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All Journal International Journal of Advances in Applied Sciences Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Al Ishlah Jurnal Pendidikan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL SIMTIKA (Sistem Informasi dan Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Pengabdian Kepada Masyarakat Ekonomi, Pendidikan, dan Sosial Humaniora Soko Guru: Jurnal Ilmu Pendidikan
Vitriani
Universitas Muhammadiyah Riau
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Other

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Intelligent hyperparameter optimization of multilayer perceptron for water quality classification Edi Ismanto; Rahmad Gunawan; Harun Mukhtar; Rahmad Al Rian; Vitriani Vitriani; Hadhrami Ab Ghani
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp490-500

Abstract

Assessment of water quality is essential for monitoring the environment and sustainable resource management. However, complex physicochemical parameters and class imbalance pose challenges for reliable predictive modeling. Although the multilayer perceptron (MLP) is widely applied for classification tasks, its performance depends strongly on effective hyperparameter optimization. This study evaluates three metaheuristic approaches—Bayesian optimization (BO), genetic algorithm (GA), and particle swarm optimization (PSO)—to enhance the performance of MLP on an imbalanced water potability dataset, which is addressed using synthetic minority over-sampling technique (SMOTE). Experimental results obtained using stratified 10-fold cross-validation demonstrate consistent improvements over the baseline MLP (accuracy of 0.7899±0.013). BO and GA improve predictive performance, while PSO achieves the best overall results with an accuracy of 0.9324±0.006, F1-score of 0.9338±0.006, and receiver operating characteristic–area under the curve (ROC–AUC) of 0.9652±0.005. The findings indicate that PSO provides superior convergence stability and generalization by effectively balancing exploration and exploitation during hyperparameter search. The integration of class balancing and swarm-based optimization substantially enhances classification robustness and discriminative capability, supporting its applicability for intelligent water quality monitoring systems.
LSTM Network Hyperparameter Optimization for Stock Price Prediction Using the Optuna Framework Edi Ismanto; Vitriani Vitriani
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.24944

Abstract

This article has been retracted by the publisher.This article has been retracted because of misconduct and plagiarism. The document and its content have been removed from the Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, and reasonable effort should be made to remove all references to this article.
Co-Authors dwi winarti winarti Edi Ismanto Finanta Okmayura Gunawan Ali Gunawan Ali Gunawan, Rahmad Haana Udtari Anjani Hadhrami Ab Ghani Harun Mukhtar Hayami, Regiolina Herlandy, Pratama Benny Januar Al Amien Melly Novalia Mulya Hastuti Rahmad Al Rian Risnal Diansyah, Risnal Robby Satria Robby Satria Robby Wahyu Nur Rohman