Penyiraman merupakan pekerjaan yang bersifat rutinitas paling penting untuk tanaman agar terus tumbuh dan berkembang. Sistem penyiraman secara otomatis dapat meringankan beban untuk menyediakan air ketika tanaman membutuhkannya, otomatisasi dapat digunakan atau dimanfaatkan untuk membantu mengerjakan yang bersifat rutinitas karena dapat berjalan terus menerus tanpa mengenal waktu. Mengetahui kapan penyiraman dilakukan adalah aspek penting dari proses penyiraman. Proyek ini menggunakan papan Arduino Uno, yang terdiri dari Mikrokontroler ATmega 328, Soil Moisture Sensor, LCD, DHT22, Relay dan Pompa. Arduino Uno berguna untuk menghadapi permasalahan yang terjadi pada kehidupan saat ini. Sistem ini diprogram sedemikian rupa sehingga akan merasakan tingkat kelembaban tanaman dan menyediakan air jika diperlukan. Jenis sistem ini sering digunakan untuk perawatan tanaman umum, sebagai bagian dari merawat kebun kecil dan sedang. Sistem Penyiraman Otomatis Berbasis Mikrokontroller Arduino Uno mampu meningkatkan kinerja suatu organisasi ataupun instansi dalam pertanian atau perkebunan.
Dipole-dipole is one configuration design survey in electrical resistivity method which is common to interpreted shallow subsurface base on resistivity parameter, and the model can be developed using inversion formula. Otherwise, we developed some model without empirical mathematic formula; it is called Artificial Neural Network (ANN). ANN is a system which has a pattern like the human brain process to solve the complex problems. The research aims to develop a neural network algorithm using Matlab and compare the result 2D model resistivity between ANN and inversion by Res2Dinv (existing procedure) software. The research was done in Taman Rumah Kita (TRK) where located in Faculty of Science and Mathematics, Diponegoro University. A cylinder was buried in the center of TRK and getting the best architecture of the network and the value of Mean Square Error (MSE) of the output network. The backpropagation artificial neural network was built from many layers such as an input layer, hidden layer, and output layer and developed by Matlab programs. The Network train was tested using synthetic data and field data. The synthetic data was made with forwarding modeling method by Res2Mod software, and the field data was obtained by doing the measurement at the measurement site using dipole-dipole resistivity method. The comparing result models are present the best architecture obtained one input layer with three input units, three hidden layers with each layer has 100 neurons and one output layer that obtained by trial and error process. MSE obtained respectively in observation lines are 0.0210 at Line 1, and 0.0345 at Line 2.
Heart sounds have a special pattern that can indicate a person's heart condition. An abnormal heart will produce a characteristic sound that is often called a murmur ( Sitinjak , 2008). Murmurs are caused by various things that can indicate a person's heart condition. From these murmurs can be known the types of abnormalities experienced by patients. In this study, cardiac abnormalities that can be identified are aortic stenosis (as), mitral regurgitation (mr), mitral valve prolapse (mvp), mitral stenosis (ms), and normal. The data used for training as many as 1000 heart sound files consisting of 200 files each for each heart abnormality.Data in the form of heart rate sound samples with the format. Wav. The program was created using the Artificial Neural Network method to identify the five types of cardiac abnormalities. The training method is created using the traingdx function provided in the Neural Network Toolbox on MATLAB. Based on the results of the training can be obtained a validity value of 97,7%.
The purpose of this research is to produce a system that can stabilize REMOTELY OPERATED UNDERWATER VEHICLE (ROV) using an ARTIFICIAL NEURAL NETWORK (ANN) smart system. The way the system works is done by reading the input in the form of an Accelerometer and Gyroscope sensor which is then processed using a microcontroller and the output is PWM (Pulse Width Modulation) which is interpreted by the ESC (Electronic Speed Control) driver to move the motor according to the speed it should be. In addition, the microcontroller also has to determine which direction the motorbike can go against from the ocean currents or the source of the shock it receives. ANN in this system is used to determine the size of the ROV’s freedom of air so that the motor can withstand any unstable external shocks (air currents) and still maintain its position and position.
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