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This study discusses the position of women in the creation of humankind based on Amina Wadud's feminist hermeneutics perspective as an effort to critique classical interpretations of the Qur'an that tend to be gender biased. For centuries, the tradition of interpretation dominated by male exegetes has shaped a theological understanding that places women in a subordinate and inferior position, especially in the narrative of human creation. Amina Wadud, as one of the contemporary Muslim feminists, offers a feminist hermeneutics approach that emphasizes the importance of historical context, linguistic analysis, and women's experiences in understanding the Qur'anic text in a more fair and comprehensive manner. This study specifically examines Wadud's interpretation of QS. An-Nisa 'verse 1, which states that humans were created from nafsun wahidah (one soul). Wadud asserts that this concept indicates the equality of origin between men and women, thereby rejecting the patriarchal view that women were created from men's ribs as second-class beings. The research method used was qualitative with a literature study approach, through analysis of Amina Wadud's works and relevant academic literature. The results of the study show that Wadud's feminist hermeneutics not only serves as a critique of classical interpretations that are laden with patriarchal bias, but also provides a strong theological basis for the recognition of gender equality in Islam. This approach opens up space for women to play an equal role in the social, political, and religious spheres. Thus, Amina Wadud's thinking contributes significantly to building a more inclusive, egalitarian, and gender-equitable understanding of Islam in accordance with the universal values of the Qur'an.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.