Wireless Fidelity (WiFi) devices are often used to access the internet network, both for working and in information searching. Accessing the internet can be administered anywhere provided that the area is within the WiFi devices range. A WiFi device uses 2.4 GHz and 5 GHz operating frequencies. There were several methods employed in the previous studies so that an antenna design could work in two different frequencies, i.e., winding bowtie method, Sierpinski method, and double-circular method. This paper employed a simple method, the slit method. The objective of this paper is to discover a simple antenna model that works on 2.4 GHz and 5 GHz frequencies. This paper employed a square patch microstrip antenna with a slit method. The dimensions of the designed square patch microstrip antenna were 42.03 mm × 27.13 mm × 0.035 mm. The antenna worked at 2.4 GHz and 5 GHz frequencies. The obtained simulation results after the optimization showed that the square patch microstrip antenna using the slit method acquired a value of S11 (return loss) of -10.15 dB at a frequency of 2.4 GHz and -37.315 dB at a frequency of 5 GHz.

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The antenna performance is seen from the S-parameter value. The Sparameter graph can be seen as the return loss (S11, S22) and the mutual coupling (S21, S12) value. This research focuses on analyzing mutual coupling on Square and Vivaldi array antennas using the ring metamaterial method. The value of mutual coupling is considered very important to analyze because it affects the performance of the antenna in which is arranged in an array. The simulation results of the mutual coupling value obtained on a square array antenna use a ring metamaterial is -17 dB at a frequency of 2.4 GHz. Meanwhile, the Vivaldi array antenna uses a ring metamaterial that produces a mutual coupling value of -13.840744 dB at a frequency of 3.0162 GHz. The factors that affect the square array antenna so that it becomes the best to suppress the mutual coupling value between antenna elements are a selection of metamaterial shape and proper placement between the antenna array elements is arranged horizontally.

Teknologi 5G memanfaatkan spektrum millimeter wave untuk menyediakan kapasitas, kecepatan data, dan cakupan yang luas untuk koneksi secara menyeluruh.  3GPP release 15 menyatakan teknologi beamforming dapat memenuhi karakteristik jaringan 5G karena dapat meningkatkan 5G broadcast dan traffic beam coverage. Beamforming merupakan proses penggabungan sinyal pada elemen array untuk membentuk sinar radiasi (beam radiation) dan menyelaraskan fasa sinyal untuk membentuk beam pada arah tertentu. Butler matrix adalah salah satu teknik yang digunakan pada beamforming yang bertujuan untuk mendapatkan beda fasa disetiap elemen dari antena yang dirancang, sehingga arah radiasi yang dihasilkan dapat fokus pada arah yang dibutuhkan. Pada penelitian ini dirancang antena mikrostrip rectangular array dengan pemodelan MIMO 4x4 dan metode butler matrix. Perancangan butler matrix 4x4 menggunakan 2 skenario perancangan, kemudian menentukan skenario terbaik untuk digabungkan dengan antena mikrostrip array MIMO 4x4. Hasil simulasi didapatkan return loss pada pada elemen 1, elemen 2, elemen 3 dan elemen 4 sebesar -14,504 dB, -6,71 dB, -6,79 dB dan -15,129 dB. VSWR sebesar 1,46; 2,716; 2,687 dan 1,424. Gain sebesar11,1 dBi; 10,8 dBi; 10,8 dBi dan 11 dBi. Pola radiasi secara unidirectional dengan arah radiasi antena yang berbeda-beda, dilihat dari sudut pandang elevasi arah pancar utama pada sudut 0,0o; 8,0o; 8,0o; dan 22,0o. Hal ini membuktikan bahwa penggunaan butler matrix 4x4 pada perancangan antena mikrostrip array MIMO 4x4 dapat meningkatkan nilai gain dan mendapatkan pola radiasi antena yang terarah dengan arah radiasi yang berbeda-beda.

Currency is an item humans require as a medium of exchange in transactions, including for those with vision impairments. It can be challenging for certain blind people to identify currencies. This research aimed to help blind people identify nominal currency when in the transaction. Deep Learning with the CNN algorithm and preprocessing with a sequential model were the methods used in this research. This algorithm is modeled as neurons in the human brain that communicate and learn patterns. Data collecting, preprocessing, testing, and evaluation are the stages in this research. 681 datasets are used, consisting of IDR 50.000, IDR 75.000, and IDR 100.000. Model testing was carried out with different iterations of 5, 10, 15, and 20 epochs. Different epoch values will affect the time it takes the model to learn, but the longer of learning process will result more accurate models. The highest result obtained from all epoch tests is 100%. The class prediction results for the 69 test data show that they can be predicted based on the actual class, indicating that the model is adequate. The results of this classification might be used to construct a smartphone app that would assist visually challenged people in recognizing the nominals.