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All Journal Medina-Te : Jurnal Studi Islam Chimica et Natura Acta Jurnal Pendidikan Ilmu Pengetahuan Alam (JP-IPA) Jurnal Ilmiah Berkala: Sains dan Terapan Kimia Journal of Artificial Intelligence and Engineering Applications (JAIEA) I-PHILANTHROPY: A Research Journal on Management of Zakat and Waqf Journal of Economics and Business Kartika : Jurnal Ilmiah Farmasi El-Mubarak: Islamic Studies Journal Journal of Religion and Social Community
Abubakar Sidik
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Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Physics Social Sciences Other

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Application of Weighted Loss Function in Convolutional Neural Network for Acne Image Classification Abubakar Sidik; Purnamasari, Ade Irma; Pratama, Denni; Marta, Puji Pramudya; Wijaya, Yudhistira Arie
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1885

Abstract

Automated acne image classification using Convolutional Neural Networks (CNN) holds significant potential in dermatological diagnosis but faces a fundamental challenge of class imbalance. This phenomenon causes standard models to be biased towards majority classes and fail to recognize clinically important minority classes. This study aims to address this bias by applying a Weighted Loss Function to the EfficientNetB1 architecture. The research method employs a comparative experimental approach between two scenarios: the Baseline model (Standard Cross-Entropy) and the Proposed model (Weighted Cross-Entropy). The dataset consists of 5 acne classes with an imbalanced distribution. The results show that the Weighted Loss model significantly outperforms the Baseline model. Overall accuracy increased from 80% to 86%. The most significant improvement occurred in the minority class 'Papules', where the F1-Score surged by 0.10 points (from 0.71 to 0.81). It is concluded that the application of Weighted Loss Function effectively overcomes bias due to imbalanced data without the need for synthetic data augmentation, resulting in a fairer and more reliable model for clinical implementation.
Co-Authors Agus Azhar Ma'arif Umpuna Alam Agus Safari Agus Safari Andi Arsyad Beben Benardi Carolina, Anne Dian S Kamara Disfa Lidian Handayani Erdah Litriani Fazl, Fazli Fazli Fazl Hasim Hasim IMAN PERMANA MAKSUM Junus, Nur Azizah Dj. Kamara, Dian S Lemiyana Linar Z Udin Linar Z Udin Linar Z. Udin Marlina, Ema Marta, Puji Pramudya Panyo, Risna Harun Pratama, Denni Purnamasari, Ade Irma Rachman, Saadah D Rachman, Saadah Diana Riska Nuari Saadah D Rachman Safari, Agus Safri Ishmayana Safri Ishmayana Safri Ishmayana Said Abdullah Syahab Sriastuti, Nyoman Tarisa Udin, Linar Z Yudhistira Arie Wijaya