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All Journal KLIK: Kajian Ilmiah Informatika dan Komputer Adpebi Science Series
Khairani Daulay, Nelly
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Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Economics, Econometrics & Finance Environmental Science

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Automatic Cat Feeding And Monitoring System in Hiro Catshop Shop Based on The Internet of Things Khairani Daulay, Nelly; Novi Lestari; Armanto; Deni Nurdiansyah; Rahmad Dani; Alfia Tiara Permatasari
Adpebi Science Series 2022: 1st AICMEST 2022
Publisher : ADPEBI

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Abstract

Cat feeding is still done manually without a system and there is no monitoring system for cat food leftovers that can be accessed through the website, Build a Monitoring System and Automatic Cat Feeder at Hiro CatShop Based on the Internet Of Things (IoT) that can monitor and provide food cat automatically and can be accessed remotely via the Website. The method that the author uses in conducting this research is a qualitative research method. Qualitative research methods seek an understanding of meaning, understanding, reality, events, or life by being directly and/or indirectly involved in the environment under study. cat in place, DHT11 sensor as a temperature detector in the room around the cat food place, servo motor as a means of opening the lid for cat food that comes out according to a predetermined schedule, NodeMCU to read and send sensor data into the database With an automatic cat feeding monitoring system this will make it easier for shop owners or shop employees to monitor cat feeding through the website, shop owners don't need to be afraid anymore if it's too late to feed the cat and waste too much time to come to the cat feeding place and also don't need to be afraid of losing n data caused by lost records (human error).
Klasifikasi Penyakit Pada Daun Tanaman Padi Berbasis YoloV5 (You Only Look Once) Aditia Putra Pranjaya; Rizki, Fido; Kurniawan, Rudi; Khairani Daulay, Nelly
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1916

Abstract

The rise of disease attacks on plant leaves causes huge losses for farmers, especially rice farmers. Lack of knowledge in identifying the symptoms of disease in rice plants can cause farmers to have difficulty in dealing with diseases that attack their rice plants, causing errors in handling diseases in rice plants that result in crop failure. When looking at the facts that occur today, it is very necessary to have a technology that can be used to recognise diseases in rice plants, so that it can help rice farmers in recognising a symptom of a disease that attacks their rice plants. With the application of computer vision using the YOLOv5 algorithm, we can create an introduction system related to diseases in rice plants based on the type of disease. In the process of applying the YOLOv5 algorithm, we will collect as many as 1500 images of 2 types of diseases and 1 type of normal rice leaves and each class we collect 500 images, and divide the data into 3 parts, the percentage of which is 70% for train data, 20% for valid data and 10% for test data and this process we do in Roboflow for image labelling and dataset creation. We will process the dataset from roboflow using the YOLOv5 algorithm. Based on the model evaluation results, the highest value of mAP 95%, precision 88%, recall 100% is obtained. The last stage is testing the system in real-time with a webcam and producing a test accuracy value in the Narrow Brown Spot class of 93%, in the Leaf Blight class of 81%, and Normal Rice Leaves 91%.
Co-Authors Aditia Putra Pranjaya Alfia Tiara Permatasari Armanto Deni Nurdiansyah Novi Lestari Rahmad Dani Rizki, Fido Rudi Kurniawan