Vehicle detection is the artificial intelligence that can help us in transportation highway systems like counter vehicles passing through the road on Eid Mubarak day etc. The object in this case is divided into six classifications there are car, motorbike, van, truck, and three-wheel. On the dataset vehicle is mostly an image of a car that we get from Kaggle. To solve vehicle detection problems such as poor vehicle detection and reduced detection accuracy, we provide a new vehicle detection with a dataset at kaggle. The clustering process consists of steps in which input images are transformed into morphometrics. The next step is to classify the image data using the K-Means algorithm. The images grouped by this detection are vehicles. The first step is to determine the randomly drawn mean or center point of two image data values ??in the database. If there is no data transfer, the group is considered stable and group creation is completed. Seven vehicle image data are used to test this application. And the result of our experiment on vehicle detection is about 85.7 % accurate

Sudoku is a digital game that is included in the type of logic-based puzzle game where the goal is to fill in the puzzle with random numbers. Therefore, in this research it is proposed to use Artificial Intelligence which contains the Depth First Search Algorithm to track the number of possible solutions that lead to only one so that it becomes efficient. This game has different levels of difficulty such as easy, medium and difficult. The time and complexity of execution will vary depending on the difficulty so it is proposed to use Android Studio software. The experimental results prove that there is an increase in playing the Sudoku game quickly and accurately by applying the Depth First Search Algorithm method. This is proven by the ability to complete this game using the Depth First Search Algorithm using the Android Studio programming language. The average time at the easy level is 11:04 minutes, at the normal level is 10:52 minutes, at the hard level is 25:46 minutes, and at the extreme level is 38 minutes.

Pelatihan penggunaan Mendeley dan MS. Word untuk otomatisasi penulisan karya ilmiah diadakan untuk meningkatkan efisiensi dan akurasi dalam penulisan referensi dan format dokumen ilmiah. Pelatihan ini melibatkan 137 peserta menggunakan media zoom yang terdiri dari guru dan dosen dari Perkumpulan Profesi Multimedia dan Teknologi Informasi. Mendeley merupakan alat manajemen referensi yang membantu dalam pengorganisasian dan penyusunan kutipan secara otomatis, sementara MS. Word menyediakan fitur otomatisasi format yang mempermudah penulisan karya ilmiah sesuai dengan standar akademik. Studi yang relevan menunjukkan bahwa pelatihan semacam ini dapat meningkatkan keterampilan teknis dan produktivitas peserta dalam penulisan ilmiah. Hasil kegiatan pelatihan para peserta guru dan dosen mampu menghasilkan karya ilmiah yang lebih terstruktur dan berkualitas tinggi dalam waktu yang lebih singkat. Dengan demikian, pelatihan ini menjadi langkah penting dalam mendukung profesionalisme dan kualitas publikasi ilmiah di kalangan guru dan dosen.

Rivest Cipher 4 (RC4) has the main factors that make this algorithm widely used, namely its speed and simplicity, so it is known to be easy for efficient implementation. The nature of the key in the RC4 algorithm is symmetrical and performs a plain per digit or byte per byte encryption process with binary operations (usually XOR) with a semirandom number. To improve the visual image after the encryption process, in this article we use the Least Significant Bit (LSB). In this study, the quality of the stego image and the original image has been calculated using MSE, PSNR and Entropy. Experiments were carried out by images with a size of 128x128 pixels to 2048x2048 pixels. Experiments using imperceptibility prove that the stego image quality is very good. This is evidenced by the image quality which has an average PSNR value above 53 dB, while the lowest PSNR value is 48 dB with a minimum dimension of 128x128 pixels.