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All Journal Cerdika: Jurnal Ilmiah Indonesia Computer Journal
Dini Gustiningsih
Politeknik Kelapa Sawit Citra Widya Edukasi
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Public Health Social Sciences

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Penerapan Teknik Transformasi untuk Meningkatkan Kualitas Citra Penyakit Ganoderma pada Tanaman Kelapa Sawit Menggunakan CNN-Based Enhancement dan Gamma Correction Aditya Aditya; Amanda Amanda; Dini Gustiningsih
Computer Journal Vol. 4 No. 2 (2026): August
Publisher : Yayasan Pendidikan Mitra Mandiri Aceh (YPMMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58477/cj.v4i2.499

Abstract

Ganoderma disease is one of the most destructive diseases affecting oil palm plants, causing basal stem rot, reduced productivity, plant mortality, and financial losses. This study applies Gamma Correction and CNN-Based Enhancement to improve the quality of oil palm images for Ganoderma disease identification. Gamma Correction improves image illumination and intensity, while CNN-Based Enhancement enhances structural details through a deep learning approach. The model achieved a test accuracy of 0.7714 and a test loss of 0.4165. The Healthy, Infected, and Initial Infection classes achieved F1-scores of 0.8571, 0.7500, and 0.7200, respectively. The results indicate that image transformation techniques can support Ganoderma disease identification in oil palm plants. The proposed enhancement process, which combines intensity correction with CNN-based enhancement, provides a potential approach for improving image quality and supporting the development of automated detection systems that are more accurate, adaptive, and suitable for field deployment.
Co-Authors Aditya Aditya Amanda Amanda Jimmy Pandapotan Pardede Jojon Soesatrijo