Arwin Datumaya Wahyudi Sumari, Rosa Andrie Asmara, Dimas Rossiawan Hendra Putra, Ika Noer Syamsiana
In the Artificial Intelligence (AI) world, prediction is a means for recognizing a phenomenon. Three approaches have been used for this task, namely supervised, semi-supervised, and unsupervised. Data annotation or labeling is a must in the first two methods, while the last method clusters the inputted data without knowing the labels. The most basic for those methods is a training data set has to be provided at first and a massive amount of data for the supervised one. This paper used a new means called Cognitive Artificial Intelligence (CAI) Knowledge Growing System (KGS) for the prediction task. KGS is characterized by its cognitive learning capability to learn by interacting directly with the phenomenon. Therefore, it is prospective to overcome the existing methods in the context of past data providing and data recognizing through just-in-time labeling. We also show that KGS succeeds in predicting some data with 100% perfect accuracy. © 2021 IEEE.
Cognitive Artificial Intelligence Research Group (CAIRG), Department of Electrical Engineering, Politeknik Negeri Malang, Abdulrachman Saleh Air Force Base, 2nd Operation Command, Indonesian Air Force, Jawa Timur, Indonesia; Cognitive Artificial Intelligence Research Group (CAIRG), Department of Information Technology, Politeknik Negeri Malang, Jawa Timur, Indonesia
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