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In a large dataset, data mining is a solution to arrange new models  into useful information. The algorithm is often used in machine learning is C4.5. This algorithm is known to be very strong in classifying, but has several weaknesses, such as overlapping and overfitting of data. To handle this, it is necessary to select an attribute that can identify the relevant attribute without reducing the accuracy of the algorithm itself. The Particle Swarm Optimization (PSO) is an optimization algorithm which one can be used as an attribute selection. The PSO benefit is that to easy to use, efficient and has a simple concept when to compared of data mining algorithms and other optimization techniques. In this study, the precision of C4.5 which is optimized by Particle Swarm Optimization (PSO) algorithm is proven to be higher than using the C4.5 algorithm alone. Where the algorithm C4.5+PSO has an precision  of 66.80% while the algorithm of C4.5 has an precision of 76.32%.


C4.5 K-Means K-Medoid Particle Swarm Optimization (PSO) C4.5 K-Means K-Medoid Particle Swarm Optimization (PSO)

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Safira, D., & Mustakim. (2021). Perbandingan Algoritma C4.5 dengan C4.5+Particle Swarm Optimization untuk Klasifikasi Angkatan Kerja. Jurnal Komputer Terapan, 7(2), 272–279.


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