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All Journal Saintika Medika: Jurnal Ilmu Kesehatan dan Kedokteran Keluarga. Jurnal Pemberdayaan Masyarakat Madani (JPMM) Mattawang: Jurnal Pengabdian Masyarakat Jurnal Kewarganegaraan International Journal of Economics, Management, Business, and Social Science Jurnal Kolaboratif Sains Al-Buhuts (e-journal) Jurnal Ilmiah Metansi (Manajemen dan Akuntansi) Economics and Business Journal Journal of Mandalika Literature Jurnal Smart Paud Journal of Embedded Systems, Security and Intelligent Systems Media Farmasi Jurnal Armada Pendidikan PENG: Jurnal Ekonomi dan Manajemen IPSIKOM Jurnal Pemikiran dan Pengembangan Pembelajaran Aksi Kita: Jurnal Pengabdian Kepada Masyarakat
Rahmatia
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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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Hybrid Machine Learning Models Based on MobileNetV2 Feature Extraction for Robusta Coffee Leaf Disease Classification Rahmatia; Rasyid, Muh. Rafli; Arifin, Nurhikma
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.11896

Abstract

Purpose – This study aims to evaluate the effectiveness of a hybrid machine learning approach for classifying robusta coffee (Coffea canephora) leaves into healthy and diseased categories, addressing challenges in manual field inspection and limited comparative analyses across classifiers. Design/methods/approach – A hybrid framework was implemented by combining MobileNetV2 as a feature extractor with four machine learning classifiers: Random Forest, K-Nearest Neighbor, Linear Support Vector Machine, and Gaussian Naive Bayes. The dataset comprised 1,560 images (791 healthy and 769 diseased), split into 70% training, 10% validation, and 20% testing using a hash-based grouped strategy to prevent data leakage from duplicate images. Model performance was evaluated using accuracy, F1-score, ROC-AUC, and McNemar’s statistical test. Findings – Gaussian Naive Bayes achieved the highest accuracy (93.89%) and F1-score (93.85%), while Random Forest obtained the highest ROC-AUC (96.94%). However, McNemar’s test showed no statistically significant differences among the models (p > 0.05), indicating comparable classification performance. The results demonstrate that lightweight hybrid approaches can achieve strong performance even with relatively small datasets. Research implications/limitations – The study is limited to binary classification and a relatively small dataset, which may restrict generalizability to more complex, multi-class disease scenarios. Further research with larger and more diverse datasets is recommended. Originality/value – This study provides a systematic comparison of multiple machine learning classifiers using a unified MobileNetV2 feature representation, offering practical insights into efficient and reliable approaches for early-stage coffee leaf disease screening in resource-constrained environments.
Co-Authors - Trimulato, - Abdireviane, Indraswati Tri Abdul Rasyid Abdul Sakur Andi Zakinah Juniarti Arafat, Muhammad Yasser Arif, Mutmaina Arifin, Nurhikma ASNI Avid Inang Rum Ayu Wandira Baharuddin, Nur Annisa Dewi Sugiarti Djumriah Dulsukmi Kasim Elindawati Tutuf Arif Fani Temarwut, Farid Fatmawati Fatmawati Febrianti Finta Amalinda Firmansyah Kusumayadi Fuad Nur Hapsawati Taan Harding Helda Badarudin Herinda Mardin Herman Hikrawati Hindun DJ Usman Ismunandar Jumiati Bandu Larosa, Esta Lita Ulfa Ngaito M. Yogi Riyantama Isjoni Maryam Iskandar Misnawati Musdelifa Abu Samad Noka, Isara Abda Nurul Asqia Nurul Huda Nurwijayanti Prayitno Mulani Rahman Rasyid, Muh. Rafli Retno Fitrianti Reyhan Reza Alyani Djiko Rizki Eka Sakti Octaviani Sahrun Hasan Sandalayuk, Marselia Sitti Saenab Sri Ayu Ashari Sriyani Oktavia Sudirman Sudirman Syamsul Alam Ramli Syefira Salsabila Tri Eko Tri Septyo Indratno Umi Kalsum Yuszda K Salimi