Pandawa is one of the stories in wayang show consisting of five figures: Yudhistira, Bima, Arjuna, Nakula and Sadewa. This research is using Convolutional Neural Network (CNN) and apply it on the Raspberry Pi 4 to classify the puppet figures. CNN is one of the methods that can be used for classification of image data that has more than two classes. The network architecture used can classify Pandawa figures, using 1000 dataset with a size of 100 × 100 consisting of 80% training data and 20% test data. The network architecture has 3 convolution layers and 3 hidden layers, as well as 1 layer output. The classification results on the training data have an accuracy rate of 97.88% while test data has an accuracy rate of 96.5%.

Ilmu kimia dipandang sebagai salah satu ilmu yang cukup sulit dan kurang menarik untuk dipelajari oleh kebanyakan siswa. Diantara penyebab siswa mengalami kesulitan dalam belajar kimia adalah kurangnya minat perhatian siswa pada proses pembelajaran dalam kimia. Penggunaan teknologi informasi dan komunikasi merupakan cara yang efektif dan efisien untuk meningkatkan minat serta kualitas pembelajaran dan hasil belajar di sekolah. Selanjutnya, untuk mempermudah siswa agar memahami materi pelajaran terutama kimia tersebut dibuatlah media pembelajaran dimana modul dengan model magazine (majalah) dikombinasikan dengan aplikasi menggunakan teknologi Augmented Reality (AR). Aplikasi ini mengimplementasikan metode Marker Based Tracking dan Markerless Augmented Reality. Beberapa tahap perancangan aplikasi adalah seperti mengumpulkan data tentang modul, desain antarmuka, database, informasi yang ditampilkan dan build aplikasi kedalam bentuk Android package (Apk). Selanjutnya untuk membangun aplikasi ini digunakan tools seperti Unity 3D, Vuforia Object Scanner, Vuforia SDK, Android SDK, Java Development Kit, Adobe Photoshop, dan Adobe Premiere Pro. Penelitian ini menghasilkan aplikasi Augmented Reality berbasis Android yang dapat secara sukses menampilkan isi materi lebih lanjut pada modul yang telah tersedia berupa video penjelasan materi tentang termokimia.

Hyperspectral imaging technology is a powerful tool for non-destructive quality assessment of fruits. The objective of this research was to develop novel calibration model based on hyperspectral imaging to estimate soluble solid content (SSC) of starfruits. A hyperspectral imaging system, which consists of a near infrared  camera, a spectrograph V10, a halogen lighting and a conveyor belt system, was used in this study to acquire hyperspectral  images of the samples in visible and near infrared (500-1000 nm) regions. Partial least square (PLS) was used to build the model and to find the optimal wavelength. Two different masks were applied for obtaining the spectral data. The optimal wavelengths were evaluated using multi linear regression (MLR). The coefficient of determination (R2) for validation using the model with first mask (M1) and second mask (M2) were 0.82 and 0.80, respectively.

The 5G system requires more significant system capacity, more full bandwidth, and higher frequency. One type of antenna that can be used to increase the channel capacity is microstrip MIMO antenna. The Federal Communications Commission of the U.S. has recently designated the frequency band from 27.5 to 28.35 GHz for 5G applications. In this paper, the design of 28 GHz microstrip MIMO antenna for future 5G applications was proposed. The antenna was designed by using RT Duroid 5880 substrate with a dielectric constant of 2.2 and the loss tangent of 0.0009. The antenna operated from 27.10 GHz to 28.88 GHz with 1.78 GHz (6.35%) of bandwidth. The antenna consisted of four elements feeding by a microstrip line. Based on the simulated results, the high gain of 14.8 dBi is obtained with a linear directional pattern. Comparison performance regarding gain, return loss, VSWR and bandwidth are also presented for single, two and four elements.  It is shown that the increasing number of elements of antenna increased the gain and the return loss. The antenna meets the 5G requirements.

Health is an essential thing in our lives. One of the characteristics of a healthy body is having an ideal body. To get an ideal body, we should regulate our diet or eating pattern to our daily activity. We can find out that our body is ideal or not by using BMI formula. Moreover, we can total our daily calories need by using BMR formula. Both of the formulas actually can be calculated manually, but the methods are not efficient. Also, in those formulas, the calculations are still standard and inflexible. Therefore, this study uses fuzzy logic with Mamdani methods to calculate the body mass index and calorie requirement figures. Using the fuzzy logic can give tolerances to the result so that there are no significant results. This application uses Java language based on Android so that it can be used practically on smartphones. The result shows that BMI and BMR obtained by this application are the accuracy of 89.62% and the error value of 10.38%.