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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.

The advancement of information technology today has provided great benefits in the world of education. The construction of computer-based academic information system websites is part of the use of information technology. With information technology, it is possible for academic databases to be processed quickly and easily, so that in the presentation of the required academic information reports can be obtained precisely, quickly, and efficiently. SMP Negeri 2 Wewewa Utara already has sufficient information technology infrastructure, but it has not been used optimally in academic management. The school already has a computer lab and internet access, but academic data management is still carried out manually using a ledger and Microsoft Excel application. To address issues such as unintegrated student data, complex and time-consuming grade management processes, delivering academic information, and monitoring student learning progress, an academic information system is needed. The purpose of developing this system is to solve the problems contained in the previous system by creating an academic information system at SMP N 2 Wewewa Utara. With the implementation of the new system at SMP N 2 Wewewa Utara, the knowledge and skills of employees, teachers, and principals in the field of web-based academic information systems can be improved. In the development of the academic information system that will be included in the system, it includes information about students, subjects, student classes, classrooms, teachers, homeroom teachers, and student score reports. The software used is DBMS (Database Management System), while the data storage medium is MySQL and PHP as a Programming Language.

Human personality is diverse and influences the way people act, think, and interact in daily life. These differences also appear in literature, especially in drama, where characters reveal their traits through dialogue, actions, and conflicts. This study aims to analyze the personality of Othello in William Shakespeare’s Othello using the Big Five Personality Traits (OCEAN). This research employs a qualitative descriptive method with data taken from selected dialogues and monologues that reflect Othello’s personality. The data were categorized into five dimensions: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The findings show that Neuroticism (30.77%) is the most dominant trait in Othello’s character, followed by Extraversion (23.08%), Agreeableness (15.38%), Conscientiousness (15.38%), and Openness (15.38%). These traits influence his decisions, relationships, and eventual downfall, indicating that emotional instability and insecurity play a crucial role in the tragic development of the play. This study demonstrates how universal personality traits shape human behavior and conflict in Shakespeare’s drama and shows that modern psychological frameworks can be effectively applied to classical literature.

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.

Current advances in information technology have encouraged universities to utilize student academic data as a basis for decision-making, one of which is predicting academic achievement. This study aims to apply the C4.5 algorithm to develop a system for predicting student academic success in the Islamic Religious Education Study Program. This method was chosen because it produces a decision tree model that is easy to understand and has a high level of accuracy. The data used comes from student achievement indexes from semesters 1 to 5. The research results showed that the prediction system achieved 99.62% accuracy and achieved high recall precision across each class category. This demonstrates the effectiveness of the C4.5 algorithm in predicting student academic achievement and has the potential to serve as a valuable tool for decision-makers in higher education.

Aritmia merupakan gangguan irama jantung yang umum terjadi pada lansia dan dapat menimbulkan risiko kesehatan serius jika tidak terdeteksi secara dini. Penelitian yang dilakukan bertujuan untuk mengidentifikasi aritmia pada lansia menggunakan algortima K- Nearest Neighbor (KNN) berdasarkan data elektrokardiogram (EKG). Data yang digunakan berjumlah 105 data EKG lansia yang diperoleh dalam format CSV. Proses awal melibatkan pembersihan dan normalisasi data menggunakan metode StandardScaler, serta pelabelan awal menggunakan algoritma K-Means Clustering untuk mengelompokkan data ke dalam dua kelas: Normal dan Sangat Berpotensi Aritmia. Data kemudian dibagi menjadi 70% data latih dan 30% data uji dengan metode stratified split untuk menjaga proporsi label. Model KNN dilatih dengan parameter k = 3, dan dievaluasi menggunakan confusion matrix serta classification report. Hasil pengujian menunjukkan akurasi model sebesar 97% dengan nilai precision dan recall yang tinggi pada kedua kelas. Hasil ini menunjukkan bahwa algoritma KNN efektif dalam mengklasifikasikan kondisi aritmia pada lansia dan memiliki potensi untuk diterapkan dalam sistem pendukung diagnosis berbasis data EKG.

Communication is a fundamental aspect of human life. However, individuals with hearing and speech impairments often face barriers in communicating with the general public. The Indonesian Sign System (SIBI) serves as a communication solution for the deaf and speech-impaired community in Indonesia, yet public understanding of SIBI remains limited. To address this issue, this study aims to develop an automatic translation model from SIBI sign language into Indonesian text by utilizing Deep Learning technology, specifically the Convolutional Neural Network (CNN) algorithm. CNN was chosen for its ability to effectively recognize visual patterns, making it suitable for processing hand gesture images in sign language. This research involved collecting and classifying a dataset of hand images based on the alphabet or words in SIBI, which were then used to train the CNN model. The designed CNN model was built to accurately classify hand signs and translate them into Indonesian text. The results of this study have the potential to serve as a supportive solution for inclusive communication between the deaf community and the wider public, and can be further developed for contextual sentence translation. Keywords: Indonesian Sign System (SIBI), CNN, Deep Learning, Automatic Translation, Inclusive Communication

Credit scoring is an important element in decision-making for providing financing, especially for microfinance institutions. Several methods for predicting credit scoring include Decession Tree, Gradient Boosted, Neural Network, K-NN, and Rule Induction. This study aims to improve the accuracy of financing risk prediction by efficiently integrating historical data. The Neural Network (NN) algorithm is a machine learning algorithm consisting of neurons (nodes) connected to each other in several layers (input, hidden, and output). NN is used for pattern recognition, classification, regression, and complex non-linear modeling. The NN algorithm has the advantage of working well on large and diverse data and unstructured data. However, the NN algorithm has weaknesses such as overfitting and data dependence. In this study, the integration of the Sample Bootstrapping and Weighted Principal Component Analysis (PCA) methods is proposed to improve optimal accuracy in the NN algorithm. The Sample Bootstrapping method is used to reduce the amount of training data to be processed. The Weighted PCA method is used to reduce attributes. This study uses a financing customer dataset. The results of the study show that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA resulted in an accuracy increase of 1-3% (97%-99%) compared to other algorithms. Therefore, it can be concluded that the integration of the NN algorithm with Sample Bootstrapping and Weighted PCA produces better accuracy than other algorithms

This study aimed to improve the speaking skills of sixth-grade students at SDN Negeri Kelapa Dua Wetan 01 Pagi in using the Simple Past Tense pattern (was/were) through the implementation of the Role Playing method. The research was conducted in two cycles involving 32 students as the subjects. The research method used was Classroom Action Research, consisting of the stages of planning, action implementation, observation, and reflection in each cycle. Data were collected through oral pre-tests and post-tests, as well as observations of students’ learning activities during the lessons. The results showed a significant improvement in students’ speaking skills. Their average score increased from 60.3 on the pre-test to 74.1 on the post-test. The percentage of students who achieved the Minimum Mastery Criteria also rose from 31% to 75%. The Role Playing method proved effective in enhancing students’ accuracy in using was/were, improving speaking fluency, and boosting self-confidence in oral communication. In conclusion, the application of the Role Playing method had a positive and significant impact on English learning outcomes among the sixth-grade students at SDN Negeri Kelapa Dua Wetan 01 Pagi. It is recommended that teachers continue to apply this method consistently, and that schools provide additional supporting facilities to optimize the learning process. For future studies, teachers and students are encouraged to expand the variety of role-play scenarios and include a control group to obtain a deeper and more comprehensive understanding of the effectiveness of the Role Playing method.