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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal CoreIT Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL TEKNOLOGI DAN OPEN SOURCE Digital Zone: Jurnal Teknologi Informasi dan Komunikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JURTEKSI Zonasi: Jurnal Sistem Informasi Journal of Applied Engineering and Technological Science (JAETS) Jurnal Pengabdian Masyarakat Multidisiplin JPMB: Jurnal Pemberdayaan Masyarakat Berkarakter IJISTECH Jutsi: Jurnal Teknologi dan Sistem Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat J-COSCIS : Journal of Computer Science Community Service Edumatica: Jurnal Pendidikan Matematika Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat SATIN - Sains dan Teknologi Informasi JURNAL KARYA ILMIAH MULTIDISIPLIN INOVTEK Polbeng - Seri Informatika Edu Komputika Journal
Ahmad Zamsuri, Ahmad
Universitas Lancang Kuning
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Journal : INOVTEK Polbeng - Seri Informatika

 1 
Evaluation of the Effect Of Regularization on Neural Networks for Regression Prediction: A Case Study of MLLP, CNN, and FNN Models Susandri; Zamsuri, Ahmad; Nasution, Nurliana; Ramadhani, Maya
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/m2rcsf96

Abstract

Regularization is an important technique for developing deep learning models to improve generalization and reduce overfitting. This study evaluated the effect of regularization on the performance of neural network models in regression prediction tasks using earthquake data. We compare Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) architectures with L2 and Dropout regularization. The experimental results show that MLP without regularization achieved the best performance (RMSE: 0.500, MAE: 0.380, R²: 0.625), although prone to overfitting. CNN performed poorly on tabular data, while FNN showed marginal improvement with deeper layers. The novelty of this study lies in a comparative evaluation of regularization strategies across multiple architectures for earthquake regression prediction, highlighting practical implications for early warning systems.
Co-Authors Abini, Eka Yestira Nita Alkhairi, Putrama Alwan, Hiba Basim Anam, M Khairul Anam, M. Khairul Andi Zahran Budiman Andre Armada Aprilia Milanda Putri Arini Arita Fitri, Triyani Arpan Asparizal, Asparizal Baehaqi Bayu Febriadi, Bayu Bimby, Novia Putri Dadang Juandi Dafwen Toresa Deni Iskandar Eko Sediyono Elvira Asril, Elvira Elvira Elvira Fadly Suandi Fajar, Muhammad Al Fajrizal Fajrizal Fajrizal, Fajrizal Febrizal Alfarasy Syam Febrizal Alfarasy Syam Febrizal As-Syam Feldiansyah Feldiansyah Feldiansyah, Feldiansyah Fitri Juliani Gunadi Widi Nurcahyo Guntoro, Guntoro Hamdani Hamdani Hazira, Nadila Hendrawan, Riki Hiba Basim Alwan idel waldelmi, idel Julianti, Dola Keumala Anggraini Khairani Djahara, Khairani Lisnawita Lisnawita Loneli Costaner Mariza Devega Mhd. Arief Hasan, Mhd. Arief Muhamad Sadar, Muhamad Muzdalifah, Indah Nurfika Sari Nurliana Nasution Nurliana Nasution, Nurliana Nuroini, Indi Pandu Pratama Putra, Pandu Pratama Pane, Eddissyah Putra Pardede, Akim Manaor Hara Rahmiati Rahmiati Ramadani, Indah Ramadhani, Maya Roki Hardianto Sahrul Saputra Saputra, Eko Ikhwan Sari Herlina Sari Herlina Sari Herlina Sarjon Defit Shelydia Martha Sumijan , Sumijan Susandri, Susandri Susi Handayani Sutejo Sutejo Syafitri (Scopus ID: 57200085316), Wenni Syahtriatna D Syahtriatna Djusar Syahtriatna Djusar Syam, Salmaini Safitri Tintien Koerniawati Triyani Arita Fitri Turnandes, Yogo Vebby Vebby Vebby Walhidayat Walhidayat Walhidayat Wenni Syafitri, Wenni Wirta Agustin Yaya S. Kusumah Yenni, Heda Yogi Yunefri, Yogi Yogo Turnandes Yogo Turnandes Yoyon Efendi Yoyon Efendi Yuhelmi Yuhelmi Yuvi Darmayunata Zamzami, Zamzami