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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) International Journal of Artificial Intelligence and Robotics (IJAIR)
Novrido Charibaldi
UPN "Veteran" Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Perbandingan Waktu Respon Aplikasi Database NoSQL Elasticsearch dan MongoDB pada Pengujian Operasi CRUD Theresia Liana Sinaga; Novrido Charibaldi; Nur Heri Cahyana
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 8 No. 1 (2023): Januari 2023
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2023.8.1.22-35

Abstract

Currently, humans live in an era of data oceans, where the amount of data production is increasing from time to time, which is followed by severe challenges in terms of processing, storing, and analyzing data, especially big data. The increase in the number of large data production can affect the speed of access to the database, effectiveness, and speed of response time in the data processing. Relational databases have been the leading model for data storage, analysis, processing, and retrieval for more than forty years. However, due to the increasing need for large-scale data storage, the scalability and performance of a data processing system, as well as the constant growth of the amount of data, another alternative to databases emerged, namely NoSQL technology. Based on previous studies regarding the comparison of response time and database performance, the average concludes that NoSQL performance is more effective and efficient than relational databases. Based on the implementation and testing, it can be concluded that the NoSQL database application MongoDB is proven to be superior in every command of CRUD tested compared to the Elasticsearch NoSQL database application, where in testing the create data command with a JSON file, the MongoDB database application is 42.5 times faster than the Elasticsearch database application. In testing the command to create data into a database containing different amounts of data, the MongoDB database application is 333.9 times faster than the average response time of the Elasticsearch database application. In testing the read command for data in a database containing different amounts of data, the MongoDB database application is 35.5 times faster than the Elasticsearch database application. In testing the update operation of data in a database containing different amounts of data, the MongoDB database application is 9.8 times faster than the Elasticsearch database application. in testing the delete operation of data in a database containing different amounts of data, the MongoDB database application is 58.9 times faster than the Elasticsearch database application.
Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center Nico Nico; Novrido Charibaldi; Yuli Fauziah
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 1 (2022): May 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.721 KB) | DOI: 10.25139/ijair.v4i1.4323

Abstract

One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.
Co-Authors Nico Nico Nur Heri Cahyana Theresia Liana Sinaga Yuli Fauziah