Real Work Lectures (KKN) is a form of community service carried out by students as an implementation of the Tri Dharma of Higher Education. During the implementation of KKN in Keban Agung Dusun Later Agung, students synergized with local mosque congregations to carry out integrated service programs. The aim of this program is to comprehensively increase community capacity and welfare through collaboration between students and mosque congregations. The approach used is a participatory approach, by actively involving the community in every stage of program implementation. The results achieved from this program include increasing the capacity of mosque youth in the religious, economic and social fields; development of productive businesses for groups of mothers; as well as increasing public awareness of environmental cleanliness and health. Apart from that, this program has also succeeded in building trust and close collaboration between KKN students and mosque congregations, so that it can become a model for community service programs based on mosques in the future.

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.

Purpose: This work aims to apply Quantum Hilbert Scrambling to enhance the security and integrity of image watermarking without affecting visual quality degradation. Further conception of the surveyed methods could result in a very good solution to conventional methods of watermarking in solving some problems of digital image security and integrity with new concepts of quantum computing.
Methods: The paper reviews Quantum Hilbert Scrambling, whose computational complexity is . The process involves encoding the image into a quantum state, permuting qubits by the Hilbert curve, and embedding a watermark using quantum gates.
Result: The quantitative performance evaluation metrics, like Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM), have shown high Peak Signal to Noise Ratio (PSNR) values from 56.13 dB to 57.87 dB and Structural Similarity Index (SSIM) from 0.9985 to 0.9990, correspondingly. This justifies the fact that the quality degradation is very slight and the fine details of the structure are well maintained.
Novelty: The proposed method uniquely integrates quantum computing with traditional watermarking steps for a secure and effective approach in digital watermarking. Further development should focus on improving the quantum circuit regarding computation efficiency, extending the applicability of the method to a wide range of images, and various situations in watermarking, and finding hybrid approaches by combining quantum and classical approaches towards better performance and scalability.

Ecoprint is a textile printing technique that uses natural materials, such as leaves, flowers, and plant stems, to create patterns on fabric. This method is environmentally friendly, reduces the use of hazardous chemicals, and produces unique motifs that cannot be repeated. The process involves direct contact between organic materials and fabric, similar to batik techniques but without special tools. Ecoprint is increasingly popular in the world of art and fashion as a sustainable alternative. The target of this PKM is children in Marga Bhakti Village, so that they can learn about the dangers of synthetic dyes and natural dyes and foster environmental literacy. The method applied in this PKM activity is to directly train children in Marga Bhakti Village to carry out eco-print activities directly. This activity can improve the knowledge and skills of children in Marga Bhakti Village. By introducing and also practicing eco-printing through eco-print making training attended by children, it will be able to improve creativity, skills and foster environmental literacy.

Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.

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.

Di tahun ini sudah terjadi perubahan atau revolusi dalam sistem industri yaitu revolusi industri 4.0, di mana industri sudah mengimplementasikan sebuah mesin-mesin yang serba cepat dan praktis untuk membantu dalam hal produktivitas. Dengan semakin pesatnya teknologi, banyak alat-alat yang di ciptakan untuk perkembangan teknologi di dunia. Pemanfaatan teknologi di gunakan untuk sebagai penggerak di pesatnya sistem berbasis AI ini dapat di manfaatkan untuk di berbagai bidang, salah satunya adalah penerapan sistem deep learning dan salah satu teknik yang terkenal dan sudah banyak digunakan untuk klasifikasi citra adalah menggunakan metode Jaringan Syaraf Tiruan (JST) yang mampu mengenali wajah manusia dan suatu gambar citra lalu mengklasifikasinya atau bisa di sebut image classification dan image recognition. Untuk perancangan sistem deteksi helm pada penelitian ini akan menggunakan algoritma CNN(Convolutional Neural Network). Convolutional Neural Network dan untuk model arsitektur yang di gunakan adalah MobileNetV2 dengan akurasi 80%, yang dapat di kategorikan baik dalam sebuah akurasi.

Salah satu peninggalan budaya Indonesia dari tanah jawa yaitu Aksara Jawa. Aksara Jawa telah digunakan oleh masyarakat sejak jaman dulu untuk menulis sastra dan menulis sehari-hari. Karena memiliki bentuk yang rumit aksara jawa menjadi jarang dikenali. Oleh karena itu pada penelitian ini akan dijadikan salah satu upaya untuk belajar sekaligus melestarikan budaya khususnya Aksara Jawa yakni melakukan suatu transliterasi Aksara Jawa. Metode yang digunakan pada penelitian ini adalah dengan menggunakan Optical Character Recognition (OCR) berbasis Template matching dan ekstraksi fitur Linear Binary Pattern (LBP). OCR akan dapat membantu dalam proses pengkonversian gambar yang berisi tulisan Aksara Jawa yang nantinya akan dilakukan proses transliterasi. Cara kerja Template Matching adalah dengan mencocokkan tiap bagian pada citra dengan citra template yang telah ditentukan. LBP akan membantu dalam pengenalan huruf aksara yang memiliki objek terpisah. Berdasarkan dari pengujian diperoleh rata-rata akurasi sebesar 89,4%. Nilai akurasi tertinggi sebesar 100% dan nilai akurasi terendah sebesar 50%. Tingkat keberhasilan dalam proses transliterasi bergantung pada kejelasan objek huruf Aksara Jawa pada citra uji.