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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JTIK (Jurnal Teknik Informatika Kaputama) Indonesian Journal of Electrical Engineering and Computer Science ARSY : Jurnal Aplikasi Riset kepada Masyarakat BERKAT: Jurnal Pemberdayaan Masyarakat International Journal of Research in Vocational Studies (IJRVOCAS) Jurnal Armada Informatika Jurnal Sains dan Teknologi Journal of Computer Science Advancements
Hikmah Adwin Adam
Politeknik Negeri Medan
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Analisis Deteksi Penyakit Daun Pisang Menggunakan Ekstraksi Fitur CNN (MobileNetV2) dan Klasifikasi SVM Yuyun Yusnida Lase; Lampson Pindahaman Purba; Santi Prayudani; Arif Ridho Lubis; Hikmah Adwin Adam
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6590

Abstract

Banana plants (Musa spp.) are one of the leading horticultural commodities in Indonesia that have high economic value and play an important role in national food security. However, banana productivity often decreases due to attacks by various diseases such as Sigatoka, Cordana, and Pestalotiopsis infections that can spread quickly. Early detection of these diseases is crucial to prevent greater losses. This study aims to develop a banana plant disease detection system based on digital image processing with the Support Vector Machine (SVM) algorithm. The research method includes the stages of banana leaf image acquisition, pre-processing using color segmentation, color and texture feature extraction, and disease type classification with the SVM algorithm. The test results show that the developed system is able to recognize banana leaf diseases with an accuracy of 97.8%, precision of 97%, and recall of 98%. These findings prove that the application of digital image processing and the SVM algorithm is effective in detecting banana plant diseases. This system is expected to be a fast, efficient, and accurate diagnostic tool for farmers to increase the productivity and quality of banana harvests.
Co-Authors Ahmad Hidayat Daya Februwanti Gultom Ferry Fachrizal - Habibi Ramdani Safitri Harris Aminuddin Husna, Meryatul Julham Julham Julham Lampson Pindahaman Purba Lubis, Arif Ridho Marliana Sari Muharman Lubis Noferianto Sitompul Prayudani, Santi Tumanggor, Orli Binta Wiwin Sry Adinda Banjarnahor Yulia Fatmi Yuyun Yusnida Lase