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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 123 Documents
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Search results for , issue "Vol 13, No 2: June 2024" : 123 Documents clear
Advancing machine learning for identifying cardiovascular disease via granular computing Ku Khalif, Ku Muhammad Naim; Muhammad, Noryanti; Mohd Aziz, Mohd Khairul Bazli; Irawan, Mohammad Isa; Iqbal, Mohammad; Setiawan, Muhammad Nanda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2433-2440

Abstract

Machine learning in cardiovascular disease (CVD) has broad applications in healthcare, automatically identifying hidden patterns in vast data without human intervention. Early-stage cardiovascular illness can benefit from machine learning models in drug selection. The integration of granular computing, specifically z-numbers, with machine learning algorithms, is suggested for CVD identification. Granular computing enables handling unpredictable and imprecise situations, akin to human cognitive abilities. Machine learning algorithms such as Naïve Bayes, k-nearest neighbor, random forest, and gradient boosting are commonly used in constructing these models. Experimental findings indicate that incorporating granular computing into machine learning models enhances the ability to represent uncertainty and improves accuracy in CVD detection.
Hybrid model: IndoBERT and long short-term memory for detecting Indonesian hoax news Yefferson, Danny Yongky; Lawijaya, Viriyaputra; Girsang, Abba Suganda
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1913-1924

Abstract

The world has entered an era that technology has developed far. Due to rapid technological development, information is easily spread. However, not all information spread through social media is factual information. Responding to this social phenomenon, we initiated to create a hoax detection system using the combined method of Indo bidirectional encoder representations from transformers (IndoBERT) and long short-term memory (LSTM). The dataset used in this study are obtained through the process scraping on the site turnbackhoax.id and cable news network (CNN) Indonesia. We decided to use the IndoBERT-LSTM method to detect hoaxes, using IndoBERT as the feature extractor and LSTM as the classification layer can be an effective method because of its advantages in managing and understanding Indonesian language. The results show that the IndoBERT-LSTM model achieved an accuracy of 93.2%, precision of 92%, recall of 89.7%, and F1-score of 90,8%. From a total of 5876 data composed of a total of 1998 factual news and 3878 hoax data. The hoax detection system using IndoBERT-LSTM is a promising approach for detecting hoaxes accurately and efficiently. This model has the potential to make a significant impact in the fight against the spread of Hoaxes.
Artificial intelligence and internet of things in manufacturing decision processes Wijaya, Santo; Hermanto Rudy, Lim; Debora, Fransisca; Ardila Rahma, Rana; Ramadhan, Arief; Attaqwa, Yusita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2185-2200

Abstract

This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.

Page 13 of 13 | Total Record : 123

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