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All Journal TEKNIK INFORMATIKA Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Eksplora Informatika JURNAL ILMIAH INFORMATIKA KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pengabdian Masyarakat Sains dan Teknologi Journal of Computing and Informatics Research J-Icon : Jurnal Komputer dan Informatika
Dedy Hartama
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Optimizing Multilayer Perceptron for Car Purchase Prediction with GridSearch and Optuna Ginanti Riski; Dedy Hartama; Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6328

Abstract

Multilayer Perceptron (MLP) is a powerful machine learning algorithm capable of modeling complex, non-linear relationships, making it suitable for predicting car purchasing power. However, its performance depends on hyperparameter tuning and data quality. This study optimizes MLP performance using GridSearch and Optuna for hyperparameter tuning while addressing data imbalance with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset comprises demographic and financial attributes influencing car purchasing power. Initially, the dataset exhibited class imbalance, which could lead to biased predictions; SMOTE was applied to generate synthetic samples, ensuring a balanced class distribution. Two hyperparameter tuning approaches were implemented: GridSearch, which systematically explores a predefined parameter grid, and Optuna, an adaptive optimization framework utilizing a Bayesian approach. The results show that Optuna achieved the highest accuracy of 95.00% using the Adam optimizer, whereas GridSearch obtained the best accuracy of 94.17% with the RMSProp optimizer, demonstrating Optuna's superior ability to identify optimal hyperparameters. Additionally, SMOTE significantly improved model stability and predictive performance by ensuring adequate class representation. These findings offer insights into best practices for optimizing MLP in predictive modeling. The combination of SMOTE and advanced hyperparameter tuning techniques is applicable to various domains requiring accurate predictive analytics, such as finance, healthcare, and marketing. Future research can explore alternative optimization algorithms and data augmentation techniques to further enhance model robustness and accuracy.
Co-Authors Achmad Noerkhaerin Putra Agus Perdana Windarto Ahmad Riza Fuadi Ambariyanto Ambariyanto Cici Astria Dwi Findi Auliasari Eko Ahadi Fahmi Firzada Ginanti Riski Gita Febrianti Hanifah Urbach Sari Ihsan Syajidan Ika Okta Kirana Indra Gunawan Indra Swanto Ritonga Jonas Rayandi Saragih Khairunnissa Fanny Irnanda Kristin Daya Rohani Sianipar Muasir Pagan Muhammad Ridwan Lubis Muhammad Zarlis Nasib Marbun P.P.P.A.N.W Fikrul Ilmi R.H Zer Rizky Khairunnisa Sormin Sapriyaldi, Muhammad Septri Wanti Siahaan Sinta Maulina Dewi Solikhun Suranta Bill Fatric Ginting Surya Darma Waldi Setiawan Wanayumini Wanto, Anjar Wendi Robiansyah