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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JOIV : International Journal on Informatics Visualization Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Sosial
Prabowo, Muhamad Cahyo Ardi
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Journal : JOIV : International Journal on Informatics Visualization

 1 
Development of an IoT-Based Egg Incubator with PID Control System and Web Application Prabowo, Muhamad Cahyo Ardi; Sayekti, Ilham; Astuti, Sri; Nursaputro, Septiantar Tebe; Supriyati, Supriyati
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2044

Abstract

The rapid development of technology significantly impacts various aspects of life, including the field of livestock farming. The advancement of technology is expected to enhance the rate and effectiveness of production, particularly in the hatching of chicken eggs or chick breeding. The existing technology relies on manual on/off systems and manual monitoring, hindering successful egg-hatching rates and percentages. Therefore, this research aims to explain the development of an automated egg incubator using a Proportional Integral Derivative (PID) control system with hypertuning parameters, as well as temperature and humidity monitoring, along with a protection system based on voltage sensors, all integrated with the Internet of Things (IoT). The PID control is employed to regulate the temperature of the egg incubator, ensuring stability according to the predetermined set point temperature. The IoT system in this study comprises an ESP32 node as a microcontroller connected to a sensor, using Firebase and User app for monitoring the egg incubator. The study employed PID control with parameter values Kp=10, Ki=3, and Kd=8. The research yielded time-efficient egg incubation and prevention of turning delays. The DHT21 sensor achieved a 90% success rate in detecting room temperature (38°C) and humidity (77%-84%) within the incubator, while PID control effectively maintained the target temperature. The ACS712 sensor accurately detected current in the heater, power supply, and motor. The Kodular application can display sensor readings. The future implication is developing a more adaptive PID method toward changes and nonlinear dynamics. 
Determining the Rice Seeds Quality Using Convolutional Neural Network Hidayat, Sidiq Syamsul; Rahmawati, Dwi; Prabowo, Muhamad Cahyo Ardi; Triyono, Liliek; Putri, Farika Tono
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1175

Abstract

Seed inspection is crucial for plant nurseries and farmers as it ensures seed quality when growing seedlings. It is traditionally accomplished by expert inspectors filtering samples manually, but there are some challenges, such as cost, accuracy, and large numbers. Speed and accuracy were the main conditions for increasing agricultural productivity. Machine learning is a sub-science of Artificial Intelligence that can be applied in research on the classification of rice seed quality. The pipeline of a machine learning system is dataset collection, training, validation, and testing. Model making begins with taking data on the characteristics of rice seeds based on physical parameters in the form of seed shape and color. The dataset used is two thousand images divided into two categories, namely superior seeds and non-superior seeds. Training and Validation was conducted using the Convolutional Neural Network (CNN) algorithm with the concept of cross-validation on Google Collaboratory notebooks. The ratio split of train data and validation data in modeling from a dataset is 80:20. The result of the model formed is a model with the development of a Deep Convolutional Neural Network (Deep CNN) that can classify the digital image data of rice seeds from the results of data calls uploaded into the system. The results of the experiment conducted on 30 test data can be analyzed so that the system can classify superior and non-superior seeds with a precision value of 93% and a recall of 95%.
Co-Authors Anggraeni K., Sri Bermana, Cello Flaurecita Gadis Cendrasari, Erie Rosita DWI RAHMAWATI Eni Dwi Wardihani, Eni Dwi Ilham Sayekti Indra, Ragil Tri Janitra, Atikah Ayu Kabir, Noer Ni'mat Syamsu Khamami Khamami, Khamami Luthfia, Irrin Mirrah Mara, Muhlasah Novitasari Nursaputro, Septiantar Tebe Pramuji, Tulus Putri, Farika Tono Rahardjo, Parsumo Ramadhan, Zidan Rifqi Sarono Widodo, Sarono Sidiq Syamsul Hidayat, Sidiq Syamsul Sri Astuti Sri Wahyuni Supriyati . Taufiq Yulianto Thomas Agung Setyawan, Thomas Agung Triyono, Liliek