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All Journal International Journal of Global Operations Research Jurnal Mandiri IT
Nindyatama, Zefanya Permata
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Library & Information Science Mathematics

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Comparative Analysis of the Speed of the Sorting Method on Google Translate Indonesian-English Using Binary Search Ekowati, Maria Atik Sunarti; Nindyatama, Zefanya Permata; Widianto, Widianto; Dananti, Kristyanan
International Journal of Global Operations Research Vol. 3 No. 3 (2022): International Journal of Global Operations Research (IJGOR), August, 2022
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v3i3.167

Abstract

In Indonesia, English is a compulsory subject for students. This course is an uninteresting subject and tends to be difficult for students to understand. Meanwhile, on the other hand, international issues are often discussed, namely English which is Students must know and be fluent in reading, writing and communication. The basic idea of research objectives, comparing the sorting process using two different algorithms, namely bubble sort and Merge Sort. The basic model of the research method Comparative Analysis of the Speed of the Shorting Method on Google Translate Indonesian-English Using Binary Search. Sorting, Sorted (ordered according to certain rules/rules), and the data is presented in sorted form, as said in dictionaries, and files in a directory. The algorithm used for sorting is bubble sort, which is an element comparison operation that is exchanged for other elements until the end of the data series is reached, until no more elements are swapped. Results for find out how well the performance speed of the bubble sort algorithm is in sorting data.
Integration of artificial intelligence in cyber security systems to counter quantum computing threats Ekowati, Maria Atik Sunarti; Poernomo, Moyo Hady; Nindyatama, Zefanya Permata
Jurnal Mandiri IT Vol. 13 No. 4 (2025): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i4.388

Abstract

With the rapid advancement in quantum computing, threats to cybersecurity systems are increasingly complex, especially in terms of encryption and data protection. The integration of artificial intelligence (AI) into cybersecurity systems is essential to address these challenges. This study aims to examine the potential of AI in improving the detection and mitigation capabilities of threats arising from the quantum computing revolution. The urgency of this research is driven by the prediction that existing cryptographic algorithms will be easily cracked by quantum computers, raising the need for more adaptive and dynamic security systems. The method used in this study is a simulation approach using machine learning algorithms to model and identify cyber threat patterns specific to quantum computing. The results show that AI-based systems can detect attacks faster and with higher accuracy compared to conventional systems. The output of this research is the development of a security system prototype that combines artificial intelligence and post-quantum security technologies, which can be implemented in various cyber applications to ensure more effective data protection in the quantum computing era.
Co-Authors Dananti, Kristyanan Ekowati, Maria Atik Sunarti Moyo Hady Poernomo Widianto Widianto