Pemanfaatan Data Mining untuk Klasifikasi Penyakit Daun pada Tebu dan Cara Pencegahan Penyakit dengan Metode Algoritma K-Nearest Neighbors
DOI:
https://doi.org/10.62951/bridge.v3i3.580Keywords:
data mining, disease prevention, k-nearest neighbors algorithm, leaf disease classification, sugarcaneAbstract
This study aims to utilize data mining techniques, specifically the K-Nearest Neighbors (KNN) algorithm, to classify leaf diseases in sugarcane (Saccharum officinarum). Early and accurate detection of leaf disease types is a crucial step in prevention and control strategies, thereby reducing potential crop losses caused by pathogen attacks. Leaf diseases in sugarcane, such as leaf scald, rust, and mosaic virus, are known to affect photosynthesis, inhibit growth, and reduce the quality and quantity of sugarcane produced. The classification process in this study was carried out through image analysis of infected sugarcane leaves, where features such as color, texture, and shape were extracted using digital image processing techniques. The KNN algorithm was chosen because of its non-parametric nature, ease of implementation, and its ability to provide accurate classification results even with limited data size. The working principle of KNN is to determine the class of a new sample based on the majority class of its k nearest neighbors in the feature space, making it very suitable for the case of leaf disease image classification. In addition to building a classification model, this study also examines disease prevention strategies based on the identification results. These strategies include the use of disease-resistant sugarcane varieties, the implementation of appropriate planting patterns, land moisture management, regular plantation sanitation, and the measured and environmentally friendly use of pesticides or fungicides. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess model effectiveness across various data scenarios. The results of this study are expected to not only contribute to the development of decision support systems for farmers and related parties but also support the application of artificial intelligence-based technology in the agricultural sector.
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References
Afifi, W. (2022). Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Pt Indosat Tbk Dengan Metode K-Nearest Neighbor (K-Nn) Dan Naive Bayes Classifier (Nbc) (Bachelor's Thesis, Fakultas Sains Dan Teknologi Uin Syarif Hidayatullah Jakarta).
Alfredo, J., Resita, C., & Gustiawati, R. (2020). Motivasi Berprestasi Peserta Ekstrakulikuler Futsal Di Kecamatan Cikarang Selatan Kabupaten Bekasi. Jurnal Literasi Olahraga, 1(2). https://doi.org/10.35706/jlo.v1i2.3961
Arianto, T. (2024). Realitas Budaya Masyarakat Urban. Yayasan Tri Edukasi Ilmiah.
Asri, R. M., Hidayat, N., Fauzi, M. A., Kom, S., & Kom, M. (2016). Pemodelan System Pakar Untuk Identifikasi Penyakit Pada Tanaman Kedelai Menggunakan Metode Fuzzy K-Nearest Neighbor. S1. Universitas Brawijaya.
Harjanti, T. W., & Himawan, H. (2021). Teknologi Pengolahan Citra Digital Untuk Ekstraksi Ciri Pada Citra Daun Untuk Identifikasi Tumbuhan Obat. Faktor Exacta, 14(3), 150-159. https://doi.org/10.30998/faktorexacta.v14i3.9841
Hendriyani, H. (2024). Kajian Makna Simbolis Ornamen Pada Interior Area Ibadah Masjid Al-Jabbar, Bandung (Doctoral Dissertation, Universitas Komputer Indonesia).
Ikhwan, A., & Aslami, N. (2020). Implementasi Data Mining Untuk Manajemen Bantuan Sosial Menggunakan Algoritma K-Means. (Jurti) Jurnal Teknologi Informasi, 4(2), 208-217 https://doi.org/10.36294/jurti.v4i2.2103
Irfansyah, D., Mustikasari, M., & Suroso, A. (2021). Arsitektur Convolutional Neural Network (Cnn) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi. Jurnal Informatika: Jurnal Pengembangan It, 6(2), 87-92. https://doi.org/10.30591/jpit.v6i2.2802
Lubis, A. A., Dewy, M. S., & Isnaini, M. (2023). Pengembangan Aplikasi Pembelajaran Algoritma Glcm Dan Knn Dalam Pengenalan Penyakit Pada Daun. Jurnal Tik Dalam Pendidikan, 10(2). https://doi.org/10.24114/jtikp.v10i2.54010
Mustofa, M. I., Furqon, M. T., & Ratnawati, D. E. (2022). Penggunaan Metode Ekstraksi Fitur Tekstur Gray Level Co-Ocurrrence Matrix Dan K-Nearest Neighbor Untuk Identifikasi Jenis Penyakit Tanaman Apel. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 6(9), 4451-4458.
Sujaini, H. (2019). Klasifikasi Citra Alat Musik Tradisional Dengan Metode K-Nearest Neighbor, Random Forest, Dan Support Vector Machine. Jurnal Sistem Informasi Bisnis, 9(2), 185-191. https://doi.org/10.21456/vol9iss2pp185-191
Syaputra, A. (2024). Klasifikasi Penyakit Daun Pada Tebu Dengan Pendekatan Algoritma K-Nearest Neighbors, Multilayer Perceptron Dan Support Vector Mechine. Jurnal Ilmiah Informatika Global, 15(3), 131-138. https://doi.org/10.36982/jiig.v15i3.4856
Syaputra, A. (2024). Klasifikasi Penyakit Daun Pada Tebu Dengan Pendekatan Algoritma K-Nearest Neighbors, Multilayer Perceptron Dan Support Vector Machine. Jurnal Ilmiah Informatika Global, 15(3), 131-138. https://doi.org/10.36982/jiig.v15i3.4856
Van Klinken, G. (2015). The Making Of Middle Indonesia: Kelas Menengah Di Kota Kupang 1930an-1980an. Yayasan Pustaka Obor Indonesia. https://doi.org/10.1163/9789004265424
Wardiman, B., Fitriyani, E., Herlyani, S., Ashar, J. R., & Panga, N. J. (2024). Pertanian Keberlanjutan. Tohar Media.
Yulianti S, H. (2020). Klasifikasi Tanaman Obat Menggunakan Metode Gray Level Co-Occurrence Matrix Dan Support Vector Machine (Doctoral Dissertation, Universitas Komputer Indonesia).
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