Kombinasi Metode Fitur Ekstraksi untuk Indentifikasi Penyakit pada Daun Teh

Teh merupakan salah satu minuman yang paling banyak dikonsumsi di dunia, namun produksi teh seringkali terhambat dan mengalami penurunan oleh berbagai penyakit yang mempengaruhi pertumbuhan dan kualitas daun teh. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi penyakit daun teh dengan memanfaatkan teknologi Image Classification dan menerapkan metode kombinasi analisis tekstur Haralick, Color Histogram, Hu Moment dan pengklasifikasian objek menggunakan Random Forest classifier. Dataset yang digunakan dalam penelitian ini dikumpulkan dari perkebunan teh Johnstone Boiyon di Koiwa, Kabupaten Bomet, Kenya dengan jumlah 1510 citra yang terbagi menjadi 8 kelas. Pra pemrosesan pada penelitian ini dilakukan dengan menambahkan tahapan augmentasi data untuk memperoleh jumlah citra yang lebih besar sehingga algoritma dapat mempejalari pola lebih banyak. Hasil penelitian menunjukkan bahwa kombinasi dari metode yang diusulkan mencapai akurasi 99% dengan nilai standard deviasi yang rendah sebesar 0.001055% yang menunjukkan keefektifan kombinasi analisis tekstur Haralick, Color Histogram, dan Hu Moment serta Random Forest Classifier dalam mengklasifikasikan penyakit daun teh

IMPLEMENTATION OF THE RIJNDAEL ALGORITHM ON WEB-BASED WHISTLEBLOWING SYSTEM

In carrying out its responsibilities, an employee works for an agency or company and also works with his colleagues, whether they are co-workers or their own superiors. So it is very important for an employee to gain trust in his work environment. If there is a violation or behavior that deviates from an employee in the work environment, then there must be someone who reports it but of course by protecting the identity of the reporter. Based on these problems, the authors make and design a web-based whistle blowing application to protect the identity of people who report violations that occur in their work environment. This whistle blowing web is created using cryptographic algorithm methods. Cryptographic algorithms work by disguising data or information into a form of password that has no meaning. The author uses the Rijndael algorithm to encrypt the complainant's data. So that by using the Rijndael algorithm on this web-based Whistleblowing system, the data or reporting information will be safe in the database and it is hoped that an optimal system will be created for data and information securit

Feature Extraction With Forest Classifer To Predicate Covid 19 Based On Thorax X-Ray Results

Coronavirus 19 (COVID-19) is a highly contagious infection caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a new virus for which no cure has been found, marked by the increasing death rate worldwide. Coronavirus disease which can cause pneumonia which attacks the air sacs of the lungs with symptoms of dry cough, sore throat to acute respiratory distress (ARDS) that occurs in COVID-19 patients. One of the ways to detect the virus is by detecting chest X-rays in the patient. Over the past decade's mechine learning technology has developed rapidly and is integrated into CAD systems to provide accurate accuracy. This research was conducted by detecting thoracic radiographs using feature extraction Hu-Moments, Harralic and Histogram and detecting the best accuracy with a classification algorithm to detect the results of COVID-19. The study was conducted by testing the dataset obtained from the Kaggle repository which has images, namely 1281 X-rays of COVID-19, 3270 X-rays Normal, 1656 X-rays of  pneumonia, and X-rays of bacteria-pneumonia 3001. In general, this research is included in the Good category because it produces the highest accuracy by the Random forest classification algorithm where the accuracy result is 84% and the standard deviation is 0.015847. In addition, the research also produced Kappa of 0.713. The results of this accuracy are carried out in several stages, namely by feature extraction in the form of hu-moments, Harralic and histogram. In this study, the best results were given by the Random forest algorithm with feature extraction Histogram and Hu-Moment

Chicken Disease Detection Based on Fases Image Using EfficientNetV2L Model

Livestock farming requires technological innovation to increase productivity and efficiency. Chickens are a livestock animal with good market prospects. However, not all farmers understand about chicken diseases and signs of sickness. Detection of chicken diseases can be done through various methods, one of which is by looking at the shape of the chicken's feces. Images in feces can be detected using machine learning. Convolutional Neural Networks (CNN) are used to speed up disease prediction. Transfer learning is used to leverage knowledge that has been learned by previous models. In this study, we propose our own CNN architecture model and present research by building a new model to detect and classify diseases in chickens through their feces. The model training process is carried out by inputting training data and validation data, the number of epochs, and the created checkpointer object. The hyperparameter tuning stage is carried out to increase the accuracy rate of the model. The research is conducted by testing datasets obtained from the Kaggle repository which has images of coccidiosis, salmonella, Newcastle, and healthy feces. The results of the study show that our proposed model only achieves an accuracy rate of 93%, while the best accuracy rate in the study is achieved by using the EfficientNerV2L model with the RMSProp optimizer, which is 97%

Penerapan Metode Belajar Daring pada Warga Rt 002/02 Tegal Parang di Masa Pandemi Covid-19

Dunia telah memasuki era globalisasi yang diawali dengan persaingan pengembangan dan penerapan teknologi baru di negara-negara maju. Dengan kondisi saat ini, sebagian besar pendidikan di Indonesia  masih didominasi oleh model pendidikan online. Oleh karena itu, untuk mendukung penyelenggaraan pendidikan dan meningkatkan kualitas pendidikan, teknologi informasi dan komunikasi harus dimanfaatkan secara optimal. Salah satu  pemanfaatan teknologi informasi dan komunikasi dalam pendidikan adalah munculnya konsep e-learning yang mempengaruhi transformasi pendidikan tradisional ke dalam format digital. Hal ini tidak terlepas dari Rukun Tetangga (RT) 002/02 Kecamatan Tegal Parang, Jakarta Selatan, dimana kondisi pandemi saat ini memaksanya untuk melakukan kegiatan belajar mengajar secara daring (daring) dan pengetahuannya tentang penerapan beberapa program ke sel. memperluas ponsel atau laptop. Untuk mendukung kegiatan belajar mengajar, banyak warga siswa yang  hanya mengandalkan chat (WhatsApp) untuk berkomunikasi dengan pihak sekolah atau mengirim video dan mengajukan pertanyaan-pertanyaan prakti