dimana node saling saling terhubung dengan node lainnya. Bipartite graph yang dikombinasikan dengan graph database menghasilkan solusi yang tepat dalam menyimpan data berelasi dengan dua jenis node yang berbeda. Namun dalam solusi tersebut menimbulkan permasalahan baru mengenai pencarian atau penelusuran (traversal) terhadap data yang terdapat dalam struktur data tersebut. Tujuan dari penelitian ini adalah mengembangkan algoritma yang dapat digunakan dalam penelusuran (traversal) data pada struktur bipartite graph dalam graph database. Algoritma yang dikembangkan adalah terapan dari algoritma depth-first search (DFS) yang telah dimodifikasi sehingga dapat digunakan dalam penelusuran (traversal) data pada bipartite graph dalam graph database. Hasil pengujian terhadap algoritma tersebut yang telah diimplementasikan ke dalam program terhadap data yang ada pada satu garis ikatan keluarga besar yaitu keluarga “George Washington /CASSIDY/” pada 30 kali percobaan dengan satuan waktu nanosecond yaitu 10-9 detik menunjukkan waktu maksimal yang didapat sebesar 42831 nanosecond, waktu minimal 5150 nanosecond, dan didapat rata-rata waktu penelusuran sebesar 9407,93 nanosecond.
Co-Authors Ahsan Firdaus Annurdien Rasyid Danang Wijaya Dwi Setiawan Hajidah Salsabila Allissa Fitri Igi Ardiyanto Marifa Kurniasari Muhammad Dzulfiqar Amien Muhammad Nurwidya Ardiansyah Nova Suparmanto Teguh Bharata Adji