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All Journal International Journal of Engineering, Science and Information Technology International Journal Software Engineering and Computer Science (IJSECS)
Dikky Suryadi
STMIK Al Muslim
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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The Application of Artificial Intelligence for Anomaly Detection in Big Data Systems for Decision-Making Cut Susan Octiva; Dikky Suryadi; Loso Judijanto; Mitranikasih Laia; Dedy Irwan
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3358

Abstract

The development of big data technology has generated huge volumes of diverse data, creating challenges in detecting anomalies that could potentially affect decision-making. This research aims to examine the application of artificial intelligence (AI) in detecting anomalies in big data systems to support faster, more accurate and effective decision-making. The approach used includes the integration of machine learning algorithms, such as classification-based detection, clustering, and deep learning, in identifying abnormal patterns in large datasets. The research method involves real-time dataset-based simulations by measuring the performance of AI models using accuracy, precision, recall, and F1-score metrics. The results show that the application of AI can significantly improve the anomaly detection capability compared to conventional methods, with an average accuracy of 92%.
Using Artificial Neural Networks and the Kohonen Method, an Image Pattern Recognition System for Khat Art Types Rasna Rasna; Adyanata Lubis; Dikky Suryadi; Alexius Ulan Bani; Fifto Nugroho
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.851

Abstract

Arabic letter writing is known as khat art. Khat is classified into many categories and can be identified into three types: Khat Naskhi, Khat Qufi, and Khat Farisi, per the rules established in the art of Khat. Arabic letters, the subjects of khat art, evolved following the region where it first appeared. As a result, the Qufi style, for instance, marked the start of the evolution of Khat in the tenth century. Previously somewhat rigid, Khat became more fluid and beautiful, although it remained angular. Subsequently, the art of Sulus, Naskhi, Raiham, Riqa, and Tauqi evolved and exhibited the form of Khat, cursive (italic)—artificial neural network-based khat art type recognition by selecting the Kohonen
Co-Authors Adyanata Lubis Alexius Ulan Bani Cut Susan Octiva Dedy Irwan Fifto Nugroho Loso Judijanto Mitranikasih Laia Rasna