Implementation of Bot Telegram as Broadcasting Media Classification Results of Convolutional Neural Network (CNN) Images of Rice Plant Leaves

Adi Fajaryanto; Fauzan Masykur; Mohammad  Rizqi Rosyadi

doi:10.47709/cnahpc.v5i1.1976

Authors

Adi Fajaryanto Universitas Muhammadiyah Ponorogo
Fauzan Masykur Universitas Muhammadiyah Ponorogo
Mohammad Rizqi Rosyadi Universitas Muhammadiyah Ponorogo

DOI:

10.47709/cnahpc.v5i1.1976

Keywords:

CNN, Quality of Service, Rice Plants, Deep Learning

Dimension Badge

Abstract

Rice plants play an important role in the life of the Indonesian people because rice is the raw material for rice as a staple food. The rice production process does not rule out the possibility of interference by pests and diseases resulting in losses that cause crop failure. Meanwhile, pests on rice plants can be caused by various types, namely types of fungi (leafblast, hispa, brownspot) and types of nuisance animals. In this research, it will be carried out how to classify the image of rice plant leaves using the deep learning Convolutional Neural Network (CNN) algorithm, then the results of the classification are sent to users by utilizing the telegram chat application. The rice plant leaf image dataset is grouped into 4 groups (leafblast, brownspot, hispa and healthy). From several experiments it can be seen the results of system performance, namely the classification speed takes 30-60 seconds.

Downloads

Download data is not yet available.

Google Scholar Cite Analysis
Abstract viewed = 229 times

References

Adhiwibowo, W., Wahyu Christanto, F., & Firman Daru, A. (2021). Implementasi API Bot Telegram untuk Sistem Notifikasi pada The Dude Network Monitoring System. 593–599.

Anami, B. S., Malvade, N. N., & Palaiah, S. (2020). Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images. Artificial Intelligence in Agriculture, 4, 12–20. https://doi.org/10.1016/j.aiia.2020.03.001

Chen, W. L., Lin, Y. B., Ng, F. L., Liu, C. Y., & Lin, Y. W. (2020). RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies. IEEE Internet of Things Journal, 7(2), 1001–1010. https://doi.org/10.1109/JIOT.2019.2947624

Cobantoro, A. F. (2018). ANALISA QoS (QUALITY OF SERVICE) PADA JARINGAN RT-RW NET DENGAN KENDALI RASPBERRY PI. Network Engineering Research Operation, 4(1), 31–36. https://doi.org/10.21107/nero.v4i1.109

Defriani, M., & Jaelani, I. J. (2022). Recognition of Regional Traditional House in Indonesia Using Convolutional Neural Network (CNN) Method. Journal of Computer Networks, Architecture and High Performance Computing, 4(2), 104–115. https://doi.org/10.47709/cnahpc.v4i2.1562

Fitriansyah, Fifit, A. (2020). Penggunaan Telegram Sebagai Media Komunikasi Dalam Pembelajaran Online. Jurnal Humaniora Bina Sarana Informatika, 20(Cakrawala-Jurnal Humaniora), 113.

Gentia, D., Sukarsa, I. M., & Wibawa, K. S. (2020). Rancang Bangun Chatbot Sebagai Penghubung Komunikasi Antara Aplikasi Line Messenger Dengan Telegram Messenger. Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), 8(3), 156. https://doi.org/10.24843/jim.2020.v08.i03.p01

Haryono Wibowo, G., & Ratna Widiasari, I. (2023). Automation of two Ubuntu servers with Ansible and Telegram as notifications. Jurnal Dan Penelitian Teknik Informatika, 8(1). https://doi.org/10.33395/sinkron.v8i1.11968

Mahar Maligan, J., Dian Pratiwi, D., & Dewanti Widyaningsih, T. (2019). Studi Preferensi Konsumen terhadap Nasi Putih dan Nasi Jagung Putih pada Pekerja Wanita di Kantor Pemerintah Kota Malang. Indonesian Journal of Human Nutrition, 6(1), 41–52. https://doi.org/10.21776/ub.ijhn.2019.006.01.5

Masykur, F., Prasetyo, A., Widaningrum, I., Cobantoro, A. F., & Setyawan, M. B. (2020). Application of Message Queuing Telemetry Transport (MQTT) Protocol in the Internet of Things to Monitor Mushroom Cultivation. 7th International Conference on Information Technology, Computer, and Electrical Engineering, ICITACEE 2020 - Proceedings, 135–139. https://doi.org/10.1109/ICITACEE50144.2020.9239118

Masykur, F., Setyawan, M. B., & Winangun, K. (2022). Epoch Optimization on Rice Leaf Image Classification Using Convolutional Neural Network (CNN) MobileNet. CESS (Journal of Computer Engineering, System and Science), 7(2), 581. https://doi.org/10.24114/cess.v7i2.37336

Putri Dewi, Y. D., & Purwidiani, N. (2015). Studi Pola Konsumsi Makanan Pokok pada Penduduk Desa Pagendingan Kecamatan Galis Kabupaten Pamekasan Madura. E-Journal Boga, 4(3), 108–121.

Sibiya, M., & Sumbwanyambe, M. (2019). A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks. AgriEngineering, 1(1), 119–131. https://doi.org/10.3390/agriengineering1010009

Sreevallabhadev, R. (2020). An improved machine learning algorithm for predicting blast disease in paddy crop. Materials Today: Proceedings, 33(xxxx), 682–686. https://doi.org/10.1016/j.matpr.2020.05.802

Wibowo.?;Kurniawan, ; (2019). BOT TELEGRAM SEBAGAI MEDIA ALTERNATIF AKSES INFORMASI AKADEMIK Program Studi Informatika - Universitas Muhammadiyah Surakarta Jurnal Ilmiah Komputer dan Informatika ( KOMPUTA ). Jurnal Ilmiah Komputer Dan Informatika (KOMPUTA), 8(1).