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All Journal International Journal of Artificial Intelligence Research Bulletin of Social Informatics Theory and Application
Kareem, Shahab Wahhab
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Computer Science & IT Electrical & Electronics Engineering Social Sciences

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Structure learning of bayesian network using swarm intelligent algorithm: a review Kareem, Shahab Wahhab; Askar, Shavan; Ahmed, Kosrat Dlshad
Bulletin of Social Informatics Theory and Application Vol. 5 No. 2 (2021)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v5i2.463

Abstract

Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. “Deleting, reversing, moving, and inserting” is an approach to finding the best answer to the proposition of problem in the algorithm. In the Enhanced Surface Water Searching Technique, mostly, the hunt for water is done by elephants during dry seasons, It is Pigeon Optimization, Simulated Annealing, Greedy search, and the BDeu metrics being reviewed in combination to evaluate all these strategies being used in order to solve this problem. They subjected different data sets to the uncertainty matrix in an investigation to find out which of these approaches performed best. According to evaluation data, the algorithm shows stronger results and delivers better points. Additionally, this article also represents the structure learning processes for Bayesian Network as well.
Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement Ramadhanu, Agung; Defit, Sarjon; Kareem, Shahab Wahhab
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (480.052 KB) | DOI: 10.29099/ijair.v6i1.225

Abstract

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).
Analysis of Expert System for Early Diagnosis of Disorders During Pregnancy Using the Forward Chaining Method Basiroh, Basiroh; Priyatno, Priyatno; Kareem, Shahab Wahhab; Nurdiyanto, Heri
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (361.062 KB) | DOI: 10.29099/ijair.v5i1.203

Abstract

Nowadays technological developments are increasingly having a positive influence on the development of human life, including in the health sector. One of them is an expert system that can transfer an expert's knowledge into a computer application to simplify and speed up the diagnosis of a disorder or disease in humans. The purpose of this final project is to design an application to diagnose diseases that occur during pregnancy which is caused by the existence of these pregnancies to simplify and speed up the diagnosis of diseases experienced by pregnant women. This study uses the forward chaining method. By involving experts in this expert system analysis according to current needs. Users are given easy access to information on several types of pregnancy disorders and their symptoms, as well as consultation through several questions that the user must answer to find out the results of the diagnosis. While experts are facilitated in system management, both the process of adding, updating and, deleting data.
Co-Authors Ahmed, Kosrat Dlshad askar, shavan Nurdiyanto, Heri Priyatno, Priyatno Ramadhanu, Agung Sarjon Defit