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All Journal Journal of Technology and Informatics (JoTI) TIERS Information Technology Journal
I Made Ardi Sudestra
Universitas Pendidikan Ganesha
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Meta-Analysis Of Genetic Algorithm Implementation For Optimization Of Artificial Neural Network Methods Komang Nova Artawan; Agus Dharma; I Made Ardi Sudestra
TIERS Information Technology Journal Vol. 4 No. 2 (2023)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v4i2.5515

Abstract

One of artificial intelligence methods, which is artificial neural networks, have been widely used in data analysis to make predictions, forecasting, and data classification. The artificial neural network method has convergence or local minimum problems because it requires randomly generated weight values. There is a lot of research that discusses optimization techniques for initiating this initial weight to solve that problem. In this study, a meta-analysis was carried out regarding the implementation of genetic algorithms for optimization of artificial neural network methods. Based on 10 journals that has reviewed in this study, it was concluded that optimization of the genetic algorithm can increase the output value of the artificial neural network by 3.44%, but this genetic algorithm optimization have no significant effect based on the sig (2-tailed) value is 0.595 and t count value is 0.551 that have been obtained and tested using paired samples t-test method with help of SPSS software.
Comparative Performance of Machine Learning Algorithms for Diabetes Prediction I Made Ardi Sudestra; Adie Wahyudi Oktavia Gama; Gede Humaswara Prathama; I Gusti Ngurah Darma Paramartha; Musawer Hakimi
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1195

Abstract

Early detection of diabetes mellitus is crucial to prevent severe complications. This study evaluates three machine learning algorithms for diabetes prediction using a quantitative comparative experimental design. The algorithms are k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest. These methods were chosen to compare distinct learning paradigms. k-NN is distance-based, SVM is margin-based, and Random Forest is an ensemble method. The goal is to find the optimal model for clinical use. The Pima Indians Diabetes dataset was used. It includes 390 patients and 15 clinical features. Performance was measured by accuracy, precision, recall, and F1-score. Random Forest had the highest accuracy (89.7%) and F1-score, providing the most balanced classification. SVM followed with 84.6%, and k-NN achieved 76.9%. Although k-NN had the highest recall (0.750), its precision was low (0.375), showing a high false-positive rate. Feature importance analysis pointed to blood glucose levels as the most significant predictor, which matches clinical knowledge. In summary, ensemble techniques like Random Forest offer the most reliable results. This highlights the importance of selecting the right algorithm for early diabetes detection in clinical applications.
Co-Authors Adie Wahyudi Oktavia Gama Agus Dharma Gede Humaswara Prathama I Gusti Ngurah Darma Paramartha Komang Nova Artawan Musawer Hakimi