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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sains Dan Teknologi (SAINTEKBU) JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Teknik Informatika (JUTIF) BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Journal of Sustainable Transformation Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi Journal of Social Computer and Religiosity Jurnal Komtika (Komputasi dan Informatika)
Zulfariansyah, Muhammad
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Social Sciences

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Oil Palm Stem Disease Detection Based on Color Moments and GLCM Texture Features Using Artificial Neural Networks Hamdani, Hamdani; Septiarini, Anindita; Akhmad Syaifudin, Encik; Tejawati, Andi; Zulfariansyah, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5554

Abstract

Oil palm is an essential commodity for the economy; however, basal stem rot caused by Ganoderma boninense poses a significant threat to plantation productivity and long-term vitality. It highlights the importance of early detection of stem disease to facilitate timely intervention and minimize potential economic losses. This study presents an image-based approach to diagnosing oil palm stem maladies, leveraging handcrafted color and texture features within a supervised machine learning framework. The dataset contained 525 images of oil palm stems, of which 205 depicted healthy specimens, and 320 depicted diseased ones. These were captured within their natural environment. Color features were derived by analyzing color moments within the HSV color space, while texture features were extracted from the Grey-Level Co-occurrence Matrix (GLCM). The extracted features were classified employing an Artificial Neural Network (ANN) and were subsequently contrasted with classifiers including Decision Tree, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine. Model performance was evaluated using k-fold cross-validation with k = 5 and k = 10 to ensure the consistency and reliability of the assessment. The experimental results demonstrated that the highest accuracy of 97.52% was achieved when the ANN model was used to classify the integrated color and texture features. The innovative aspect of this research resides in demonstrating that handcrafted features integrated with artificial neural networks can attain high detection accuracy in scenarios with limited data, providing a viable alternative to data-intensive deep learning techniques. This method facilitates a dependable, computer vision-driven early detection system for oil palm stem diseases, thereby promoting sustainable plantation management. 
Co-Authors Achmad, Refi Riduan Ahmad Yunus Akhmad Syaifudin, Encik Anasputri, Trie Nurkharisma Andi Yolanda Sagita Anindita Septiarini, Anindita dedy dedy, dedy Ferryza Nurwahyu Hamdani Hamdani Hesananda, Rizki Ibrahim, Muhammad Rivani Jundillah, Muhammad Labib Kuncoro, Yohan Najmi, Khayreen Noviani, Irma Ayu Putut Pamilih Widagdo, Putut Pamilih Rayhan Abdilah Rapiq Rizawanti, Riftika Septian, Septian Shafa Nur Alifia Sidabutar, Erni Veronica Supriyanto, Muhammad Ibadurrahman Arrasyid Syilla Arviani P Tejawati, Andi Youlisa, Fatika