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The book Uqūd al-Lujjayn fī Bayān Ḥuqūq az-Zaujayn by Sheikh Nawawi al-Bantani is one of the classical texts that is very popular among Islamic boarding schools and the Muslim community of the archipelago. However, in contemporary discourse, this book is often the target of criticism from Muslim feminists who accuse it of being full of gender bias and patriarchal. This criticism arises because of discussions about the wife's obligation to obey her husband, the prohibition of leaving the house without permission, and the husband's permissibility to reprimand his wife who is considered nusyūz. This paper attempts to conduct a philological and hermeneutic analysis of the text Uqūd al-Lujjayn by reviewing the historical, methodological, and fiqh reference sources of Sheikh Nawawi al-Bantani. This study found that the patriarchal character in the book is pseudo (pseudo), not ideological. Sheikh Nawawi actually wrote this book within the tradition of Islamic jurisprudence (fiqh akhlaqiyyah), not as a legitimation of male power over women. Through a philological approach, it was found that the text's structure and selection of hadith in this book aimed to educate household morals, not to reinforce the patriarchal system. Therefore, accusations of gender bias against Sheikh Nawawi require methodological reexamination to avoid falling into ahistorical ideological readings.

This study aims to analyze the acceptability, nutritional content, and financial feasibility of processed food products based on kimpul flour (Xanthosoma sagittifolium) and milkfish (Chanos chanos) as a functional local food innovation with potential for development to support food security. Kimpul flour was selected as a substitute for wheat flour due to its complex carbohydrate content, low glycemic index, and dietary fiber beneficial for digestion. Milkfish was utilized as an economical animal protein source rich in omega-3 fatty acids and widely available in Indonesia. The developed products included milkfish risoles, milkfish satay lilit, and kimpul pancake with various substitution formulations, tested using an experimental laboratory method. A hedonic test was conducted with 30 semi-trained panelists to evaluate color, taste, aroma, and texture. Nutritional content was analyzed using the Fat Secret application, while financial feasibility was assessed through a two-year cash flow projection. The results indicated that kimpul flour substitution at 8% in risoles, 10% in satay lilit, and 50% in pancakes yielded the highest acceptability scores based on organoleptic evaluations favored by the panelists. Nutritional analysis demonstrated an increase in dietary fiber and a balanced macronutrient profile. Financial analysis showed that the business is feasible and profitable for development at the micro, small, and medium enterprise (MSME) scale. This study concludes that kimpul flour and milkfish have strong potential as functional food ingredients with high economic value.

This study analyzes the implementation of Islamic inheritance law (mawaris) in contemporary Muslim society as a means to realize justice and blessing (barakah) in the distribution of inherited wealth. The research is motivated by the growing number of inheritance disputes caused by limited understanding of faraid principles, the persistence of customary inheritance practices, and the coexistence of national legal systems that often diverge from Islamic inheritance regulations. These conditions frequently lead to unequal distribution and family conflicts. The study aims to examine the relevance of mawaris in modern contexts and to explore how its application, when aligned with the objectives of Islamic law (maqasid al-shariah), can address current social challenges. Employing a qualitative method with a normative library research approach, the study draws on primary sources such as the Qur’an, Hadith, and classical fiqh texts, as well as secondary sources including contemporary books and scholarly journal articles. Data were analyzed through content analysis to identify key concepts and arguments regarding the contemporary application of mawaris. The findings show that proper implementation of Islamic inheritance law fosters justice, minimizes family disputes, strengthens kinship ties, and safeguards the blessing of wealth. Nevertheless, insufficient public literacy on Islamic inheritance law and the dominance of non-sharia considerations remain significant barriers. Therefore, the study underscores the need for enhanced public education, greater involvement of religious scholars in inheritance mediation, and contextual integration of Islamic legal principles to ensure fair and beneficial inheritance distribution in modern Muslim families.

Madhhab fanaticism in Islamic education, which appears in fiqh teaching practices that tend to absolutize a single madhhab, restrict dialogical space, and insufficiently appreciate the diversity of scholarly opinions. Such conditions may foster exclusive and intolerant religious attitudes among students. To respond to this problem, the study employs a qualitative approach with a library research design by critically examining books, journal articles, and documents related to fiqh madhhabs, Islamic education, religious moderation, and multicultural education. The data are analyzed using content analysis and descriptive–analytical techniques to map the forms of madhhab fanaticism in the learning process, the stages of transformation in madhhab understanding, and the pedagogical strategies that support the emergence of tolerant attitudes. The findings indicate that the transformation toward a more tolerant understanding of madhhabs is supported by curricula that accommodate comparative fiqh (multi-madhhab), inclusive learning materials, dialogical teaching methods, and teachers’ role modeling of moderate attitudes. Inhibiting factors include deeply rooted madhhab fanaticism, the dominance of conservative curricula, ideological bias in textbooks, and a weak culture of critical thinking. It is concluded that strengthening fiqh instructional design that consciously integrates religious moderation and multicultural education is essential for shifting madhhab understanding from patterns of fanaticism to patterns of tolerance within Islamic educational institutions.

Gender equality in the religious sphere in Indonesia is a complex phenomenon, shaped by the interaction between theological doctrines, institutional practices, and the socio-cultural context of each tradition. This study employs a qualitative-descriptive approach through literature review to analyze six major religions in Indonesia: Catholicism, Protestantism, Islam, Hindu Dharma, Buddhism, and Confucianism. The analysis focuses on three main dimensions: theological perspectives regarding gender roles and positions, social changes influencing gender relations, and transformations in institutional structures and practices that support gender equality. The findings indicate that historically, women were often placed in subordinate positions due to patriarchal norms, although the core teachings of these religions contain egalitarian principles and recognition of women’s dignity. Contemporary transformations are reflected in the reinterpretation of religious texts and practices that promote gender equality: Catholicism emphasizes the recognition of women’s dignity within the clerical hierarchy; Protestantism opens opportunities for the ordination of female pastors; Islam reinterprets the principles of Qawwamun and family law rights; Balinese Hindu Dharma adjusts women’s roles in rituals and public spheres; Buddhism revives the bhikkhuni order within the sangha; and Confucianism modernizes the interpretation of Wu Lun and San Cong to emphasize mutual responsibilities between husband and wife. In conclusion, despite persistent patriarchal challenges, this study affirms that religion has the potential to act as an agent of social transformation promoting gender equality while maintaining the moral, spiritual, and traditional values distinctive to each tradition. These findings contribute significantly to academic literature, interfaith dialogue, and the development of more gender-inclusive policies.

The purpose of this study is to develop teaching materials in the form of interactive textbooks on the subject of Rukun Iman (the Five Pillars of Islam) for MIS NU students in Palangkaraya City. The motive for this study is the need for more contextual and relevant teaching resources on Aqidah Akhlak (Islamic beliefs and morals) in order to optimize students' understanding. The ADDIE development model, which includes the phases of needs analysis, product design, development, implementation, and evaluation, was used in this study. Validation was carried out by subject matter experts, design experts, and educators as practitioners to ensure the suitability of the product. Based on the validation results, subject matter experts and designers consistently rated the teaching materials as “very feasible” in terms of media functionality, visual appeal, and material completeness. Improved learning outcomes through pre- and post-tests, as well as a feasibility rate of 93.02%, were demonstrated during implementation in a field test with 30 second-grade students. Moderate to significant learning gains were shown by N-Gain analysis. The findings prove the effectiveness of interactive learning books in improving students' understanding of the Five Pillars of Islam and providing alternative teaching resources that are more interesting and easier to understand. It is hoped that this publication will serve as a guide for educators in Islamic elementary schools to maximize the teaching of Aqidah Akhlak.

Textbooks are the heart of the learning process, therefore an in-depth analysis of the suitability, relevance, and pedagogical potential of textbooks is crucial to ensure that learning outcomes can be achieved optimally. This study aims to analyze and describe based on aspects of content, presentation, language, graphics in the Indonesian language textbook for grade II elementary school revised independent curriculum published by Erlangga by Dr.A. Indradi, M.Pd & Rahmah Purwahida, S.Pd., M.Hum. The method used is qualitative descriptive analysis by referring to textbook feasibility guidelines. The results of the analysis show that this textbook is generally suitable for use. The suitability of the material with learning outcomes includes the development of four language skills, namely listening, speaking, reading, and writing, which are presented in an integrated and contextual manner. The presentation of the material is considered effective through the use of simple language, logical flow, and varied assignments. Overall, this textbook is a relevant and supportive learning resource in implementing the independent curriculum at the grade II elementary school level.

The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

Iwan Fals’ song "Bento" offers social criticism, which is analyzed using Teun A. Van Dijk's critical discourse analysis model. Released in 1989, the song critiques the social conditions and behaviors of the wealthy elite during the New Order era, highlighting issues of arrogance, corruption, and social inequality. This qualitative descriptive study applies the Analisis Wacana Kritis (AWK) method, analyzing the lyrics of "Bento" through three dimensions: text, social cognition, and social context. In the text dimension, the analysis reveals the main theme as a depiction of the fictional character "Bento," portrayed as an arrogant and corrupt figure. The text structure employs a narrative persona pattern, with semantic elements describing the luxury of New Order rulers and the exploitation of the common people. The stylistic devices, including anthropomorphism and rhetoric, emphasize the selfish enjoyment of the elite. In the social context, the song is a response to the corruption, collusion, and nepotism (KKN) that characterized the New Order regime. Through satire, "Bento" critiques the dominance of the elite and their disregard for the common people. Overall, the song exposes the power dynamics and corruption under the New Order regime.

This study aims to explain the application of various differentiated learning techniques based on Problem-Based Learning (PBL) to improve analytical and writing skills related to procedural texts among junior high school students. This research is based on students' lack of ability to understand and compose procedural texts methodically and in accordance with language conventions, which is caused by the prevalence of conventional teacher-centered learning. This study used a descriptive qualitative methodology involving seventh-grade students from a junior high school that has adopted the PBL model in Indonesian language subjects. Data were collected through observation, interviews, and documentation, then analyzed qualitatively. The results showed that the application of PBL along with differentiated learning and TPACK increased student engagement, accommodated diverse learning styles, and fostered critical thinking, analytical abilities, and collaborative skills. Learning became more meaningful and relevant, enabling students to compose procedural texts more effectively. This study recommends the application of the PBL model with differentiation as an innovative strategy to improve the quality of Indonesian language education in junior high schools.

The Ministry of Social Affairs has made a new breakthrough in facilitating the public in checking social assistance recipients, namely the social assistance check application. User reviews can be used to find out whether the application provides benefits to the community or not. However, these reviews need to be processed using sentiment analysis. Then to do sentiment analysis requires machine learning. One method that includes machine learning is Naïve Bayes. The purpose of this research is to implement the Naïve Bayes method in conducting sentiment analysis and find out whether the social assistance check application is beneficial to society based on the results of sentiment analysis. In this study, two categories of sentiment are used, namely positive and negative. The author collects by crawling using the Google Play Scrapper library. The results of crawling data obtained as many as 4000 data. The results showed that the actual data that had been labeled using Textblob resulted in 987 negative label reviews and 628 positive label reviews. Meanwhile, the Naïve Bayes method is able to analyze the review sentiment of the social assistance check application with the results of 1181 negative sentiments and 434 positive sentiments. The Naïve Bayes model has a good accuracy rate of 0.77 or 77% in analyzing sentiment for social assistance check application reviews.

This research focuses on the correlation between students’ reading habit and their reading comprehension in analytical exposition texts of eleventh grade students at SMA PGRI Pekanbaru. This research was motivated by the fact that some students face difficulties in reading English texts due to a lack of reading habit and challenges in understanding English texts, especially analytical exposition texts. This research employed a correlational design. The sample consisted of 37 eleventh grade students of SMA PGRI Pekanbaru in the academic year 2024/2025, selected through total sampling technique. To collect the data, the researcher used a questionnaire to measure students’ reading habit and an objective test to assess their comprehension of analytical exposition texts. Pearson Product-Moment correlation analysis showed a correlation coefficient (r_xy) of 0.501, indicating a moderate positive correlation between the two variables. With a significance value of 0.001, which is lower than 0.05, the correlation was statistically significant. Furthermore, since the r_value (0.501) was greater than the r_table value (0.344) for 37 respondents at the 5% significance level, the alternative hypothesis (Ha) was accepted. These findings indicate that students with better reading habits tend to have higher comprehension in understanding analytical exposition texts.

This study examines Japanese advertising and product-information texts on Shiseido Japan’s official website (www.brand.shiseido.co.jp) that grammatically prevent readers from construing statements as universal claims (“always” or “true for everyone”). It addresses two problems: how universal readings are blocked through grammatical construction in this register, and how the main blocking mechanisms differ in limiting generalisation and managing scope. The data consist of sentence-level usage, precautionary, and quality-related statements that plausibly invite broad general interpretations. Seven analytically representative tokens are used as illustrative evidence, covering wake-negation, baai-based case framing, and event/occasion packaging with V-ru koto ga aru, including rare-event calibration with mare ni and layered conditional framing. The study employs qualitative, theory-driven grammatical analysis focusing on clause structure, embedding, nominalisation, connective relations, and the compositional contribution of key markers. The results identify recurring templates with distinct structural signatures. Wake-negation blocks over-strong uptake by denying a candidate inference (…to iu wake de wa arimasen). Case framing with baai shifts categorical commitments into situation-restricted possibility (…baai ga arimasu), including complex variants that add causal linkage, avoidance marking, and directive closure. Event/occasion packaging with koto plus existential predication (…koto ga arimasu) presents anomalies as contingent occurrences, and it can be triggered by causal conditions (e.g., temperature change) or conditional frames (…to). Rare-event marking with mare ni further calibrates frequency and often co-occurs with contrastive reassurance about quality. Overall, universal-blocking emerges as a set of non-redundant grammatical routes that constrain inference, situational domain, and event profiling in a compact public informational genre.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

Injustice against women in contemporary Islamic law practice is still a serious problem, especially in cases of divorce, child custody, and the division of common property, which are often decided textually without considering the social, economic, and psychological aspects of women. This phenomenon shows that the application of Islamic law is still normative and does not fully reflect substantive justice as the purpose of maqāṣid al-syarī'ah. This research aims to analyze the nature of justice for women in the modern era, identify the steps needed to realize this justice, and formulate Islamic legal solutions based on maqāṣid al-syarī'ah that can be applied contextually in the religious justice system. The research method used is qualitative with a normative-empirical approach through literature analysis, case studies, and empirical data from religious court decisions and reports of official institutions such as Komnas Perempuan. The results of the study show that justice for women can only be achieved through a dynamic maqāṣid approach, by placing the interests of women and children above the legal-formalities of classical fiqh. The efforts needed include the integration of empirical data in judges' decisions, reform of religious justice policies, increasing the capacity of judges in understanding maqāṣid, and empowering women through legal literacy.

The concept of the Day of the LORD in the prophetic tradition of the Old Testament is often narrowly understood as a day of victory and salvation for Israel. However, the book of Amos offers a sharp critique of such an interpretation. This study aims to examine the eschatological meaning of the Day of the LORD in Amos 5:18–24 as a moment of judgment and restoration. This research employs a qualitative method with a biblical hermeneutical approach, particularly historical-contextual and theological analysis of Amos 5:18–24. The findings indicate that the Day of the LORD is portrayed as a divine event that brings judgment against social injustice, religious hypocrisy, and ethical deviation among the people of Israel. At the same time, the text opens the possibility of restoration through the demand for justice and righteousness as expressions of true worship. Therefore, Amos’s eschatology is not speculative about the future but functions as a prophetic critique that calls for repentance and social transformation. This study affirms that the Day of the LORD remains contextually relevant for the contemporary church in integrating faith, worship, and social justice as an inseparable unity.

This study aims to evaluate the effect of Return on Assets (ROA), Debt to Equity Ratio (DER), and Current Ratio on Firm Value, with Sales Growth as a moderator variable, in textile and garment companies listed on the Indonesia Stock Exchange (IDX) for the period 2020-2024. The population of this study consisted of 21 companies, and purposive sampling was used to select 19 companies according to the established criteria. The analytical method used was panel data regression analysis with the help of Eviews version 12.0. The results of this study indicate that Return on Assets (ROA) has a negative and significant effect on firm value, Debt to Equity Ratio (DER) and Firm Size have a positive and significant effect on firm value, Current Ratio (CR) does not have a significant effect on firm value, and Sales Growth cannot moderate the effect of Return on Assets on firm value. The condition of ROA that has a negative effect on firm value can describe a situation where the greater the company's profit in the form of assets, the lower the company's value will be, or conversely, the higher the company's value, the lower the company's assets will be.

The growth of e-commerce in Indonesia has led to an increase in product reviews, including for beauty products on Tokopedia and Shopee. These reviews serve as important sources of information to assess consumer satisfaction; however, manually analyzing thousands of reviews daily is impractical. This study applies Natural Language Processing (NLP) with Naive Bayes, C4.5, XGBoost algorithms to classify sentiment in Indonesian-language reviews. The dataset used consists of 76,256 reviews labeled as positive, negative, and neutral. The research stages include text preprocessing, feature representation using BoW and TF-IDF, data balancing through SMOTE, and model performance evaluation based on accuracy, precision, and recall. Differences in results among the algorithms were analyzed using ANOVA. The results show that Naive Bayes achieved the highest accuracy at 67.71%, followed by XGBoost at 65.91%, and C4.5 at 58.39%, with Naive Bayes performing best in identifying positive and negative sentiments, while XGBoost and C4.5 handled more complex data patterns effectively. These findings provide guidance for sentiment analysis in Indonesian and support businesses in obtaining automated insights from customer reviews to improve product quality and services.