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All Journal Statistika Jurnal Ilmiah Kursor Jurnal Sains Matematika dan Statistika MESIN PROSIDING SEMINAR NASIONAL CENDEKIAWAN JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MAJAMATH: Jurnal Matematika dan Pendidikan Matematika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika JTIK (Jurnal Teknik Informatika Kaputama) JISA (Jurnal Informatika dan Sains) Abdimasku : Jurnal Pengabdian Masyarakat Journal of Integrated and Advanced Engineering (JIAE) JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Jurnal Pengabdian Masyarakat dan aplikasi Teknologi METRIK SERIAL TEKNOLOGI DAN SAINS Metrik Serial Humaniora dan Sains Kocenin Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Sosial
Joko Riyono
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : Jurnal Ilmiah Kursor

 1 
EVALUATION OF PARTICLE SWARM ALGORITHM MODIFICATIONS ON SUPPORT VECTOR MACHINE HYPERPARAMETER OPTIMIZATION TUNING FOR RAIN PREDICTION Putri, Aina Latifa Riyana; Riyono, Joko; Pujiastuti, Christina Eni; Supriyadi
Jurnal Ilmiah Kursor Vol. 12 No. 4 (2024)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i4.411

Abstract

The Particle Swarm Optimization (PSO) algorithm, though simple and effective, faces challenges like premature convergence and local optima entrapment. Modifications in the PSO structure, particularly in acceleration coefficients (  and ), are proposed to address these issues. Techniques like Time Varying Acceleration Coefficients (TVAC), Sine Cosine Acceleration Coefficients (SCAC), and Nonlinear Dynamics Acceleration Coefficients (NDAC) have been implemented to enhance convergence speed and solution quality. This research evaluates various PSO modifications for improving convergence and robustness in rainfall potential prediction using Support Vector Machine (SVM) classification. The UAPSO-SVM algorithm C=0.82568 and γ=0.01960 excels in initial exploration, discovering more optimal global solutions with smaller variability. In contrast, TVACPSO-SVM shows gradual improvement but requires more iterations for stability, while SBPSO-SVM achieves the fastest convergence at iteration 14 but risks overfitting. Robustness analysis reveals all PSO-SVM variants maintain stable performance despite variations in dataset subset sizes, with accuracy stabilizing after a spike at 20%.. Therefore, PSO modifications enhance convergence speed and resilience to data fluctuations, improving their effectiveness for rainfall prediction.  
Co-Authors Bijlsma, David Leon Budiyanto , Chandra Ch. Eni Pujiastuti Christina Eni Pujiastuti Dody Prayitno Dody Prayitno Eni Pujiastuti Eni Pujiastuti, Christina Fani Puspitasari Fayza Nayla Riyana Putri Latifa Riyana Putri, Aina Leo, Joseph Andrew Luthfan , M. Aufar Prayitno , Dody Pujiastuti, Ch Eni Pujiastuti, Ch. Eni Pujiastuti, Christina Eni Pujiastuti, Eni Puspa, Sofia Debi Puspitasari, Fani Putri, Aina Latifa Riyana Putri, Dianing Novita Nurmala Putri, Fayza Nayla Riyana Rosyida Permatasari, Rosyida Shintadewi Julian , Engeline Supriyadi Supriyadi Supriyadi sutanti sutanti Syaifudin Syaifudin, Syaifudin Syaifudin, - -