Main Article Content
Rice is one of the most favored crops by the Indonesian people, because of its many benefits, especially as a staple food for Indonesians, and is also used as a raw material for the feed and food industry. In particular, rice is processed to produce rice which contains high carbohydrates, so that rice is widely used and used as a human staple food. Some things that often happen at this time by rice farmers, many losses caused by rice plant diseases that are too late to be identified, causing crop failure. In this case, this rice plant disease is still in a mild stage, but many farmers ignore it, so that a bigger and wider problem arises and it is too late to control. The purpose of this study is to assist rice farmers in identifying rice plant diseases, which will use the Tsukamoto fuzzy method and implement it into the system, so that farmers do not feel overwhelmed again in identifying rice plant diseases. In general, Fuzzy can be referred to as uncertain logic but its advantage is that it is capable of the punishment process so that its design does not require complex mathematical equations. There are various fields that can be used by fuzzy logic, one of which is to identify rice plant diseases
W. Febrianto, A. Yudha Suryatama, N. Afrianto, I. Mualana, P. Nur Hidayat, and J. Ipmawati, “Analisis Sistem Pakar Untuk Mengidentifikasi Penyakit Dan Hama Pada Tanaman Padi Dengan Metode Bayes,” Smart Comp Jurnalnya Orang Pint. Komput., vol. 8, no. 1, pp. 11–16, 2019, doi: 10.30591/smartcomp.v8i1.1311.
O. Nurdiawan, “Penerapan Sistem Pakar Menggunakan Metode Fuzzy Sugeno Identifikasi Hama Tanaman Padi,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 5, no. 1, pp. 45–59, 2018, doi: 10.35957/jatisi.v5i1.112.
H. B. Prajapati, J. P. Shah, and V. K. Dabhi, “Detection and classification of rice plant diseases,” Intell. Decis. Technol., vol. 11, no. 3, pp. 357–373, 2017, doi: 10.3233/IDT-170301.
R. N. Whidhiasih and I. Ekawati, “Identifikasi Jenis Penyakit Daun Padi Menggunakan Adaptif Neuro Fuzzy Inferene System ( ANFIS ),” Semin. Nas. Energi Dan Teknol., pp. 131–140, 2019.
Y. Wendra and D. Aldo, “Metode Case Based Reasoning Untuk Identifikasi Penyakit Tanaman Padi,” Jursima, vol. 8, no. 2, pp. 103–110, 2020.
D. A. Puryono, “Sistem Informasi Pendeteksi Hama Penyakit Tanaman Padi Menggunakan Metode Fuzzy Tsukamoto Berbasis Android,” vol. 10, no. 2, pp. 63–69, 2018, doi: 10.31219/osf.io/hpk5s.
E. Nugraha, A. P. Wibawa, M. L. Hakim, U. Kholifah, R. H. Dini, and M. R. Irwanto, “Implementation of fuzzy tsukamoto method in decision support system of journal acceptance,” J. Phys. Conf. Ser., vol. 1280, no. 2, 2019, doi: 10.1088/1742-6596/1280/2/022031.
S. Hardi, A. Triwiyono, and Amalia, “Expert System for Diagnosing Osteoarthritis with Fuzzy Tsukamoto Method,” J. Phys. Conf. Ser., vol. 1641, p. 012107, 2020, doi: 10.1088/1742-6596/1641/1/012107.
D. Sitanggang et al., “Diagnosing chicken diseases using fuzzy Tsukamoto web-based expert system,” IOP Conf. Ser. Mater. Sci. Eng., vol. 505, no. 1, 2019, doi: 10.1088/1757-899X/505/1/012086.
J. Jufriadi, G. W. Nurcahyo, and S. Sumijan, “Logika Fuzzy dengan Metode Mamdani dalam Menentukan Tingkat Peminatan Tipe Motor Honda,” J. Inform. Ekon. Bisnis, vol. 3, pp. 7–11, 2020, doi: 10.37034/infeb.v3i1.60.
W. Febriani, G. W. Nurcahyo, and S. Sumijan, “Diagnosa Penyakit Rubella Menggunakan Metode Fuzzy Tsukamoto,” J. Sistim Inf. dan Teknol., vol. 1, no. 3, pp. 12–17, 2019, doi: 10.35134/jsisfotek.v1i3.4.
R. R. Putra, H. Hamdani, S. Aryza, and N. A. Manik, “Sistem Penjadwalan Bel Sekolah Otomatis Berbasis RTC Menggunakan Mikrokontroler,” J. Media Inform. Budidarma, vol. 4, no. 2, p. 386, 2020, doi: 10.30865/mib.v4i2.1957.
N. Musyaffa and M. Ryansyah, “Implementation of VPN Using Router MikroTik at Al-Basyariah Education Foundation Bogor,” vol. 12, no. 2, pp. 49–55, 2020.
A. Setiawan, B. Yanto, and K. Yasdomi, LOGIKA FUZZY Dengan MATLAB (Contoh Kasus Penelitian Penyakit Bayi dengan Fuzzy Tsukamoto), vol. 1, no. March. 2018.
Kaur, S., Pandey, S., & Goel, S. (2019). Plants disease identification and classification through leaf images: A survey. Archives of Computational Methods in Engineering, 26(2), 507-530.
Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180, 96-107.
Hang, J., Zhang, D., Chen, P., Zhang, J., & Wang, B. (2019). Classification of plant leaf diseases based on improved convolutional neural network. Sensors, 19(19), 4161
Bera, T., Das, A., Sil, J., & Das, A. K. (2019). A survey on rice plant disease identification using image processing and data mining techniques. In Emerging Technologies in Data Mining and Information Security (pp. 365-376). Springer, Singapore.
Khan, N., Bano, A., Ali, S., & Babar, M. A. (2020). Crosstalk amongst phytohormones from planta and PGPR under biotic and abiotic stresses. Plant Growth Regulation, 90(2), 189-203.