“Face Mask Detection Using the Convolutional Neural Network” is a PC based program that aims to detect and classify human beings whether a person is using a mask or not with access through a webcam camera. This program is created using the Python language with several libraries. The classification of face masks uses the Convolutional Neural Network method with the MobileNetV2 architecture. Meanwhile, human face detection uses the Haarcascade Classifier. How the program works is by accessing the connected camera and if the person detected is wearing a mask, the person will be labeled "using a mask" and given a green box to mark the detection along with the analysis value, whereas if not, it will be labeled "not using a mask" and a red box with also the predicted value. From the test results, it can be proven that the accuracy program is good enough to detect the use of face masks with an average object detection accuracy of 88.53% and the classifier for the use of mask an average of 84.45%.
Diabetes adalah penyakit yang terjadi ketika kandungan glukosa di dalam darah tinggi. Tes glukosa yang menghasilkan keakuratan tinggi harus dilakukan beberapa kali untuk mendeteksi diabetes di dalam tubuh. Beberapa indikator di dalam tubuh dapat menjadi titik awal untuk mendeteksi diabetes. Bagaimanapun juga, keterbatasan seorang tenaga medis dalam mendeteksi dalam jumlah data yang sangat besar dengan cara manual menjadi kendala. Salah satu solusi untuk gap tersebut adalah menggunakan komputer sebagai perhitungan matematika dalam metode pengelompokan K-Means dan Fuzzy C-Means. Pengelompokan terdiri dari kelompok diabetes dan non-diabetes. Pengujian untuk masing-masing metode dilakukan terhadap 9 data. Hasil pengujian terbaik metode K-Means adalah 73,438% dan untuk metode Fuzzy C-Means adalah 82,812%.
Car Type Detection and Recogniton system is an application that is developed using You Only Look Once (YOLO) and Convolutional Neural Network (CNN) algorithm. This application purpose is to detect and recognize the car image from the data input. In this application the input image will be divided into two parts, namely the training image and test image. For the training image, the first step, the training image will be divided into two process stages, namely detecting the image of a car and searching for the unique features of a car.To detect the image of the car, the image will be processed to detect parts of the image of the car and not the car using the YOLO method. After obtaining a part of the car image, the image of the car will be saved as a detection model. The image that has been detected will be learning the car image by the CNN method. For the test images of the stages carried out as in the training image, after the image of the car is detected, an introduction will be made based on learning that has been done with the CNN method to obtain output in the form of a car that is successfully recognized and detected will be labeled by the application.
Tomato is one of the farming commodities in Indonesia, easy to plant but easy to get sick. Analizing the disease in plain view still not yet achieve high accuracy result, so we use the help of Convolutional Neural Network (CNN) algorithm. This research is quantitative, with image of a single tomato leaf that is infected as the input. The constructed model gains an accuracy of 58.33% with 12.716 image consisting of Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Target Spot, Spider Mites, Mosaic Virus, Yellow Leaf Curl Virus, Septoria Leaf Spot and healthy leaf. The conclusion from this research is that classification of Tomato leaf disease using CNN can help achieve a higher accuracy but using LeNet-5 as the model architecture is not very effective.
Sistem Isyarat Bahasa Indonesia merupakan salah satu media untuk berkomunikasi sesamakaum tunarungu. Maka untuk mendukung terwujudnya seperangkat isyarat jari, perludirancang program aplikasi pengenalan pola bahasa isyarat. Perancangan ini menggunakantransformasi Haar Wavelet dan Momentum Backpropagation Neural Network. Haar Waveletdigunakan untuk mendapatkan ciri penting citra dan Momentum Backpropagation NeuralNetwork untuk melakukan proses pembelajaran dan pengenalan. Tujuan perancangan iniadalah untuk mengetahui pola bentuk tangan yang benar dalam mempelajari abjad bahasaisyarat. Perancangan aplikasi ini menggunakan bahasa pemograman Visual Basic dan SQLserver sebagai database. Hasil pengujian menunjukkan bahwa persentase pengenalan dengandata pengujian sebesar 46,36% dan 36.36% dengan data pembelajaran. Gambar pola tanganyang hampir sama menyebabkan ciri yang dominan sulit untuk didapat.
In this global era, when seeking information or communicate with each other, people are using information technology to support those activities. Smartphones for example, is one of affordable technology that can be owned by common people. These days, people are using smartphones anywhere and any time. On every smartphones that being used, it has a operating systems to manages smartphones hardware and provide services that given. Android is the most popular of a mobile operating systems that being used to operate smartphones in the world. Through the popularity of android and growing smartphones users, transmitting information are more effective if the information can be accessed using smartphones. On the first year of research” Mapping Banten Lama’s Cultural Heritage Website “has already developed, and to make accessibility of the information more convenient, on the second year of research has developed application program “Android Based Information System for Mapping Banten Lama’s Cultural Heritage”. This application program is accessible through any kind of android smartphones. Prototyping method is used to develop this application program. For storing data, this application program use MySql database that operate inside server (hosting) so it can can be accessed through internet. Android studio and Java programming language are used to build this application program. Black Box Testing and User Acceptance Test (UAT) are used to test this application program. The result of this research is develop a product of software that can be accessed through android smartphones. Pada era global saat ini masyarakat menggunakan teknologi informasi dalam mencari informasi maupun untuk berkomunikasi. Salah satu teknologi yang terjangkau dan dapat dimiliki oleh masyarakat umum adalah handphone. Mereka membawa dan menggunakan handphone setiap saat dimanapun mereka berada. Perusahaan gadget mulai mengembangkan perangkatnya menggunakan sistem operasi Android yang akhir-akhir ini sangat popular dan menjadi perhatian masyarakat Indonesia maupun dunia. Dengan memanfaatkan perkembangan teknologi tersebut, dalam mengenalkan berbagai informasi kepada masyarakat umum akan lebih efektif bila informasi dapat diakses melalui handphone. Website Warisan Budaya Kawasan Banten Lama sudah dibuat pada penelitian tahun pertama dan agar lebih memudahkan masyarakat luas untuk akses , pada penelitian tahun kedua dikembangkan suatu program aplikasi “Sistem Informasi Pemetaan Warisan Budaya Kawasan Banten Lama Berbasis Android”. Program aplikasi yang dikembangkan ini dapat diakses diberbagai tipe handphone yang berbasis Android. Metodologi pengembangan sistem menggunankan metode Prototyping. Basisdata menggunakan My Sql, yang disimpan dalam server (hosting) agar dapat diakses melalui internet. Selain itu program aplikasi menggunakan Android Studio dengan bahasa pemrograman Java. Metode pengujian menggunakan User Acceptance Test (UAT). Penelitian ini menghasilkan produk software yang dapat diakses melalui handphone berbasis android.
Fish Image Classification Using Convolutional Neural Network is an application that helping classification process. The application is used by user for knowing an information about what is the name of fish species from image that visitor captured. The application is designed by Python programming language. Method of designing the application using System Development Life Cycle. The method used in training model is Convolutional Neural Network, Data used in training process are the species data set is more than 10 species and each species is more than 1000 images, The data collected has been divided into training data and test data. In training process, Fish Image Classifiation Program produce a train loss value of 0.189203, validation loss value of 0.033459 and accuracy value of 0.991029. The evaluation process is carried out using a Confusion Matrix where the diagonal data is the correct prediction data, while the other data is the wrong prediction. By evaluating the Confusion Matrix, predicted accuracy reaches 99.1%precision and recall is 0.98. The resulting accuracy is very good accuracy so that it can predict the image inputted by the user accurately.
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