Linguistics plays a crucial role in studying and understanding the structure of language, including in the context of Arabic. The Arabic language, with its complex system of morphology, syntax, and phonology, requires an in-depth linguistic approach to analyze its grammatical structure. This study aims to explore how linguistic theories, such as phonology, morphology, syntax, and semantics, contribute to understanding the basic structure of Arabic. Through linguistic approaches, a clearer and more structured understanding of sentence structure, word patterns, and the relationships between language elements can be achieved. This paper also discusses the challenges faced in applying linguistic theories to Arabic, as well as the importance of a linguistic approach in teaching Arabic to non-native speakers. The findings of this study are expected to provide broader insights into the role of linguistics in learning and teaching Arabic more effectively and systematically. 

Pakar psikologi mengingatkan pentingnya untuk tidak meremehkan kesehatan mental atau depresi sejak dini karena dapat menyerang berbagai usia dan mampu memengaruhi kondisi psikis penyintasnya. Mulai dari menyalahkan orang lain, menyakiti diri sendiri, hingga menyebabkan keinginan bunuh diri, merupakan bahaya yang mengancam saat seseorang depresi. Oleh karena itu penelitian ini bertujuan untuk membuat perhitungan dengan metode fuzzy sugeno untuk mengukur tingkat depresi pada mahasiswa dan membuat sistem yang mempermudah pengguna untuk mengetahui tingkatan depresi yang dialaminya. Sampel dalam penelitian ini adalah mahasiswa Ilmu Komputer Universitas Negeri Medan. Pengambilan sampel dilakukan dengan teknik Stratified Random Sampling. 60 responden yang terukur, terdapat 40% yang terindikasi tidak ada depresi/gangguan mood, 45% terindikasi depresi sedang, dan 15% terindikasi depresi berat. Hasil akurasi metode fuzzy sugeno yang dihitung menggunakan Mean Absolute Error (MAE) dengan memperoleh 5,36 yang artinya rata-rata kesalahan mendekati nilai sebenarnya.

Purpose: This research aims to improve the accuracy of sentiment analysis on PLN Mobile app reviews by overcoming the challenge of data imbalance. This goal is important to provide a better understanding of user opinions and support PT PLN (Persero) in improving mobile application services.
Methods: This research uses the Random Forest algorithm combined with Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced data. Data is collected through web scraping reviews from the Google Play Store, followed by preprocessing processes such as data cleaning, stopword removal, tokenization, and stemming. Feature extraction is performed using the Bag of Words (BoW) method, and the data is tested with four sharing schemes.
Result: The results showed that the 90%-10% sharing scheme gave the best performance with an accuracy of 81% and an average precision and recall of 0.79. This finding confirms that the larger the proportion of training data, the better the model performs sentiment classification.
Novelty: This research's novelty lies in combining SMOTE with BoW and Random Forest to overcome data imbalance. This approach is a significant reference for future sentiment analysis research. It provides practical insights that PT PLN (Persero) can use to improve the quality of its application services.

Milk consumption and the need for beef cattle for food purposes or sacrificial purposes continue to increase from year to year. This increase is in line with the increasing level of the economy and awareness of the need for nutritious food. However, the increase in demand still faces many production constraints due to the difficulties faced by farmers, the decline in cattle production from year to year in Indonesia. This causes Indonesia to have to import beef for 35.95% of the total national beef consumption. With many factors that affect the decline in the production rate of dairy cows or beef cattle every year, one of them is air pollution and poor air quality in cages. Along with the development of technology, a monitoring and detection system for air cleanliness in cattle pens was created, using the MQ-7 sensor as the concentration of carbon monoxide, the MQ-135 sensor as the concentration of ammonia gas, the MQ-2 sensor as the smoke detection, and When the air pollution value of each sensor input is according to the air pollution standard index (ISPU), the outputs namely indicator lights, buzzers and blowers will be on, then there will be temporary air neutralization in the cage. This tool is made easier by internet of things technology, so that it can send notifications via telegram and can see the level of air pollution levels through the web.

The utilization of electromagnetic fields (EMF) in modern medical technology has become a significant focus of research. This study aims to explore the therapeutic effects of EMF, especially at low frequencies (ELF) and Pulsed Electromagnetic Fields (PEMF), in enhancing the healing of various medical conditions. The method used is a literature study by analyzing various sources from Google Scholar, Science Direct, and PubMed. The results showed that EMF therapy is effective in relieving pain, accelerating tissue healing, and improving the quality of life of patients with musculoskeletal disorders and fractures. Despite the many benefits, it is important to consider the potential health risks of long-term exposure to EMF. This study recommends the development of strict regulations and training for medical personnel to ensure the safe and effective use of EMF.

Science and technology will facilitate human work. However, on the other hand it will increase competition. In facing intense competition, it is necessary to have competent human resources. Students are expected to be academically prepared, in the form of knowledge and skill readiness to face increasingly fierce competition. One way to see student competence is to look at learning outcomes that can be represented by the exam scores taken. The midterm exam (UTS) is a form of exam which is an assessment component. By knowing the UTS scores, the lecturer knows the distribution of students in terms of academic competence. For this reason, it is necessary to group (clustering) using the k-means algorithm as a consideration for lecturers in forming student study groups based on UTS value clusters.