One of the poor indicators of the development and health status of a country is high maternal mortality. The world has seen that one pregnant woman in every two minutes dies because of complication with serious or long-lasting consequences. During pregnancy, pregnant women tend to reduce physical activity due to increased sensitivity factors. It will lead to decrease the elasticity of muscles and joints. The way to improve the elasticity is doing exercise during pregnancy. But, there are still many pregnant women who are less interested in doing exercise during pregnancy, due to high loaded in working day and dense schedule of daily activities in her career or as a housewife. Some women assume that by attending pregnancy exercise course in hospitals or health care centers is time-consuming and too formal because they have to follow the prenatal personal trainer schedule. The technology that allows helping pregnant women in exercise during pregnancy is virtual reality. In this study, the development of virtual reality application for exercise during pregnancy adapted from the methodology to determine when to use virtual reality in education and training combined with the Immersive Virtual Environment (IVE) questionnaire. The average results of overall components in the IVE questionnaire is 4.26 of 5-point scales that indicates the virtual reality application for exercise during pregnancy is feasible to use by pregnant woman.
Leap Motion controller is an input device that can track hands and fingers position quickly and precisely. In some gaming environment, a need may arise to capture letters written in the air by Leap Motion, which cannot be directly done right now. In this paper, we propose an approach to capture and recognize which letter has been drawn by the user with Leap Motion. This approach is based on Deep Belief Networks (DBN) with Resilient Backpropagation (Rprop) fine-tuning. To assess the performance of our proposed approach, we conduct experiments involving 30,000 samples of handwritten capital letters, 8,000 of which are to be recognized. Our experiments indicate that DBN with Rprop achieves an accuracy of 99.71%, which is better than DBN with Backpropagation or Multi-Layer Perceptron (MLP), either with Backpropagation or with Rprop. Our experiments also show that Rprop makes the process of fine-tuning significantly faster and results in a much more accurate recognition compared to ordinary Backpropagation. The time needed to recognize a letter is in the order of 5,000 microseconds, which is excellent even for online gaming experience.
-This research aimed to preserve Benthik traditional game using Benthix VR. Benthix VR used the Virtual Reality Interface Design (VRID) development model. The development phase of the VRID model started from High Level to Low-Level phase. The High-Level Design (HLD) phase consisted of identifying data elements and multiple objects, and modeling component objects. The output from the HLD phase would be input to the LowLevel Design (LLD) phase. The LLD phase was a phase of repetition and fine-tunes from the modeling of several component objects thoroughly. Testing of Benthix VR was conducted on 34 respondents with five assessment aspects. Those were enjoyment, realism, interactivity, usability, and impact. The average result of the questionnaire assessment of all aspects is 3,18824. These results indicate that users feel Benthix VR is comfortable, realistic, interactive, and fascinating. Moreover, they are also interested in playing Benthik in the real world after using the application.
Not all mushrooms are edible because some are poisonous. The edible or poisonous mushrooms can be identified by paying attention to the morphological characteristics of mushrooms, such as shape, color, and texture. There is an issue: some poisonous mushrooms have morphological features that are very similar to edible mushrooms. It can lead to the misidentification of mushrooms. This work aims to recognize edible or poisonous mushrooms using a Deep Learning approach, typically Convolutional Neural Networks. Because the training process will take a long time, Transfer Learning was applied to accelerate the learning process. Transfer learning uses an existing model as a base model in our neural network by transferring information from the related domain. There are Four base models are used, namely MobileNets, MobileNetV2, ResNet50, and VGG19. Each base model will be subjected to several experimental scenarios, such as setting the different learning rate values for pre-training and fine-tuning. The results show that the Convolutional Neural Network with transfer learning method can recognize edible or poisonous mushrooms with more than 86% accuracy. Moreover, the best accuracy result is 92.19% obtained from the base model of MobileNetsV2 with a learning rate of 0,00001 at the pre-training stage and 0,0001 at the fine-tuning stage.
STEM adalah inovasi pembelajaran yang dirancang untuk memberikan siswa pengalaman belajar yang bermakna. STEM merupakan singkatan dari science, technology, engineering dan Mathematic. Istilah tersebut mengacu pada pendekatan pembelajaran yang mengintegrasikan empat disiplin ilmu ke dalam satu proses pembelajaran. Hal ini dimaksudkan untuk mendorong siswa untuk berpikir kritis, menyeluruh dan inovatif ketika menemukan solusi untuk masalah mereka. Robot Edukasi adalah alat pembelajaran yang efektif untuk pembelajaran berbasis proyek yang mengintegrasikan STEM, pengkodean, pemikiran komputasi, dan keterampilan teknik ke dalam satu proyek. Robotika memberi siswa kesempatan untuk mengeksplorasi bagaimana teknologi bekerja dalam kehidupan nyata. Jenis robot yang digunakan adalah robot dengan panel surya dengan enam variasi bentuk. Peserta pada pengabdian ini adalah guru-guru kelas di SD Negeri 1 Karimunjawa. Peserta sangat antusias mengikuti rangkaian kegiatan ini. Peserta dapat merakit robot sesuai dengan bentuk yang diingatkan dalam kelompoknya. Salah satu yang terpenting pada pengabdian ini, dengan menggunakan robot dapat membuat lingkungan belajar yang menyenangkan dan mengasyikkan melalui integrasi sifat praktis dan teknologinya. Lingkungan belajar yang menarik memungkinkan peserta didik untuk mempelajari semua keterampilan dan pengetahuan yang dibutuhkan untuk mencapai tujuan mereka guna menyelesaikan proyek yang diminati. Para guru sangat menyambut positif dan antusias sekali dalam melakukan kegiatan ini. Kata kunci: Pembelajaran STEM, robot edukasi, guru
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