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All Journal Jurnal Teknologi Informasi Mura TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Atika Arpan
Politeknik Negeri Lampung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Enhancing rice plant disease detection through transfer learning and image segmentation with YOLOv11 Muhammad Reza Redo I Islami; Sylvia; Atika Arpan; Rizka Permata; Dwi Handoko; Rima Maulini; Dwirgo Sahlinal
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 13 No 1 (2026): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v13i1.1754

Abstract

Rice is a staple food globally, yet its productivity is often threatened by diseases such as blast and brown spot. Traditional diagnostic methods relying on human observation are prone to delays and inaccuracies. This study introduces an automated detection system that utilizes YOLOv11-seg to improve the accuracy and efficiency of rice disease identification. The model integrates object detection and instance segmentation, is trained on over 6,000 annotated images covering six categories (five disease types and healthy leaves), and leverages transfer learning from COCO weights. Experimental results show that the model achieved a bounding box mAP@50 of 0.607 and a segmentation mAP@50 of 0.564, with F1-scores of 0.62 and 0.59, respectively. The highest detection accuracy was recorded for healthy leaves (86%), while segmentation performance declined on visually similar classes such as Brown Spot and Sheath Blight. Overfitting was observed during training, with a 15–20% gap between training and validation metrics. These findings demonstrate the model's potential for real-time field application in precision agriculture. Future improvements should focus on enhancing spatial accuracy and robustness through synthetic data generation and architectural optimization.
EVALUASI KINERJA ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK ANALISIS SENTIMEN KOMENTAR PUBLIK TERHADAP PEMERINTAHAN PRESIDEN PRABOWO Atika Arpan; Sylvia Sylvia; Rizka Permata
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2864

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen komentar publik terhadap pemerintahan Presiden Prabowo menggunakan algoritma Support Vector Machine (SVM). Data komentar diambil dari platform media sosial dan melalui tahapan preprocessing seperti case folding, tokenization, stopword removal, stemming, dan ekstraksi fitur menggunakan TF-IDF. Model SVM diterapkan untuk mengklasifikasikan komentar ke dalam kategori positif, negatif, dan netral. Berdasarkan hasil evaluasi, algoritma SVM menunjukkan tingkat akurasi sebesar 92%, yang menandakan efektivitasnya dalam mengidentifikasi sentimen publik. Penelitian ini diharapkan dapat berkontribusi pada pengembangan metode analisis sentimen serta meningkatkan pemahaman terhadap persepsi masyarakat mengenai kinerja pemerintahan.
Co-Authors Dwi Handoko Dwirgo Sahlinal Fitra, Jaka Halim Fathoni Mezan El-Khaeri Kesuma Muhamad Cahyo Rifki Dwi Putra Muhammad Reza Redo I Islami Rima Maulini Rizka Permata Sylvia Sylvia Sylvia