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Untoro, Meida Cahyo
Teknik Informatika, Institut Teknologi Sumatera
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Journal : Journal of Intelligent Decision Support System (IDSS)

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Naïve bayes classification for oil palm leaf disease based on color and texture features Kesuma, Alvin; Bangun, Natasya Ate Malem; Untoro, Meida Cahyo
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 3 (2025): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i3.305

Abstract

This study presents a comparison between standard Naïve Bayes classifier and its Genetic Algorithm-optimized variant for automated classification of oil palm leaf diseases. The system incorporates RGB color features alongside texture features extracted using the Gray Level Co-Occurrence Matrix. A dataset of of 225 JPG images of oil palm leaves, divided into training and testing sets in an 80:20 split is used. The methodology consisted of preprocessing, feature extraction, and classification. In the preprocessing phase, images were manually cropped, resized to 256 × 256 pixels, and background elements were removed. Feature extraction was then performed to obtain RGB color values and GLCM-based texture values, including contrast, correlation, energy, and homogeneity. Classification was conducted using two variants of the Naïve Bayes algorithm: one with default parameters and another optimized via GA for the Laplace smoothing hyperparameter. Model performance was assessed using a confusion matrix, with accuracy, precision, and recall serving as the primary evaluation metrics. Experimental results showed that both models achieved identical performance, with an accuracy of 51%, a precision of 52%, and a recall of 51%. These findings suggest that the Naïve Bayes classifier, even in its baseline form, demonstrates low discriminative performance for oil palm leaf disease detection, and when enhanced through GA-based optimization, it still provides only limited effectiveness. Therefore, this research highlights the need to pursue alternative methodologies, such as deep learning techniques or the adoption of more discriminative feature representations, aimed at improving both the accuracy and robustness of image-based disease detection in agriculture.
Co-Authors Afriansyah, Aidil Ahmad Agung Zefi Syahputra Aidil Afriasnyah Algifari, Muhammad Habib Amrulloh, Iqbal Anastasia Puteri Dewi Andika Setiawan Andika Setiawan, Andika Andini, Maria Anggraini, Leslie Annisa Dwi Atika Anugerah Perdana Aprilia Purwanto Aprilianda, Mohamad Meazza Arre Pangestu Athalla, Muhammad Nadhif Bagaskara, Radhinka Bangun, Natasya Ate Malem Ba’its, Alfian Kafilah Buliali, Joko Lianto Dani Al Mahkya Desi Budiarti Dharmawan, Benedictus Budhi Dian Anggraini Drantantiyas, Nike Dwi Grevika Eka Nur'azmi Yunira Eko Dwi Nugroho Eri Yuni Nilasari Faisal, Amir Faza Nur Fuadina Febrianto, Andre Feri Fahrianto Fery Widhiatmoko Fitrawan, Mhd. Kadar Gunawan, Rayhan Fatih Harmiansyah Hidayah, Fathan Rizki Ibn, Ferreyla Setara Ilham Firman Ashari Irawati, Febri Dwi Jerhi Wahyu Fernanda Kesuma, Alvin Kurniawansyah, Apri Laisya, Nashwa Putri Leo Viranda Millennium Leonard Rizta Listiani, Amalia M. Syamsuddin Wisnubroto Mahdia Nisrina Maharani M Mandiri, Tobyanto Putra Marbun, Rustian Afencius Maria Oktarise Natania Gultom Mastuti Widianingsih, Mastuti Muhammad Adam Aslamsyah Muhammad Affandi Muhammad Alfarizi Tazkia Muhammad Farhan Muhammad Muttaqin Muhammad Nadhif Athalla Muhammad Yusuf Muhammad Zulfarhan Najie, Muhammad Nasrulloh, M. Anas Nazla Andintya Wijaya Nestiawan Ferdiyanto Nur'azmi, Eka Nurul Fajrin Ariyani Oktaviana Rinda Sari Perdana, Agung Mahadi Putra Prabandari, Pungki Resti Praramadhana, Daffa Praseptiawan, Mugi Pungki Resti Prabandari Raidah Hanifah Raidah Hanifah Retnosari, Hesti Revangga, Dwi Arthur Riyanarto Sarno Samsu Bahri Sianturi, Elsa Elisa Yohana Sidabutar, Ribka Julyasih Sinaga, Nydia Renli Siregar, Abu Bakar Siddiq Sofian, Ahmad Alif Sophia Nouriska Suranta, Akmal Fauzan Tirta Setiawan Verdiana, Miranti Winda Yulita Wisnubroto, M. Syamsuddin Yulita, Winda Yunira, Eka Nur'azmi Yusuf, Muhammad Asyroful Nur Maulana