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This study aims to analyze the in silico profile of the compound orientin derived from the water hyacinth plant (Eichhornia crassipes) as a potential antioxidant candidate. Orientin was selected based on its chemical structure data registered in PubChem, which provides complete information regarding molecular identity, physicochemical properties, and 2D and 3D structural representations. The prediction of biological activity was conducted using PASS Online, which indicated that orientin possesses a promising likelihood of exhibiting antioxidant activity according to relevant probability values. Furthermore, the safety assessment of the compound was carried out through ProTox-II to identify potential toxicity, including toxicity class, possible hepatotoxic effects, and other predicted safety parameters. To determine its pharmacokinetic profile, pkCSM was employed to predict ADMET characteristics such as absorption, distribution, metabolism, excretion, and additional toxicity risks. The results of these analyses show that orientin demonstrates favorable potential as an antioxidant candidate, supported by predicted pharmacological properties and relatively low toxicity levels according to in silico evaluations. Therefore, orientin has promising potential for further development in subsequent in vitro and in vivo studies.

This study aims to determine the extent of the role of the school environment in influencing the learning motivation of class X students in Muhammadiyah Maumere High School. The school environment is an external factor that plays an important role in supporting or inhibiting the learning process of students. The factors studied in this study include the physical condition of the school (such as classrooms, libraries, laboratories, and sports facilities), social aspects (support of teachers and peers), as well as psychological aspects (comfort, appreciation, and conducive learning atmosphere). The method used in this study is a qualitative descriptive method with data collection techniques through observation, interviews, and questionnaires to students and teachers. The result of the study shows that school facilities in Muhammadiyah Maumere High School still have many limitations, including inadequate classrooms, lack of learning technology tools such as projectors, small libraries with limited collection of books, and the unavailability of special space for arts and special sport facilities. However, despite these limitations, most students still show a high level of learning motivation. This is influenced by the support of teachers and peers, involvement in extracurricular activities, family support, and personal expectations of student achievement. This study concluded that the school environment indeed plays an important role in shaping student learning motivation, but social and psychological factors also have a big influence. Therefore, it is recommended that schools make improvements and develop physical facilities and strengthen positive social climate to support the student learning process optimally. 

The retention of customers in the retail banking sector is a critical economic imperative; however, predictive modeling is frequently hindered by severe class imbalance and the “Black Box” nature of complex algorithms. This study proposes a Heterogeneous Stacking Ensemble framework integrating XGBoost, CatBoost, and Random Forest base learners with a Logistic Regression meta-learner to forecast customer attrition. To overcome the pervasive “Majority Class Bias,” we introduce a “Dual-Imbalance Defense” that synergizes the Synthetic Minority Over-sampling Technique (SMOTE) with algorithmic cost-sensitive penalization. Furthermore, moving beyond standard accuracy metrics, the framework mathematically derives a dynamic classification threshold to guarantee a strict 0.90 recall rate, actively optimizing the capture of at-risk capital. Model opacity is addressed through the integration of a SHapley Additive exPlanations (SHAP) TreeExplainer. This cooperative game theory approach provides localized, patient-level “Reason Codes” for regulatory compliance and reveals global systemic vulnerabilities, including non-linear drivers such as the “Product Paradox.” Achieving a 0.90 recall rate and an AUC of 0.8654, this framework provides a statistically robust and operationally transparent tool for targeted customer retention.

This study aims to develop and implement an artificial intelligence-based reading learning application using Deep Learning technology to enhance the literacy skills of eighth-grade junior high school students. The research employed the Kemmis & McTaggart Classroom Action Research model combined with a mixed-methods approach. Data collection involved pretests and posttests, complemented by observations, interviews, and questionnaires. The findings revealed that the use of this application significantly improved students' reading comprehension, question-answering skills, and overall engagement in the learning process. Key features of the application, such as adaptive learning technology, allowed for real-time adjustments to the difficulty level of the material, which catered to each student’s individual learning pace. Additionally, the provision of instant feedback enhanced the learning experience by helping students understand their progress and areas for improvement. These results suggest that the application is an effective tool in fostering literacy development and aligns with the goals of the Independent Curriculum. Consequently, this Deep Learning-based application offers a promising innovation for improving student literacy skills in the digital age. 

Object detection plays a crucial role in intelligent transportation systems, particularly for outdoor traffic monitoring applications that require accurate and real-time performance under limited computational resources. Recent developments in YOLO-based architectures have introduced multiple model variants; however, their practical performance under constrained training conditions remains insufficiently explored. This study presents a comparative evaluation of YOLOv5, YOLOv7, and YOLOv8 for outdoor traffic object detection using a real-world dataset and identical experimental settings. The main objective of this research is to analyze the robustness and detection quality of different YOLO variants when trained with a limited number of epochs, reflecting practical deployment scenarios. All models were trained and evaluated using the same dataset, preprocessing pipeline, and hardware configuration to ensure a fair comparison. Performance evaluation was conducted using multiple metrics, including precision, recall, mAP@50, Precision–Recall curves, area under the curve (AUC), and peak F1-score. Experimental results indicate that YOLOv5 outperformed YOLOv7 and YOLOv8 in terms of overall detection stability and robustness. The merged Precision–Recall analysis shows that YOLOv5 achieved a higher effective AUC and superior mAP@50, reflecting better global detection performance. In addition, YOLOv5 exhibited a higher peak F1-score, indicating a more balanced trade-off between precision and recall. In contrast, YOLOv7 and YOLOv8 showed performance degradation under limited training conditions despite their more advanced architectures. These findings suggest that YOLOv5 remains a reliable and efficient solution for outdoor traffic object detection, particularly in resource-constrained environments. The study highlights the importance of comprehensive evaluation metrics and practical experimental settings when selecting object detection models for real-world applications.

This study aims to: (a) examine the partial influence of the E-Registration system on individual taxpayer compliance at KPP Pratama Bulukumba, (b) assess the influence of the E-Billing system on individual taxpayer compliance, and (c) determine the partial influence of the E-Filing system on individual taxpayer compliance at the same tax office. The population of this research consists of individual taxpayers registered in 2023, totaling 182,071 at KPP Pratama Bulukumba. The sample comprises 99 individual taxpayers who utilize E-Registration, E-Billing, and E-Filing systems in fulfilling their tax obligations. The sampling method employed is purposive sampling, based on specific criteria. Data were collected using a structured questionnaire. The data analysis involved data quality tests, classical assumption tests, multiple linear regression analysis, and hypothesis testing. The results indicate that: (1) E-Registration has a positive and significant effect on individual taxpayer compliance, (2) E-Billing has a positive and significant effect on individual taxpayer compliance, and (3) E-Filing has a positive and significant effect on individual taxpayer compliance at KPP Pratama Bulukumba.

Micro, Small, and Medium Enterprises (MSMEs) constitute the backbone of Indonesia's national economy, contributing approximately 61% of GDP and absorbing 97% of the total workforce; however, the majority of MSME actors, particularly in eastern Indonesia, continue to face structural barriers in digital technology adoption and capital access that constrain optimal business performance. This study aims to analyze the partial and simultaneous effects of marketing digitalization and business capital on MSME performance in Ambon City. A quantitative approach with associative-causal design was employed, involving 30 respondents selected through purposive sampling from active MSME operators in Ambon City. Data were collected via a five-point Likert scale questionnaire and analyzed using multiple linear regression with IBM SPSS version 26, preceded by validity, reliability, and classical assumption tests. Results demonstrate that marketing digitalization exerts a positive and significant partial effect on MSME performance (t = 8.060; sig. = 0.000; β = 1.061), establishing it as the most dominant predictor in the model. Conversely, business capital shows no significant partial effect (t = 0.746; sig. = 0.462), attributable to the homogeneity of capital access among MSME actors in archipelagic regions. Simultaneously, both variables significantly influence MSME performance (F = 287.070; sig. = 0.000), with an exceptionally high R Square of 0.955, indicating that 95.5% of performance variance is collectively explained by the two predictors. These findings underscore the critical role of digital marketing capabilities over financial resources alone in archipelagic contexts. It is recommended that the Ambon City Government integrate digital marketing literacy training programs synergistically with inclusive financing schemes to comprehensively strengthen MSME competitiveness across the Maluku archipelago.

The rapid development of the digital ecosystem has encouraged Micro, Small, and Medium Enterprises (MSMEs) in Ambon City to adopt online platform-based marketing strategies in response to changing consumer behavior that is increasingly digitally connected. This research focuses on MSME consumers in Ambon City who actively interact with local business products through digital platforms. The main problems identified include low digital marketing adoption by local MSME actors, limited empirical studies on Eastern Indonesia context, and the absence of an integrative analytical model that simultaneously tests three dimensions of digital marketing. This research aims to analyze the influence of digital marketing strategies encompassing social media marketing, paid digital advertising, and content marketing on consumer purchasing decisions of MSMEs in Ambon City. This study employs a quantitative approach with a survey design involving 100 respondents selected through purposive sampling, and data were analyzed using multiple regression analysis after passing classical assumption tests covering normality, multicollinearity, and heteroscedasticity. Results show that social media marketing has a significant effect with a regression coefficient of 0.387, paid digital advertising with a coefficient of 0.312, and content marketing with a coefficient of 0.274, all significant at the 0.05 level. Simultaneously, the three variables explain 67.1% of variation in consumer purchasing decisions with an F-count of 65.847. Social media marketing is proven as the most dominant dimension shaping consumer purchasing decisions of MSMEs in Ambon City. This research concludes that an integrated and contextual digital marketing strategy is a crucial instrument in driving MSME growth in Eastern Indonesia and recommends strengthening digital capacity of local business actors as a priority policy for regional MSME empowerment.Keywords: digital marketing; purchasing decision; MSMEs; social media; Ambon City

This study examines the impact of MBKM, campus culture, and learning achievement on work readiness in Islamic financial institutions, specifically for students from the Faculty of Islamic Economics and Business at UIN Datokarama Palu, Class of 2022. Using a quantitative approach with descriptive research design, the study aims to analyze how these factors influence students' preparedness for work in the industry. The findings show that MBKM, campus culture, and learning achievement each have a positive and significant effect on work readiness. MBKM, with its emphasis on direct industrial experience, has the strongest impact, while campus culture shapes students' character and work ethics. Learning achievement reflects academic competencies essential for entering the workforce. The combined influence of these three factors was proven to significantly impact work readiness, with an F-value of 5.393, greater than the F-table value of 2.72. This demonstrates that work readiness stems from a combination of practical experience, acceptance of campus values, and academic success, all of which should be developed continuously.

English is an international language that plays a vital role in education and technology; therefore, students are required to master reading comprehension skills, especially narrative texts, to face the challenges of globalization. This study, entitled "Improving Students' Reading Comprehension of Narrative Texts Through Storytelling Activities Using Webtoons in Grade I of MA Daruttholibin Watumalang," aims to improve students' reading comprehension of narrative texts through storytelling activities using Webtoons as a learning medium. This study employed a Classroom Action Research (CAR) design conducted in two cycles, each consisting of planning, action, observation, and reflection. Data were collected using descriptive qualitative methods through interviews, tests, and classroom observations. The research procedure began with a pre-test, followed by the implementation of treatment and post-tests in each cycle. The findings indicate that the use of storytelling supported by Webtoons effectively improved students' reading comprehension. This improvement was reflected in students' increased learning motivation, better understanding of narrative text elements, and improved ability to accurately retell stories.

The digital era demands a fundamental transformation in the role of early childhood educators, shifting from passive technology consumers to active architects of digital literacy. However, the dominance of technocentric views often acts as a substantial psychological and pedagogical barrier for teachers in regional areas. This collaborative community service project aims to reconstruct the paradigm of 50 kindergarten teachers in Bulukumba Regency by integrating "unplugged coding" logic and deep learning into play-based learning. Utilizing a Product-Based Intensive Training method with a "Logic over Laptop" strategy, the program focused on deconstructing technology-related stigmas and reconstructing teachers' ability to transform abstract concepts into safe, concrete media for children. Data analysis revealed a significant shift in teacher paradigms; while the majority were initially in the "less successful" category, 100% of participants reached positive categories (successful and very successful) post-intervention. Statistically, the program's effectiveness was evidenced by a dramatic increase in mean scores from 18.04 to 31.24 (p < 0.05) and an N-Gain score of 0.778, classified as highly effective. Furthermore, the partner satisfaction index reached 4.82 (very satisfied), confirming that the tri-campus collaboration model (STAI Al-Gazali, UNM, and Unismuh) is highly relevant to the implementation of the Merdeka Belajar curriculum. This project concludes that strengthening digital literacy through non-digital algorithmic reasoning effectively dismantles technical barriers for teachers while ensuring the safety of child development in the digital age.

The issue of uninhabitable houses still requires an accurate identification mechanism because the manual data collection process has the potential to be time-consuming, costly, and subject to subjectivity in determining aid priorities. This study aims to develop a classification model to identify habitable and uninhabitable houses based on family socioeconomic data using the Random Forest algorithm. The research method includes data preprocessing, data division using stratified split in three scenarios, baseline model development, and optimization through hyperparameter tuning using GridSearchCV with 3-fold cross-validation and balanced class_weight parameters. The data used includes variables such as education type, employment status, occupation type, number of family members, and family insurance type. The test results show that the 70:30 data division scenario after tuning provides the best performance with a recall value of 0.5797 for uninhabitable houses and an F1-score of 0.4746. Feature importance analysis shows that education type and employment status are the most influential variables in the classification. The results of this study show that the model built is capable of increasing sensitivity in detecting uninhabitable houses to support more objective field survey prioritization.

Vocabulary acquisition is a fundamental component of English language learning because it enables students to comprehend texts and develop broader communication skills. In elementary school contexts, however, vocabulary instruction is frequently delivered through traditional teaching practices that are not closely related to students’ real-life experiences. As a result, students often face difficulties in understanding and retaining new vocabulary. One potential approach to address this issue is the integration of local wisdom into learning activities so that the materials become more contextual and meaningful for learners. Therefore, this study aims to examine how the incorporation of local cultural elements can support vocabulary development among elementary school students. This research employed a descriptive qualitative design using a library research method. The data were gathered from various academic sources, including national and international journal articles, scholarly books, and conference proceedings that discuss English language learning, vocabulary development, and the integration of local wisdom in educational practices. The collected data were analyzed through thematic analysis by identifying, comparing, and synthesizing relevant findings from previous studies. The results indicate that the use of local cultural content, such as folklore, traditional practices, and community-based contexts, can significantly support students in understanding and remembering new vocabulary items. Learning materials that reflect students’ cultural backgrounds also contribute to higher levels of learning motivation and classroom participation. In conclusion, integrating local wisdom into English learning can create a more meaningful instructional environment and effectively support vocabulary mastery among elementary school students.

The increasing sophistication of malware has rendered traditional signature-based detection methods insufficient, necessitating behavior-driven and adaptive analytical frameworks. This study presents a sequential deep learning framework that models system-level API call sequences as structured linguistic representations for behavioral malware detection. Unlike conventional comparative studies, this work systematically evaluates recurrent and attention-based architectures under controlled experimental conditions, with a particular focus on generalization performance and overfitting mitigation. Two neural architectures, a Long Short-Term Memory (LSTM) network and a Transformer-based attention model, are trained on publicly available API call sequence data for binary classification of malicious and benign executables. Beyond standard accuracy metrics, the study further examines model stability, convergence behavior, and the impact of long-range dependency modeling on detection robustness. Experimental results demonstrate that the Transformer architecture achieves superior performance, attaining 95.54% classification accuracy and consistent improvements in precision, recall, and F1-score, indicating a stronger ability to capture complex behavioral dependencies. These findings highlight the effectiveness of attention mechanisms in behavioral malware modeling and provide empirical evidence that NLP-inspired architectures offer a robust and scalable approach for real-world cybersecurity applications.

This study aims to examine the use of picture books as a visual literacy medium in teaching English at the elementary school level, particularly in the context of English as a Foreign Language (EFL). This research employed a qualitative descriptive literature review by analyzing relevant national and international journal articles published within the last five years. The findings indicate that the integration of visual and verbal elements in picture books contributes significantly to students’ language development, especially in vocabulary acquisition, reading comprehension, and meaning construction. From an affective perspective, the use of picture books enhances students’ motivation, learning interest, classroom participation, and reduces language learning anxiety. The effectiveness of visual media is theoretically supported by Dual Coding Theory, which explains that information processed through both verbal and visual channels strengthens memory retention and comprehension. However, several challenges remain, including the selection of appropriate picture books and teachers’ limited competence in visual literacy. Therefore, systematic instructional planning and professional development for teachers are essential to optimize the use of picture books in EFL classrooms at the elementary level.

Tegal possesses substantial capture fisheries potential, supported by the availability of adequate human resources and appropriate infrastructure. One of the most commonly utilized fishing gears at the Tegalsari Nusantara Fishing Port is the pocket seine net. This study was conducted to evaluate the environmental friendliness of pocket seine fishing gear operated by fishermen at the Tegalsari Nusantara Fishing Port and to analyze the composition of the catch produced by this gear. The research employed a survey method using a questionnaire instrument based on nine criteria for environmentally friendly fishing gear. The respondents in this study were fishermen operating pocket seine net, and the number of respondents was determined using the Slovin formula. Data were processed using descriptive analysis. Meanwhile, the catch composition data were analyzed using the comparative method. The results of the fishing gear friendliness assessment indicate that the pocket seine gear used by fishermen at the Tegalsari Nusantara Fishing Port falls within the environmentally friendly category, with an indicator score ranging from 19 to 27. The catch composition of pocket seine gear comprises 12 species, which are classified into main catch and bycatch. All captured species possess economic value; consequently, no catch is discarded.

This is a worrying issue in the digital age and has been shown to significantly contribute to low student academic achievement. This research aims to examine the role of Guidance and Counseling (BK) services in addressing social media addiction in students with low academic achievement. The method used in this research is library research with a qualitative approach through content analysis and literature synthesis techniques sourced from various scientific references published between 2020 and 2025. The results show that students addicted to social media have low self-control, resulting in academic procrastination and a consistent decline in learning achievement. GC services have proven to play a strategic role through preventive, curative, and developmental functions implemented through classical guidance, individual counseling, and group counseling. The Cognitive Behavioral Therapy (CBT) approach and self-management technique have proven most effective in helping students reduce social media addiction and improve learning discipline. However, the effectiveness of GC services is still constrained by the non-ideal ratio of counseling teachers, the lack of digital literacy-based GC programs, and students' negative stigma toward counseling services. This study recommends developing GC programs specifically designed to address digital addiction by involving the entire school ecosystem consistently.

The number of students arriving late often disrupts the classroom atmosphere and disrupts adherence to school rules. This study aims to assess the effectiveness of the behavioral contract technique in changing this habit. Using a qualitative approach focused on literature review, this study found that internal factors within students and environmental factors are the main causes of their tardiness. Behavioral counseling can help change behavior through reinforcement and habit training, while the behavioral contract technique provides clear rules and agreements that make students more responsible. Behavioral changes occur gradually and are more effective if students are involved and there is a balance between rewards and punishments. Therefore, this technique is suitable for use as a practical way to train student discipline in the school environment.

The study of takhrij al-hadith plays a crucial role in hadith scholarship, involving the tracing of transmission sources and evaluating the authenticity of sanad and matn through systematic analysis. In contemporary studies, differences in scholars’ approaches have led to variations in hadith classification and epistemological orientation. However, comparative research examining methodological criticism, epistemological foundations, and operational procedures of modern takhrij remains limited. This study aims to critically compare the methodologies of Muḥammad Nāṣiruddīn al-Albānī and Muqbil bin Hādī al-Wādiʿī, focusing on their evaluation of sanad and matn. Using a qualitative approach with an integrative literature review, this research analyzes primary works and relevant academic sources. The findings reveal three main epistemological patterns: independent reasoning through cross-narration, methodological caution based on classical authority, and a shared reliance on jarḥ wa taʿdīl. These differences influence hadith classification and reflect varying depths of criticism. The study shows that contemporary hadith criticism forms an epistemological spectrum rather than a strict methodological divide. It also highlights implications for religious practices, including the Megengan tradition in Java, which adapts to socio-economic contexts, and enriches comparative studies in hadith methodology.

This study explored how integrating YouTube video analysis into pre-writing activities can support idea generation and coherence in students’ writing. Writing is often challenging for EFL learners, especially in developing ideas and organizing them logically. To address this, the study used YouTube as a pre-writing tool that provides visual and auditory input to stimulate creativity and critical thinking. This research employed a descriptive qualitative method using a YouTube video titled “Writing Process: Pre-Writing” from Mr. Paige’s English Writing Center and open-ended questionnaires answered by 30 English Literature students. The findings showed that 70% of students agreed that watching or analyzing YouTube videos before writing helped them generate ideas more easily, while 56.7% found the activities more engaging and 53.3% reported better organization of thoughts. Additionally, 50% of participants felt more confident to start writing, and 63.3% believed that teachers should integrate video analysis into writing classes. These results indicate that YouTube-based pre-writing activities enhance idea generation, coherence, and motivation in writing. Overall, YouTube video analysis proves to be an effective digital learning strategy that combines creativity, engagement, and logical organization in the writing process.