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All Journal JURNAL SISTEM INFORMASI BISNIS JOIV : International Journal on Informatics Visualization Petra International journal of Business Studies (IJBS) INSIST (International Series on Interdisciplinary Research) Jurnal Teknologi Terpadu MULTINETICS Jurnal Abdi Insani BHAKTI PERSADA Jurnal Aplikasi IPTEKS (Journal of Applied Sciences and Technology) Jurnal Ilmu Kesehatan Immanuel
Maria Agustin
Fakultas Psikologi Universitas Merdeka Malang
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Design of Livestream Video System and Classification of Rice Disease Agustin, Maria; Hermawan, Indra; Arnaldy, Defiana; Muharram, Asep Taufik; Warsuta, Bambang
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1336

Abstract

One of the agricultural products which is an important aspect of the life of Indonesian people is rice. Rice disease has a devastating effect on rice production, while detecting rice diseases in real-time is still difficult. Therefore, this study designed a Livestream video system that is equipped with a rice disease Classification system. The Livestream system utilizes 4G network communication and is assisted by the WebSocket protocol to communicate in real-time and for the rice disease Classification system using YOLO algorithm. In addition, Livestream uses the raspberry pi camera V2 to take video stream data. In analyzing the performance of the Livestream system, four tests were carried out, namely: functionality test, connectivity test, classification performance test, and implementation performance test. The test was carried out using the wireshark and conky tools, while the classification training used 5447 images from the Huy Minh do dataset that he provided on the Kaggle website. The results show that all programs run well and get a good QoS value according to the index of the parameter results, it is also found that sending non-base64 can reduce the size of the data to approximately 200,000 bytes/s and the performance of the classification system is good because it has an average accuracy of 80% even though it is quite burdening the raspberry pi. This system can still be optimized and developed further to support research in the field of data transmission and the performance of machine learning in a microcontroller.
Co-Authors Ali, Euis Oktavianti Aprilia, Puput Dwi Asep Kurniawan Asep Taufik Muharram Dautrey, Jean Marc Defiana Arnaldy Dewi Yanti Liliana Herlina Herlina Hermayanti, Elva HR, Des Fitria Putri Huzaifa, Malisa INDRA HERMAWAN Kadarwati, Dinda Kana, Vera Radja Marcheta, Noorlela Matin, Iik Muhamad Malik Mulyani, Meutia Tri Munawaroh Munawaroh Nalawati, Rizki Elisa Nathanael, David Prihatin Oktivasari Putri, Dini Marsela Ratna Widya Iswara, Ratna Widya Saarce Elsye Hatane Sari, Risna Sukma, Yunila Supani, Ahyar Susanti Susanti Toni Prahasto Warsuta, Bambang Widyono, M. Farishanif Yuli Andriani Zain, Ayu Rosyida