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This study analyzes the minimum age of marriage in Islamic jurisprudence and compares it with the positive law regulations in Indonesia and Malaysia. Using a normative legal method with comparative and conceptual approaches, the study draws on primary sources, including the Qur'an, hadith, Law Number 16 of 2019 on Marriage in Indonesia, and the Islamic Family Law (Federal Territories) Act 1984 in Malaysia. The analysis focuses on how Islamic legal principles concerning marriage eligibility are interpreted and incorporated into contemporary legal frameworks in both countries. The findings reveal that Islamic jurisprudence (fiqh) associates marital readiness with the concept of baligh (puberty) without prescribing a specific numerical age, whereas state law establishes fixed minimum age requirements to safeguard the rights and welfare of women and children. Indonesia sets the minimum marriage age at 19 years for both males and females, while Malaysia prescribes 18 years for males and 16 years for females, with judicial dispensation available in both jurisdictions under certain circumstances. These legal arrangements demonstrate each country's effort to harmonize classical Islamic jurisprudence with contemporary social protection objectives through institutional ijtihad, reflecting a balance between religious principles, legal certainty, and public welfare in regulating marriage.

The development of digital services by BPJS Ketenagakerjaan through the JMO (Jamsostek Mobile) application has triggered a surge in large-scale and unstructured user reviews on the Google Play Store, thereby complicating manual analysis and conventional sentiment analysis in accurately identifying specific issues. This research aims to implement the Aspect-Based Sentiment Analysis (ABSA) method to granularly evaluate JMO application reviews based on specific aspects, while simultaneously addressing class imbalance and computational efficiency issues. The proposed method combines the pretrained IndoBERT model as a contextual feature extractor, the SMOTE technique to balance the training data, and an artificial neural network (Neural Network) as the classification layer without performing full fine-tuning. The dataset used consists of 90,268 unique reviews categorized into five main aspects through keyword matching, namely General Satisfaction/Complaints, Performance & Stability, Service & Support, Feature Quality, and UI/UX, with initial lexicon-based labeling using the InSet Lexicon. The research results indicate that the proposed model successfully achieves highly optimal performance with an accuracy rate of 91.81% and a weighted F1-score of 92%. Furthermore, the implementation of SMOTE proved effective in enhancing model reliability on the minority class (negative sentiment), achieving an F1-score of 89%. The implications of this research contribute an accurate and efficient aspect-based sentiment analysis framework for developers, and serve as a strategic evaluation tool for BPJS Ketenagakerjaan in mapping specific user complaints to accelerate continuous improvements in the performance, stability, and service quality of the JMO application.

Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

This study investigates the development of Indonesian language literacy among third-grade elementary school students. The primary problems identified were acute classroom passivity and low reading comprehension, evidenced by an initial learning mastery of only 45.16%, which were largely driven by conventional teacher-centered pedagogy. The objective of this research is to enhance early-grade reading literacy and active participation through an innovative instructional intervention. The proposed method employed a two-cycle Classroom Action Research (CAR) design at SDN Jrebeng Kulon 1, integrating the Problem-Based Learning (PBL) model assisted by serial picture media. This approach utilizes chronological visual scaffolding to facilitate narrative comprehension for students in the concrete operational stage. The results demonstrated a highly significant academic progression: classical learning mastery increased to 70.97% (mean score: 80.80) in Cycle I and culminated in an absolute 100% mastery rate (mean score: 94.51) by the end of Cycle II. The synthesis of these findings reveals that transitioning from static visual aids to serial visual stimuli within a problem-oriented framework effectively mitigates cognitive dissonance and eradicates classroom passivity. In conclusion, the integration of the PBL model with serial picture media serves as a comprehensive pedagogical solution that not only maximizes cognitive reading comprehension but also reconstructs students' verbal articulation and social-collaborative skills, offering a highly scalable strategy for early primary education.

Teachers, as professionals, play a crucial role in managing learning in the classroom. One essential skill that teachers must have is managerial skills, which involve organizing learning activities, setting up the learning environment, and applying suitable methods for learning tasks. In the context of differentiated learning focused on student needs, teacher managerial competence is vital for designing and managing instruction tailored to each student's requirements. Differentiated learning is an approach that emphasizes meeting the needs, interests, and learning styles of individual students. This study was conducted using a literature review method, drawing data from various sources including books, articles, and relevant previous research. The review shows that for successful differentiated learning, teachers need to implement managerial strategies for identifying students’ learning needs, managing content, processes, learning products, and conducting regular formative assessments. Furthermore, teachers must create an environment that supports the learning process through strong collaboration among students, between teachers and students, and with parents. Therefore, improving teacher managerial skills is one way to achieve inclusive learning and accommodate student diversity.

This study aims to analyze the effect of cutting parameters on surface roughness in the turning process of AISI 1045 steel. The investigated parameters include cutting speed, feed rate, and depth of cut. A quantitative approach was employed using multiple linear regression analysis with SPSS software. The dataset consisted of 30 simulated experimental observations with varying cutting parameter conditions. Prior to regression analysis, classical assumption tests including normality, multicollinearity, and heteroscedasticity tests were conducted to ensure the validity of the model. The results indicated that all assumptions were satisfied. The findings reveal that simultaneously, all independent variables have a significant effect on surface roughness with a coefficient of determination of 82.1%. Partially, Feed rate and cutting speed significantly influence surface roughness, while depth of cut does not show a significant effect. Feed rate is identified as the most dominant variable affecting surface roughness, where an increase in Feed rate leads to higher roughness values. In contrast, increasing cutting speed tends to reduce surface roughness. This study highlights the importance of controlling cutting parameters, particularly Feed rate and cutting speed, to improve machining quality. The results provide practical implications for manufacturing industries in optimizing machining parameters to achieve better surface quality and process efficiency.

Malware represents an evolving cybersecurity threat that demands more effective detection methods. Conventional signature-based detection systems have limitations in identifying new variants, driving the development of deep learning-based approaches. This research implements and evaluates four variants of the YOLOv11 algorithm (n, s, m, l) for malware classification based on visual image representation. The dataset consists of 22,056 malware and benign images, divided into 70% training, 15% validation, and 15% testing across 8 classes (adware, backdoor, benign, downloader, spyware, trojan, virus, worm). Each model was trained for 100 epochs with batch size 32 using Google Colab with GPU support. Results demonstrate that all variants achieve high accuracy (97.8%-98.1%) with YOLOv11m as the best performer (98.1%). YOLOv11n offers optimal balance between accuracy (97.9%) and efficiency (1.5M parameters, 0.3 ms/img inference) ideal for real-time applications. This research surpasses previous methods such as K-NN (97.18%) and hybrid CNN (96.55%) with superior inference speed (0.3-0.9 ms/img vs tens to hundreds of ms/img), proving the effectiveness of YOLOv11 for fast, accurate, and scalable malware detection.

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

Batik motif classification has attracted growing attention in visual computing due to its role in cultural heritage preservation, textile informatics, museum documentation, and automated cataloging. Although many studies report high classification accuracy, robustness under real-world acquisition conditions remains insufficiently understood. Batik images are frequently affected by illumination variation, blur, folds, watermark overlays, wearable deformation, scale inconsistency, and background clutter, creating challenges that extend beyond conventional image-noise assumptions. Existing studies largely focus on improving classification performance, while the interactions among acquisition variability, feature representation, evaluation practice, and deployment constraints remain fragmented. This systematic literature review addresses this gap by synthesizing batik classification research through a robustness-aware perspective. Using query expansion, backward and forward citation chaining, relevance screening, and thematic coding, 116 candidate records were identified, resulting in 50 highly relevant studies for detailed analysis. The review reveals that robustness is shaped less by denoising alone than by the combined effects of acquisition conditions, representation design, evaluation realism, and deployment context. Handcrafted descriptors remain competitive for small datasets and structured motifs due to their data efficiency and interpretability, whereas deep learning models achieve the highest reported accuracy when supported by sufficient data diversity and realistic augmentation. Hybrid representations emerge as the most consistently balanced approach, combining local texture stability with higher-level abstraction across heterogeneous acquisition settings. The review further identifies recurring robustness failure patterns, including background dependency, illumination instability, motif-scale inconsistency, wearable deformation, and source-shift vulnerability. Based on these findings, a robustness-oriented research agenda is proposed, emphasizing cross-acquisition evaluation, representation-stability analysis, batik-specific robustness benchmarks, acquisition-aware augmentation, and deployable lightweight or hybrid architectures. The study contributes a domain-specific synthesis that reframes batik motif classification from an accuracy-centric task toward a robustness-aware visual recognition problem.

This study was conducted to analyze the relationship between students’ critical thinking skills and mathematical problem-solving abilities through a literature review approach. The study is based on the importance of mastering higher-order thinking skills in the mathematics learning process, particularly when students are faced with complex problems related to real-life situations. The method used in this research was a literature review by examining various relevant scientific journals and academic books published between 2021 and 2026. The data analysis process was carried out through stages of identification, classification, evaluation, and synthesis of the collected sources. The findings revealed a significant and positive relationship between critical thinking skills and students’ mathematical problem-solving abilities. Critical thinking skills play an important role in helping students understand problems, process and analyze information, select appropriate solution strategies, and systematically review the results obtained. Students with strong critical thinking skills generally demonstrate more optimal mathematical problem-solving abilities. In addition, the implementation of learning models such as Problem-Based Learning and contextual approaches has been considered effective in improving both abilities. Therefore, critical thinking skills are regarded as an essential aspect that needs to be developed in mathematics learning in order to enhance students’ mathematical problem-solving abilities.

Nature tourism is one of the sectors that plays an important role in supporting the development of regional tourism, including in Lahat Regency, which has significant waterfall tourism potential. Currently, many visitors share their reviews and experiences through digital platforms such as Google Maps. This review can be used as a source of information to understand the public's evaluation of the quality of tourist attractions. This study aims to examine public perception of tourist attractions in Lahat Regency using the Support Vector Machine (SVM) method. Research data were collected through scraping from Google Maps, totaling 500 reviews from five tourist attractions, namely Curup Maung, Curup Buluh, Senyawe Waterfall, Panjang Waterfall, and Green Canyon. The research stages include data preprocessing, consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming. After that, feature extraction was carried out using the TF-IDF method and the classification process using the SVM algorithm. Based on the research results, the Support Vector Machine (SVM) method is able to perform sentiment classification quite well, although the accuracy level varies for each tourist attraction. Curup Maung and Panjang Waterfall achieved the highest accuracy level of 90%. Nevertheless, most visitor reviews were dominated by negative sentiments. This indicates that there are still several aspects that need to be improved, particularly related to tourist facilities and services. This research is expected to serve as a consideration for tourism managers and local governments in efforts to improve management quality as well as the development of tourism in Lahat Regency.

Postharvest handling of leafy vegetables, such as kale (Brassica oleracea var. acephala), is crucial for maintaining product quality and market value. This study aims to review the application of sorting and grading processes in improving the quality of horticultural products. The method used is a literature review of scientific journals and relevant references related to postharvest handling of vegetables. The findings indicate that sorting functions to separate damaged and unmarketable products based on physical condition, while grading classifies products into quality categories based on size, shape, and visual appearance. These processes contribute to reducing postharvest losses, improving product uniformity, and increasing market value. Furthermore, proper sorting and grading support quality control and enhance efficiency in distribution and marketing systems. Therefore, sorting and grading are essential components of postharvest management to ensure product quality and market competitiveness.

Background: Acne vulgaris is one of the most common skin problems and is closely associated with the activity of Propionibacterium acnes. Long-term use of antibiotics may lead to bacterial resistance; therefore, alternative antibacterial agents derived from natural products are needed. White rice (Oryza sativa L.) is known to contain bioactive compounds such as flavonoids, alkaloids, terpenoids, tannins, and saponins, which potentially exhibit antibacterial activity. Objective: This study aimed to evaluate the antibacterial activity of ethanol extract of white rice against the growth of Propionibacterium acnes. Methods: This research was conducted as a laboratory experimental study using a post-test only control group design. The ethanol extract of white rice was prepared by maceration using 70% ethanol. Antibacterial activity was evaluated using the disc diffusion method with extract concentrations of 100%, 50%, 25%, 12.5%, and 6.25%. Clindamycin was used as a positive control, while sterile distilled water served as a negative control. The parameter observed was the diameter of the inhibition zone. Result: The results of phytochemical screening showed that the ethanol extract of white rice (Oryza sativa L.) contains flavonoids, alkaloids, and terpenoids. Antibacterial activity test showed that the ethanol extract of white rice at concentrations of 100%, 50%, and 25% was able to inhibit the growth of Propionibacterium acnes by forming an inhibition zone, while at concentrations of 12.5% and 6.25% no inhibition zone was formed. The highest inhibition zone diameter was found at a concentration of 100%. The ethanol extract of white rice at concentrations of 100% (7.65 mm), 50% (6.77 mm), and 25% (6.15 mm) was able to inhibit the growth of Propionibacterium acnes, and was classified as having moderate inhibition. In contrast, at concentrations of 12.5% and 6.25%, the ethanol extract of white rice did not show any inhibitory activity detected in vitro using the disc diffusion method. The results of statistical analysis showed a p value <0.001, which means there was a significant difference in the diameter of the inhibition zone between the treatment groups. Conclusion: In conclusion, the ethanol extract of white rice exhibits antibacterial activity against Propionibacterium acnes in a concentration-dependent manner; however, its inhibitory effect remains lower than that of clindamycin.

This study aims to examine the development of teachers’ multicultural competence at SMA Ta'miriyah Surabaya. The study employed a qualitative approach using a case study design. Data were collected through interviews, observations, and documentation involving the principal and teachers as research informants. Data analysis applied the interactive model of Miles, Huberman, and Saldana, which consisted of data condensation, data display, and conclusion drawing. The findings revealed that the development of teachers’ multicultural competence at SMA Ta'miriyah was carried out through three main pathways. First, training programs that implicitly integrated multicultural values into general educational programs, such as the teacher mobilization program. Second, organizational development grounded in Islamic values as the foundation for fostering tolerance and respect for diversity, implemented through curriculum integration, school regulations, teacher role modeling, and collaboration among teachers. Third, career development, which remained primarily focused on academic aspects and had not explicitly incorporated indicators of multicultural competence. Teachers’ multicultural competence at SMA Ta'miriyah was reflected in three main dimensions. In terms of knowledge, teachers acquired understanding through teaching experience, formal education, and training. In terms of attitudes, teachers demonstrated tolerant and inclusive behavior when interacting with students from diverse cultural and ethnic backgrounds. In terms of skills, teachers were able to manage classroom diversity harmoniously through adaptive and responsive pedagogical approaches that accommodated students’ differences.

This study analyzes netizen sentiment concerning the 17+8 public aspirations circulating the digital platform X spanning the period from August 18 through October 31, 2025. 1,837 comments obtained through scraping method. Classification Research stages include data preprocessing, sentiment weighting based on lexicon, and feature extraction using TF-IDF. Data 80% used for learning purposes and the remaining 20% utilized for validation. The findings reveal that the majority of comments, amounting to 81.14%, contained negative sentiment, while the remaining 18.86% were positive. The outcomes demonstrate that community reactions toward the 17+8 People's Demands were dominated by unsupportive views. From a theoretical standpoint this scholarly work offers to enriching knowledge concerning public opinion classification on political issues through a computational approach, while also serving as a reference for future research focused on improving the accuracy of sentiment analysis related to political dynamics and the behavior of state institutions.

Indonesia’s multicultural diversity poses serious challenges in maintaining social harmony, marked by increasing cases of intolerance driven by differences in ethnicity, religion, and culture. Education, particularly civics (PPKn), plays a strategic role in instilling tolerance values from an early age. This study aims to analyze the strategies used by elementary school teachers in cultivating tolerance attitudes through PPKn  learning, identify the implementation of tolerance values in classroom activities and student social interactions, and reveal the challenges faced by teachers along with effective and contextual learning strategies. This study employed a qualitative descriptive approach based on library research, collecting data from accredited scientific journals published between 2021 and 2025, sourced from databases including google scholar, DOAJ, and sinta- indexed journals. Data were analyzed using descriptive qualitative analysis through stages of data reduction, data presentation, and conclusion drawing, with source triangulation appliet to ensure validity. The findings reveal that affective teachers employ a holistic approach encompassing cognitive, affective, and psychomotor domains, consistent with Bloom’s revised taxonomy. Teachers serve not only as knowledge transmitters but also as moral role models, as supported by Bandura’s Social Learning Theory. Implementation is carried out through three channels: intracurricular, co-curricular, and extracurricular activities. Key challenges include dominance of cognitive approaches, limited understanding of multicultural education, and negative influencesof social media. This study implies the urgent need for continuous professional training for PPKn teachers, differentiated and inclusive learning design, and active collaboration between schools and families to strengthen the internalization of tolerance values in elementary school students.

This study examines sarcastic implicatures in the 2024 United States presidential debate between Joe Biden and Donald Trump, with a particular focus on Donald Trump’s utterances. The study aims to identify the forms and types of sarcastic implicatures employed in political discourse during the debate. A qualitative descriptive method with a pragmatic approach was used to analyze how implied meanings are constructed and interpreted within the context of political communication. The data consisted of debate transcripts and video recordings broadcast by CNN, selected based on utterances containing elements of sarcasm. Data analysis was conducted through four stages: identification, classification, coding, and interpretation. The findings reveal that sarcastic implicatures are realized in two main forms, namely indirect non-literal utterances and direct non-literal utterances. In addition, several types of sarcastic implicatures were identified, including undermining, mockery, insult, criticism, and threat. The most dominant type was undermining, which was used to weaken the image and credibility of political opponents. These findings indicate that sarcastic implicatures function as an effective rhetorical strategy in political communication to influence public opinion, shape audience perceptions, and strengthen the speaker’s political position in televised political debates.

This study examines the regulation of copyright royalties as joint property in Indonesia, the United States, and Europe, aiming to provide recommendations for better legal implementation. The research employs a normative juridical method with a comparative legal approach, utilizing library research and qualitative analysis of primary and secondary legal materials. The findings indicate that Indonesia, the United States, and Europe share common ground in recognizing royalties derived from copyright as joint property when such economic benefits are obtained during marriage. However, significant differences exist in their approaches. European countries, particularly Spain and the Republic of Moldova, clearly distinguish between exclusive rights and economic rights, where copyright remains the creator's personal property while royalties are classified as joint property. The United States demonstrates considerable flexibility through state-level regulations, adopting either community property systems or equitable distribution systems. Indonesia, through Decision No. 1622/PDT.G/2023/PA.JB, has begun recognizing royalties as joint property. Nevertheless, Indonesia still requires clearer and more comprehensive regulations to ensure legal certainty regarding the status of royalties as joint property and their distribution following divorce. This study contributes to developing legal frameworks that balance protecting creators' personal rights with the principle of fairness in family law.

 Education in vocational high schools (SMKs) requires effective data management to improve students’ academic achievement and discipline. At SMK Islam Secang, students’ academic scores and attendance data have so far functioned merely as administrative archives, making it difficult to identify patterns of student performance. This study aims to classify students based on academic achievement and discipline by applying the K-Means Clustering algorithm using RapidMiner. The data used in this study consist of scores from six subjects and attendance records of 35 students from the Light Vehicle Engineering (TKR) department over two semesters. The data were obtained from original school records, compiled using Microsoft Excel, and processed in RapidMiner. The clustering process employed four clusters for academic achievement and two clusters for discipline, with Euclidean Distance used as the similarity measure. The results show that in the first semester, students were grouped into four academic achievement clusters: high achievement (6 students), moderate achievement (7 students), potentially problematic (14 students), and problematic (8 students). In the second semester, the distribution changed to high achievement (19 students), moderate achievement (14 students), potentially problematic (4 students), and problematic (1 student). Meanwhile, student discipline was divided into two clusters: disciplined (31 students) and undisciplined (4 students). These results demonstrate that K-Means Clustering is effective in mapping student conditions, revealing patterns in academic performance and attendance, and supporting educational evaluation, learning planning, and early detection of students who require academic or disciplinary intervention. Keywords: Data Mining, K-Means Clustering, Academic Achievement, Discipline, RapidMiner, Vocational High School (SMK)