Intelligent Detection of Rice Leaf Diseases Based on Histogram Color and Closing Morphological

Yunianta, Arda and Firdausiah Mansur, Andi Besse and Firgiyanto, Refa and Agus Kurniasari, Arvita and Sarwo Setyohadia, Dwi Putro Sarwo and Yufit Riskiawan, Hendra and Basori, Ahmad and Dwi Pupitasari, Trismayanti (2022) Intelligent Detection of Rice Leaf Diseases Based on Histogram Color and Closing Morphological. Emirates Journal of Food and Agriculture. ISSN 2079-052X

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Abstract

Based on data released by APJII in 2020, internet users in Indonesia which reached 73.7% of the Indonesian population are directly proportional to marketing activities through the internet, one of which is through the social media platform Instagram. Many business people communicate with their prospective customers through Instagram, one of which is the delivery MSMEs in Jember Regency. One of the benchmarks for measuring the level of interaction between MSMEs and potential consumers is through the engagement rate. We carry out a data mining process to track and summarize engagement activities on delivery MSME Instagram accounts in Jember district by utilizing a scraping technique based on the PHP programming language which is connected to the RESTful Application Programming Interface (API) provided by Instagram. From the interpolation calculation based on the correlation between the number of followers and the average value of the engagement rate, we categorize the standard value of the average engagement rate according to the case study of this research. From the evaluation results, it was found that the majority of accounts still have an average engagement value below the ideal value with a deviation value around -6.39%

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