This study aims to analyze the level of anxiety of Arabic Language Education students towards the Final Semester Exam of Qiraatul Kutub course. The method used in this study was a questionnaire survey distributed to Arabic Language Education students at one of the universities. The data obtained were analyzed descriptively to identify factors that influence anxiety levels, as well as their impact on students' academic achievement. The results showed that most students experienced moderate levels of anxiety, with the main causative factors being academic pressure, lack of preparation, and environmental expectations. These findings are expected to enable lecturers to design more effective support programs to help students overcome academic anxiety, so that they can improve their academic achievement during exams in the Qiraatul Kutub course. The method used in this study was a quantitative survey by distributing questionnaires to 10 Arabic Language Education students at UIN SMH Banten. The questionnaire included 10 items that measured anxiety levels based on a Likert scale of 1 to 4. The results of the analysis showed that 65% of respondents felt high levels of anxiety, 25% had moderate levels of anxiety, and 10% had low levels of anxiety. Factors contributing to anxiety include lack of preparation (70%), pressure from expectations from parents and teachers (60%), and lack of social support (55%). In addition, 80% of students with high levels of anxiety admitted to having difficulty managing their study time, and 75% of them felt doubtful about their own abilities. academic.

Online gambling has become a rapidly growing social issue supported by advancements in digital technology, particularly in Indonesia, with its large population of internet users. Despite being legally prohibited, its popularity continues to rise, creating challenges for law enforcement and significant socio-economic impacts. This study aims to analyze the social and economic dynamics of online gambling actors using a descriptive qualitative method. Data were collected through in-depth interviews and document analysis, then analyzed using thematic coding techniques. The findings reveal that actors, such as Koh Jimmy, learn deviant behaviors through exclusive online communities that provide operational techniques and moral justification. Economic motivation, social support, and gambling systems designed to induce addiction are key drivers of actor involvement. Although aware of legal risks, actors remain engaged due to community influence and substantial financial gains. This study underscores the relevance of differential association theory in explaining actor involvement in online gambling and provides recommendations for preventive policies through technological oversight, social education, and the strengthening of social norms. The results are expected to serve as a foundation for formulating more effective preventive and intervention measures.
 

This research is aimed to identify, describe, and develop strategies to enhance group members' commitment in the development of a health honey business using the Business Model Canvas (BMC). The study was conducted at KTH Bina Lestari in Ciamis Regency, post the Covid-19 pandemic. Data collection was carried out through interviews, observations, and documentation. Data analysis was used to assess internal and external factors to determine the highest priority for development and improvement. The research findings indicate that the selected priority strategies for honey business development in Kampung Madu are: 1) training and human resource development, 2) product quality improvement, and 3) product marketing optimization.
 

Komunikasi merupakan kegiatan kemasyarakatan yang dimana dilakukan dua atau lebih orang untuk dapat saling bertukar informasi. Komunikasi dapat dilakukan dimana saja dan kapan saja.  Namun komunikasi secara normal tidak dapat dilakukan oleh kalangan berkebutuhan khusus terutama tuna wicara. Permasalahan tersebut menimbulkan banyak kerugian bagi penyandang tunawicara untuk berkomunikasi dengan kaum dengar. Kondisi tersebut dapat dihindari dengan membuat sistem pendeteksian bahasa isyarat alfabet BISINDO berbasis computer vision. Penelitian ini memanfaatkan kecerdasan buatan dalam mengenali bahasa isyarat, serta penggunaan konsep Deep Learning dan pemanfaatan arsitektur YOLOv4.  Kecepatan dan keringanan komputasi juga menjadi pertimbangan penting dalam penelitian ini untuk mendapat hasil yang cukup baik dengan waktu komputasi secepat mungkin. Sistem dibangun menggunakan total 494 dataset citra yang sekaligus digunakan untuk pelatihan serta pengujian sistem. Untuk dapat mengukur besarnya performa dari model digunakanlah Confusion Matrix. Hasil dari pengujian menunjukkan bahwa sistem memiliki tingkat accuracy sebesar 99.7%, tingkat precision sebesar 96%, dan tingkat recall sebesar 96%. Penelitian ini membuktikan bahwa metode ini dapat digunakan untuk mendeteksi bahasa isyarat alfabet BISINDO dengan benar dan akurat berdasarkan masukan gambar.Komunikasi merupakan kegiatan kemasyarakatan yang dimana dilakukan dua atau lebih orang untuk dapat saling bertukar informasi. Komunikasi dapat dilakukan dimana saja dan kapan saja.  Namun komunikasi secara normal tidak dapat dilakukan oleh kalangan berkebutuhan khusus terutama tuna wicara. Permasalahan tersebut menimbulkan banyak kerugian bagi penyandang tunawicara untuk berkomunikasi dengan kaum dengar. Kondisi tersebut dapat dihindari dengan membuat sistem pendeteksian bahasa isyarat alfabet BISINDO berbasis computer vision. Penelitian ini memanfaatkan kecerdasan buatan dalam mengenali bahasa isyarat, serta penggunaan konsep Deep Learning dan pemanfaatan arsitektur YOLOv4.  Kecepatan dan keringanan komputasi juga menjadi pertimbangan penting dalam penelitian ini untuk mendapat hasil yang cukup baik dengan waktu komputasi secepat mungkin. Sistem dibangun menggunakan total 494 dataset citra yang sekaligus digunakan untuk pelatihan serta pengujian sistem. Untuk dapat mengukur besarnya performa dari model digunakanlah Confusion Matrix. Hasil dari pengujian menunjukkan bahwa sistem memiliki tingkat accuracy sebesar 99.7%, tingkat precision sebesar 96%, dan tingkat recall sebesar 96%. Penelitian ini membuktikan bahwa metode ini dapat digunakan untuk mendeteksi bahasa isyarat alfabet BISINDO dengan benar dan akurat berdasarkan masukan gambar.