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The rapid advancement of digital technology, particularly Artificial Intelligence (AI), has reshaped various sectors, including the field of law. This study aims to examine the integration of AI in law firms’ operations, focusing on its potential benefits, legal challenges, and ethical implications in the Indonesian legal context. This research employs a qualitative approach through a normative juridical method, supported by literature review and case analysis related to the use of AI in legal practice. Relevant legislation, including Law No. 11 of 2008 on Electronic Information and Transactions, is analyzed to assess the existing regulatory framework. The study reveals that while AI enhances efficiency in tasks such as document analysis, case prediction, and legal drafting, it also raises concerns about algorithm reliability, data bias, and the absence of specific AI-related legal regulations in Indonesia. Law firms must ensure transparency, accountability, and ethical responsibility when adopting AI to align with the principles of justice. Human interaction remains crucial to maintain trust and professional integrity in client services. The research contributes to the ongoing discourse on developing legal and ethical frameworks for AI implementation in the legal sector. It suggests the need for comprehensive regulation and professional guidelines to optimize AI utilization while safeguarding justice and ethical standards. The study is intended for publication in a national academic journal.

Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

The development of Artificial Intelligence (AI) has brought significant changes to the field of translation, offering ease and efficiency in the translation process. However, its use raises various ethical issues that need to be regulated through a clear code of ethics. This study discusses the Code of Ethics for the Use of AI in Translation, encompassing key principles such as accountability, transparency, privacy, fairness, and cultural respect.The code of ethics aims to optimize AI's potential while minimizing  negative impacts, such as algorithmic bias, loss of cultural meaning in translations, and the declining role of human translators. Through literature reviews and case studies, this research recommends steps such as regular AI system audits, human-AI collaboration, and compliance with data privacy laws. By implementing this code of ethics, the use of AI in translation is expected to operate responsibly, support linguistic diversity, and uphold professional standards in the field of translation.

This article explores the application of Islamic ethics, particularly the concept of tawhid, in the development of digital technologies and artificial intelligence (AI). The study employs a qualitative descriptive method based on a literature review to analyze Islamic principles, such as justice (al-'adl), trust (amanah), and public benefit (al-maslahah), in addressing ethical challenges posed by AI. The findings indicate that Islamic values can provide a holistic framework for tackling algorithmic bias, privacy concerns, and social inequalities associated with AI. However, the study identifies a gap in academic research regarding the integration of tawhid principles with AI development. A limitation of this research is the lack of empirical case studies to test the effectiveness of implementing these principles in technology development.

This study explores the application of machine learning to predict students' GPA based on behavioral and time-related factors, including study hours, extracurricular activities, sleep, social interactions, and physical activity. Seven regression algorithms were employed to evaluate predictive accuracy using metrics such as MAE, RMSE, and R2 Among these, Regularized Linear Regression demonstrated the highest accuracy and interpretability, highlighting its suitability for this dataset. The findings emphasize the potential of machine learning in identifying key predictors of academic performance and offer practical applications for personalized academic advising and time management. This research provides a data-driven framework to support students and educators in optimizing learning outcomes.

The integration of Artificial Intelligence (AI) in personalized and adaptive learning environments has revolutionized the education sector by offering customized learning experiences tailored to individual student needs. This study explores the role of AI in enhancing adaptive learning through data-driven insights, intelligent tutoring systems, and real-time feedback mechanisms. By employing machine learning algorithms and natural language processing, AI-driven platforms can analyze student performance, predict learning patterns, and deliver personalized content. The study highlights the effectiveness of AI in addressing diverse learning styles, improving engagement, and optimizing educational outcomes. Furthermore, it discusses the implications of AI in fostering inclusive education and lifelong learning. The findings suggest that AI-powered learning environments significantly enhance student-centered education, promoting efficiency and accessibility.

This research explores the role of digital technology and artificial intelligence (AI) in transforming Islamic family law practices, with a focus on efficiency, accessibility, and justice. This study aims to analyze how technology supports administrative processes, legal decision-making, and public literacy toward Islamic law. The method used is a multidisciplinary approach that integrates the perspectives of sharia law, technology, and ethics. The results show that digital technology improves efficiency through document digitization, online registration, and virtual hearings, while AI makes a significant contribution through big data analysis to understand family dispute patterns and offer algorithm-based recommendations. However, there are challenges related to algorithm bias, data security, and compliance with sharia values. The study emphasizes the importance of collaboration between scholars, technology experts, and legal practitioners to ensure the adoption of technology that is aligned with maqashid al-shariah, in order to create an adaptive and inclusive Islamic family legal system in the digital era.

This research examines the consumptive style of Gen Z through TikTok Shop in purchasing fashion products, especially clothes. The background of the research focuses on the dominance of social media that influences the consumption patterns of the younger generation. The research uses a phenomenological approach to understand students' experiences in shopping at TikTok Shop. Data collection techniques include observation and in-depth interviews. The results show that TikTok Shop utilizes personalization algorithms, creative video content, and massive promotions to attract the attention of Gen Z. Purchasing decisions are often influenced by trends, influencer recommendations, and discounts that create a FOMO (Fear of Missing Out) effect. While offering convenience and an interactive shopping experience, this impulsive consumption pattern poses the risk of product quality disappointment and unplanned spending. The implications of this research highlight the need for consumer education to raise awareness about wise shopping. In addition, industry players are expected to design ethical and sustainable marketing strategies to support more rational consumption behavior. 

In today's world of retail and technology, competition is fiercely competitive. With the development of retail businesses increasing in number and mushrooming in a region, consumer needs are increasing, and retail business players are competing to develop their businesses by utilizing existing technology. Daily sales transaction data continues to increase, causing a lot of storage. Toko Ira has more than 228 sales transaction data records from 2023 to 2024 that have not been used. Data requires a lot of storage space. Additionally, the data has not been used in an effective way. Based on this problem, this research aims to use data mining to classify sales transaction data to determine which items are selling best. This research is a case study with a qualitative approach. This research was conducted with the Naive Bayes method and Rapidminer was used. The results of the sales transaction data classification research are the division of products into best-selling and non-selling categories. The results of this research show that the K-Nearest Neighbors (KNN) algorithm with a 50:50 data division is more effective in predicting and classifying sales of best-selling and non-selling products in IRA stores. The results show that the Naive Bayes algorithm has an accuracy of 89.91%, while the K-Nearest Neighbors (KNN) algorithm has an accuracy of 60.09%.

This research explores the integration of artificial intelligence (AI) and communication strategies in promoting ethical practices within halal supply chain management. As the global halal market expands, the need for efficient, transparent, and ethically sound supply chains becomes critical. This study investigates how AI technologies can enhance operational efficiency while ensuring compliance with halal principles, and examines the role of effective communication in fostering stakeholder engagement and building consumer trust. Utilizing a mixed-methods approach that includes literature review and qualitative case studies, the research identifies key ethical challenges associated with AI, such as algorithmic bias and data privacy, while highlighting best communication practices to ensure transparency regarding halal compliance. The findings indicate that a synergistic approach combining AI-driven efficiencies with robust communication frameworks can significantly enhance the integrity and transparency of halal supply chains, offering actionable recommendations for businesses aiming to navigate the complexities of AI integration while upholding halal values.

This study explores the ethical implications and challenges associated with the implementation of artificial intelligence (AI) in business operations. The research aims to identify key ethical concerns, such as data privacy, algorithmic bias, and workforce displacement, while analyzing their impact on organizational practices and stakeholder trust. Utilizing a library research approach, the study synthesizes insights from academic literature, industry reports, and case studies to examine the intersection of AI ethics and business performance. The findings highlight the need for transparent governance frameworks, inclusive algorithm design, and ethical AI policies to address these challenges effectively. The research underscores the importance of balancing technological innovation with ethical considerations, offering practical implications for businesses seeking to integrate AI responsibly into their operations

With the advancement of technology and computing capabilities of computers, various applications and algorithms that were previously not applicable to portable devices such as laptops and smartphones can now be applied. Artificial intelligence (AI) is one of the increasingly popular techniques that are now a part of everyday life. AI is an umbrella term that refers to the simulation of human intelligence by machines that use big data to perform various tasks. AI has benefited many aspects of human life, including education. However, AI also has significant negative effects. This study aims to study the influence of artificial intelligence on students' critical thinking skills. This study uses qualitative research and literature analysis. However, the study also identified several negative impacts, including reliance on technology, unassured information quality, social isolation, and ethical and privacy concerns. Therefore, a balanced and strategic approach is needed in integrating AI in education to ensure that its benefits can be maximized, while its negative impact is minimized.

PT Astra International BMW Semarang is a company operating in the automotive sector with 3 supporting pillars, namely Sales, Aftersales and Spare Parts for BMW car units. The availability of spare parts is one of the determining factors for consumer satisfaction with the company because if the spare parts stock is empty it will cause consumer disappointment with the company. By using spare parts sales transaction data for the period January 2019 – June 2023, totaling 52,162, it will be utilized using data mining association techniques with the a priori algorithm and the eclat algorithm. The problem in this research is how to find out consumer purchasing patterns so that there is no shortage or empty stock of spare parts in the warehouse. This research aims to determine the association of spare parts purchasing patterns in sales transactions so that partman get recommendations in making decisions about providing priority types of spare parts. This research methodology uses CRISP-DM (Cross-Industry Standard Process for Data Mining) and is implemented with the R programming language with R studio software. In 3 trials using the Apriori algorithm and 3 trials with the Eclat algorithm, The result with the highest confidence appears in a combination of 3 itemsets with minimum support 0.01 and confidence 0.9, namely if a customer buys B11.42.8.593.186 (Set oil-filter Mx) and B83.12.5.A1A.683 (Washer Cleaner) then they will also buy Z99000000333 ( BMW Engine Oil) with confidence 1.00 or 100%. From the results of this association's analysis, it can be used as advice for the management of PT Astra International BMW Semarang in managing spare parts stock.

This research explores the implementation of computer vision technology in AI-based e-commerce platforms to enhance product identification and improve user experience. The study specifically examines the use of deep learning algorithms, particularly Convolutional Neural Networks (CNN), to automate product recognition and classification. The results indicate that AI-driven image search features significantly increase the speed and accuracy of product search, leading to greater customer engagement. However, challenges such as the need for high-quality datasets, varying image quality, and high initial investment costs were identified as barriers to effective implementation. The findings suggest that overcoming these obstacles can lead to improved operational efficiency and customer satisfaction. The success of AI in e-commerce depends on robust infrastructure, data quality, and skilled workforce training.

The purpose of this research is to develop a data search system for thesis and internship reports at the Faculty of Engineering Library of Gajah Putih University Takengon (UGP). This search engine will be created and used to help students and library employees access thesis and internship report information. Analysis of user needs, system design, creation of effective search algorithms, and evaluation of system performance are all topics that will be discussed in this thesis. Interviews with potential users, satisfaction surveys, and historical data collection of library usage are the methods used. It is expected that the results of this research will help library users find and retrieve thesis and internship report data and improve the accessibility and availability of academic information at the UGP Faculty of Engineering. When search engine technology is used, it is expected that the time required for Information will increase productivity, improve efficiency, and support the academic development of students at UGP.

This research investigated the biomechanical variables of movement analysis and its essentialcomponents for handball players at the College of Physical Education and Sports Sciences, Al-MustansiriyaUniversity, during a league match and university qualifiers. The data was analyzed using binary logisticregression, a mathematical model that defines the link between a dependent variable, which takes the valueof one when a goal is scored against the opposing team and zero when no goals are scored, and independentfactors.The bird swarm algorithm will be used in this research. It is one of the artificial intelligencealgorithms that rely on the intelligence of living flocks by monitoring their movements, such as birds, bees,cats, chickens, and many other swarm algorithms.The conclusions we reached from this study are as follows: When using logistic regression, we foundonly four explanatory variables that affect the dependent variable. They are detailed as follows: The twoexplanatory variables (The maximum height of the hip and Flight time until leaving the ball) and thedependent variable (shooting) have an inverse relationship and affect it. The two variables (Knee angle at themoment of thrust and The instantaneous speed of the ball) have a positive relationship with aiming and affectit.When we used the Bird Swarm algorithm, we found that all the explanatory variables included in thestudy had a significant effect on the dependent variable. The variables (Knee angle at the moment of thrust,Rising angle, Flight angle, The instantaneous speed of the ball, and the horizontal distance of theperformance) have a positive relationship, with the dependent variable (shooting). In contrast (The maximumheight of the hip and Flight time until leaving the ball) have an inverse relationship with the dependentvariable.Using the logistic model helps sports coaches and researchers to estimate and predict models,especially when the dependent variable takes values ​​(one or zero). In contrast, we noticed that the resultswere more accurate and objective when using the bird swarm algorithm. It further helps academics, thoseinterested in sports, and coaches benefit from these results.

Computer intelligence, particularly Artificial Intelligence (AI), has become a cornerstone in advancing bioinformatics. This study aims to explore the role of AI in addressing the challenges of analyzing complex biological data, especially in genomics, proteomics, and metabolomics. Using machine learning (ML) and deep learning (DL) algorithms, AI efficiently processes large-scale data, accelerates genomic research, predicts protein structures, and identifies disease biomarkers. However, challenges such as data quality, computational limitations, and privacy issues remain barriers to its implementation. The findings of this study highlight the importance of continuous innovation, multidisciplinary collaboration, and strict regulations in AI applications. In conclusion, AI holds great potential to revolutionize bioinformatics, significantly impacting scientific research and the development of global healthcare systems.

Technological advancements have brought significant transformations across various fields, including the application of machine learning in recommendation and classification systems. Machine learning leverages data processing, utilizes algorithms, and efficiently identifies patterns to produce accurate recommendations and predictions. This study aims to review machine learning-based recommendation system approaches, analyze model performance, and compare the algorithms used. A literature review was conducted by examining journals published in the past five years, focusing on algorithm implementation. The findings indicate that the Naïve Bayes algorithm delivers the best performance, achieving an accuracy of up to 97%. This algorithm is particularly well-suited for processing small to medium-sized datasets with high efficiency. The research provides comprehensive insights into the performance and limitations of various algorithms, serving as a valuable guide for future developments in the field.

Puskesmas Kosambi employs Non-PNS staff whose discipline, particularly regarding attendance and work location, needs to be evaluated. The previous manual attendance system was found to be ineffective in monitoring staff discipline. This study aims to develop a mobile-based attendance system equipped with GPS radius and selfie photo features to improve the accuracy and management of attendance. The GPS radius feature ensures that staff can only clock in within the designated area, such as the Puskesmas area, while the selfie photo feature verifies the identity of the staff member clocking in. This study involved 24 Non-PNS staff members and used the K-Means Clustering algorithm to group staff based on their discipline levels. The results showed that the system was effective in improving staff discipline, with 11 employees categorized as highly disciplined, 10 as moderately disciplined, and 3 as lowly disciplined. The implications of this study suggest that the implementation of a mobile-based attendance system can improve attendance monitoring and enhance work discipline at Puskesmas Kosambi.

The growth of e-commerce in Indonesia has led to the emergence of various online shopping platforms, with Shopee being one of the most popular in Semarang City. User reviews on the Shopee application serve as a valuable data source for analyzing customer satisfaction levels; however, the large volume of data requires a systematic and accurate analytical approach. This study aims to analyze user review sentiments of the Shopee application using three machine learning algorithms: Random Forest, Naïve Bayes, and Support Vector Machine (SVM), as well as comparing the accuracy of these three algorithms. This research utilized 1000 reviews collected through web scraping from the Play Store, which were categorized into three classifications: positive, neutral, and negative sentiments. The analysis process encompassed pre-processing stages, feature extraction using TF-IDF, and classification using Random Forest, Naïve Bayes, and Support Vector Machine algorithms. The results demonstrated that the Random Forest algorithm achieved the highest accuracy at 96.19%, followed by Support Vector Machine with 95.71% accuracy, and Naïve Bayes with 84.76% accuracy. This research highlights the effectiveness of Random Forest and SVM in classifying user review sentiments towards the Shopee application.