Data Mining Optimization uses C4.5 Classification and Particle Swarm Optimization (PSO) in the location selection of Student Boardinghouses

Data Mining Optimization uses C4.5 Classification and Particle Swarm Optimization (PSO) in the location selection of Student Boardinghouses

• By Waluyo,Ari; Jatnika, , Hendra; Permatasari,Marlina Rahmi Shinta; Tuslaela; Purnamasari, Indah; Perdana, Windarto,Agus.;
• Dokumen Institusi
• 570

The purpose of this study is to select the location of student boarding houses using Particle Swarm Optimization (PSO) and C4.5 optimization techniques. The source of the data was obtained by observing and giving questionnaires to 150 respondents who were lodging in the Pematangsiantar-Simalungun area. from the data set of 81 records and using 5 parameters of assessment ((C1) water cleanliness, (C2) Facilities, (C3) Transportation, (C4) Security, and (C5) Conditions) obtained the results of modeling using the C4.5 + PSO algorithm has better accuracy is 97.78% compared to the C4.5 model whose accuracy is 97.53%. Thus, it is evident that the PSO applied to the weighting of the C4.5 attribute increases the value of accuracy.. 

• Katakunci Data Mining, Particle Swarm Optimization (PSO), C4.5 Classification
• URL https://iopscience.iop.org/issue/1757-899X/874/1
• Penerbit JST (Jurnal Sains dan Teknologi,

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