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This paper analyzes the legal disharmony between the Indonesian Standard Industrial Classification (KBLI) Code 92000, which classifies gambling as a business activity, and Article 303 of the Indonesian Penal Code (KUHP), which criminalizes gambling. Using a normative legal research method supported by theoretical foundations from legal certainty, legal harmonization, and sociological jurisprudence, the paper explores the philosophical, sociological, and juridical ratio legis behind this classification. The research concludes that the classification under KBLI is administrative and does not legitimize gambling activities. The paper suggests harmonization mechanisms to resolve legal contradictions and ensure regulatory coherence.  Address the normative conflict and avoid further interpretive ambiguities, this paper proposes a series of harmonization mechanisms. First, there should be a revision or annotation of KBLI Code 92000 to clarify that its inclusion of gambling is not a recognition of its legality under Indonesian law. Second, greater inter-agency coordination is necessary, especially between the institutions responsible for economic classifications and those enforcing criminal law. Third, legislative synchronization efforts must be enhanced through the establishment of an integrated legal drafting mechanism to ensure that new or revised regulations do not conflict with existing criminal statutes.

This thesis discusses the implementation of the mukhabarah and muzara'ah system to improve the welfare of rice farmers in Benteng Bawah village, Mersam sub-district, Batanghari district, Jambi province. The main issues raised are how to implement the mukhabarah system for rice farming, how to implement the muzara'ah system for rice farming in Benteng Bawah Village, what is the impact of implementing the mukhabarah and muzara'ah system for agricuture on the economic welfare of rice farmers in Benteng Bawah Village.This type of research includes qualitative research, to obtain valid data the researcher uses data collection methods, namely observation, interviews and documentation. The data sources in this research are primary and secondary. The data analysis technique used is crosswel. Available data, which is usually in the form of a telephone call, is analyzed. This part of the analysis usually involves data classification. The resuts of research on the implementation of the mukhabarah and muzara'ah systems to improve the welfare of rice farmers in Benteng Bawah village, Mersam sub-district, Batanghari district, Jambi province, namely, in the mukhabarah system the harvest is divided into 3 parts, where 2 parts are for sharecroppers and 1 part is for land owners, because all costs and seeds are paid by the land cutivator, whereas in the muzara'ah system the harvest is divided into two or equally because the seeds and costs are paid by the land owner. These two systems have an impact on the welfare of land owners and sharecroppers, enough to meet food needs until the next harvest.

Given the increasing prevalence of mental health challenges in digital work settings, especially among IT remote workers, early detection mechanisms have become critically important. This study aims to improve the prediction accuracy of mental health conditions among IT remote workers by integrating feature engineering techniques within machine learning models. Five algorithms consisting of Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes were evaluated. The Random Forest model achieved the best performance, with 83% accuracy, 83% precision, 100% recall, and a 90% F1-score, followed closely by Logistic Regression with 82% accuracy. Nevertheless, the results demonstrate the feasibility of applying machine learning to support the early detection of mental health risks, offering a strong foundation for future research in predictive analytics and the development of intelligent support systems within digital work environments.

This research investigates the implementation of Random Forest algorithms combined with Synthetic Minority Over-sampling Technique (SMOTE) to predict elementary school dropout rates in Indonesia, supporting the Indonesia Emas 2045 vision. A significant gap was identified in previous studies, which, despite utilizing artificial intelligence for dropout interventions, had not integrated temporal dimensions into data analysis. A temporal data-based classification model was developed using Indonesian Ministry of Education data from 2021-2023, incorporating lag features, delta calculations, and rolling statistics. Two models were implemented: one with SMOTE achieving 99% accuracy with perfect recall for high-risk regions, while the non-SMOTE model reached 100% accuracy. Temporal features were identified as crucial predictors, reflecting external fluctuations and annual changes impacting dropout decisions. This approach enables educational institutions to allocate resources more efficiently by prioritizing operational assistance for high-risk schools. The model's capacity to identify high-risk regions with 100% recall represents a strategic investment in strengthening Indonesia's human resource sustainability. To address the limitations of provincial aggregate data, expansion to include individual-level variables and model validation at district or school scales is recommended for future research.

Learning strategies play a vital role in determining the success of the educational process, particularly in achieving learning objectives effectively and efficiently. This article aims to comprehensively examine fundamental concepts of learning strategies by addressing five main research questions: (1) what is the definition of learning strategies? (2) how are models, approaches, strategies, methods, and techniques of learning interrelated? (3) what are the classifications of learning strategies? (4) what are the essential components of a learning strategy? and (5) what considerations are important in selecting an appropriate learning strategy? Using a descriptive-qualitative approach, this article draws upon various educational literature, both classical and contemporary, to formulate a comprehensive understanding of learning strategies. The discussion on the definition highlights that a strategy is not merely a technical step but also a pedagogical framework that reflects a systematic way of thinking. Furthermore, this article emphasizes the importance of understanding the interconnectedness of learning models (conceptual frameworks), approaches (philosophical orientations), strategies (overall plans), methods (procedural steps), and techniques (practical actions) in order for educators to design cohesive and holistic learning processes. The classification of learning strategies is discussed based on the learning approach used, such as expository, inquiry-based, cooperative, contextual, and problem-based strategies—each with its own advantages and challenges. The article also identifies key components of learning strategies, including learning objectives, student characteristics, materials, media, methods, and evaluation. In choosing the appropriate strategy, the author highlights the need to consider factors such as learning goals, learner characteristics, available resources, and the socio-cultural context of the learning environment, particularly within the framework of Islamic education. The findings of this study are expected to contribute both theoretically and practically to the development of more contextual, adaptive, and relevant learning designs, especially for educators and prospective teachers within Islamic State Universities.    

the importance of children's literature in supporting the development of literacy, character, and imagination in the digital era. Children's literature has unique characteristics and must be adapted to the world and stage of psychological development of children. As prospective elementary school educators, understanding the theory and genre of children's literature is important in selecting and delivering appropriate reading materials. This study examines the theory of children's literature according to experts, the classification and characteristics of various genres of children's literature such as fairy tales, fables, myths and legends, children's poetry, picture stories, children's novels, fantasy, and realistic fiction. Each genre is discussed by prioritizing the educational and moral functions contained, and is equipped with examples of relevant children's literature. With this approach, this paper aims to provide prospective teachers with a comprehensive understanding of the use of children's literature in an interesting and meaningful learning process.    

the importance of children's literature in supporting the development of literacy, character, and imagination in the digital era. Children's literature has unique characteristics and must be adapted to the world and stage of psychological development of children. As prospective elementary school educators, understanding the theory and genre of children's literature is important in selecting and delivering appropriate reading materials. This study examines the theory of children's literature according to experts, the classification and characteristics of various genres of children's literature such as fairy tales, fables, myths and legends, children's poetry, picture stories, children's novels, fantasy, and realistic fiction. Each genre is discussed by prioritizing the educational and moral functions contained, and is equipped with examples of relevant children's literature. With this approach, this paper aims to provide prospective teachers with a comprehensive understanding of the use of children's literature in an interesting and meaningful learning process.    

This study aims to analyze the legal implications of the luxury goods Value Added Tax (VAT) increase policy in Indonesia, which will take effect in January 2025. This policy is based on Law Number 7 of 2021 on the Harmonization of Tax Regulations and its derivative regulations, with the objective of increasing state revenue and reducing the consumption of luxury goods, which is often associated with economic inequality. This research employs a normative method with a literature study approach, analyzing tax regulations, academic journals, and relevant literature. The findings indicate that while this policy may enhance tax revenue and reduce economic disparity, its implementation faces challenges related to legal certainty, tax compliance, and its impact on consumer purchasing power and investment in the luxury goods sector. Additionally, the potential rise in tax disputes due to differing interpretations of luxury goods classification is a major concern. Therefore, clear regulations, a more transparent tax administration mechanism, and economic impact mitigation strategies are necessary to ensure the effectiveness and fairness of this policy implementation.This study aims to analyze the legal implications of the luxury goods Value Added Tax (VAT) increase policy in Indonesia, which will take effect in January 2025. This policy is based on Law Number 7 of 2021 on the Harmonization of Tax Regulations and its derivative regulations, with the objective of increasing state revenue and reducing the consumption of luxury goods, which is often associated with economic inequality. This research employs a normative method with a literature study approach, analyzing tax regulations, academic journals, and relevant literature. The findings indicate that while this policy may enhance tax revenue and reduce economic disparity, its implementation faces challenges related to legal certainty, tax compliance, and its impact on consumer purchasing power and investment in the luxury goods sector. Additionally, the potential rise in tax disputes due to differing interpretations of luxury goods classification is a major concern. Therefore, clear regulations, a more transparent tax administration mechanism, and economic impact mitigation strategies are necessary to ensure the effectiveness and fairness of this policy implementation.

Learning strategies are very important to be applied in preparation for the implementation of learning in its implementation can be attributed to local wisdom which is a concept that refers to the culture of the surrounding community. This study aims to analyze learning strategies based on local wisdom and its integration with the improvement of narrative writing skills in Indonesian language learning over the last 10 years which have been published in SINTA-indexed journals. This research is a qualitative research using a literature review of 20 articles. The writing of this article is assisted by several applications such as publish or perish and mendeley. Learning strategies can be associated with local wisdom that is adapted to the culture in the local environment. The results of the literature review are in the form of a classification of learning strategies in improving the ability to write narratives based on local wisdom. The results of the analysis show that local wisdom can be integrated into learning models, learning media, and the development of teaching materials. Based on the research, the application of learning strategies based on local wisdom can improve the ability to write narrative texts and can train critical thinking, creativity and cultural literacy.

Learning strategies are very important to be applied in preparation for the implementation of learning in its implementation can be attributed to local wisdom which is a concept that refers to the culture of the surrounding community. This study aims to analyze learning strategies based on local wisdom and its integration with the improvement of narrative writing skills in Indonesian language learning over the last 10 years which have been published in SINTA-indexed journals. This research is a qualitative research using a literature review of 20 articles. The writing of this article is assisted by several applications such as publish or perish and mendeley. Learning strategies can be associated with local wisdom that is adapted to the culture in the local environment. The results of the literature review are in the form of a classification of learning strategies in improving the ability to write narratives based on local wisdom. The results of the analysis show that local wisdom can be integrated into learning models, learning media, and the development of teaching materials. Based on the research, the application of learning strategies based on local wisdom can improve the ability to write narrative texts and can train critical thinking, creativity and cultural literacy.

The research paper Examining the leadership of the Office of the Accountant General of the Federation (OAGF), it is evident that the country is in trouble and need a leader capable of bringing out the best in Nigerian supporters and guiding the country toward stability. The next logical step after gathering data is research analysis. Both the main data gathered from the in-person interviews and the data already available from public sources spanning the 50-year study period were analyzed using the constant comparative approach (Merriam, 2019). This clarified the qualitative case study technique utilized in the research design, which examined the influence of leadership on the governance of infrastructure development in Nigeria between 1960 and 2020. This leader needs to be dependable, emotionally knowledgeable, firm, prepared to endure hardships for the country, committed to bridging ethnic divides, and able to instill hope in the populace. leader must be able to process a variety of information and find effective solutions to challenging issues. This essay embraces the idea that leadership entails a leader or leaders, followers, and a social influence process. This viewpoint is influenced by clinical psychology socio-emotional intelligence and social psychology concepts of social influence; as a result, leadership for Nigeria will be examined from these angles.

This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the system progressively shifts to a collaborative filtering mechanism, leveraging cosine similarity to identify similar users within comparable contexts. The recommender system focuses on three categories of POIs: restaurants, hotels, and landmarks. These locations are retrieved through the Google Maps API, and only mapped locations are considered. The system was implemented on Android devices and evaluated through a user study involving 25 participants from diverse backgrounds, including software developers, IT students, and general users. Evaluation metrics such as normalized Discounted Cumulative Gain (nDCG) and classification accuracy were used to assess recommendation quality. Results demonstrate that the system performs better than traditional methods, with nDCG improvements reaching up to 83 percent. Users reported high satisfaction regarding the recommendations' accuracy, ease of use, and contextual relevance. While the system offers significant improvements, it also has certain limitations. Its dependency on Google Maps data may restrict its scope, and using only four contextual factors limits the system’s adaptability to more complex user preferences. Future enhancements could include additional dynamic contexts such as weather, POI popularity, and time-related trends, as well as integrating more advanced models to increase personalization and flexibility in real-world applications.

The online gaming industry continues to grow rapidly in Indonesia, with many users purchasing digital items through 3rd party top up services such as Pitopup.com. One of the main challenges faced by Pitopup.com is the difficulty in classifying the sales of each available game item. This research aims to apply the K-Nearest Neighbor (KNN) method to predict the sales classification of game items in order to find out the sales category for each game item and hopefully help increase stock efficiency. The dataset used was obtained from historical sales data on Pitopup.com from June to September 2024. The research stages include data processing, normalization using Min-Max Scaling, data transformation using label encoding, separating test and training data using a ratio of 80:20, and using confusion matrix as a model evaluation. The test results show that KNN algorithm is able to classify game item sales on the Pitopup.com website with a level of accuracy in several categories: marketable category at 100%, the moderately sellable category at 100% and the not sellable category at 100%.

Illegal trafficking of protected wildlife is an activity that is a threat to the survival of wildlife, Illegal trafficking categorized as a crime against wildlife includes: intentionally capturing, storing, possessing, maintaining, transporting and trading protected animals in a living condition. The problem in this thesis is what is the basis for the judge's legal considerations in passing a verdict against the perpetrator of the crime of trafficking in protected wildlife of the Siamang species in Decision Number: 134 / Pid.B / LH / 2023 / Pn Kla? is the judge's decision in imposing the sentence in accordance with substantive justice?, The research method used is normative and empirical juridical, The data used is primary and secondary data, data collection with literature studies and interviews. While data processing through the stages of data examination, data selection, data classification, and data systematization. The data that has been presented in the form of a description, discussed and analyzed descriptively qualitatively, to then draw conclusions. Based on the results of the study, it is known that the consideration of the Judge in Decision Number: 134 / Pid.B / LH / 2023 / Pn Kla in deciding the criminal case of trade in protected wildlife species of siamang, the Judge in Decision Number: 134 / Pid.B / LH / 2023 / Pn Kla related to the criminal act of trade in siamang species of wildlife considered the legal, sociological, and philosophical aspects according to Ahmad Rifai's theory. The legal aspect includes the sufficiency of evidence and the fulfillment of the elements of Article 40 Paragraph 2 in conjunction with Article 21 Paragraph 2 of Law No. 5 of 1990 concerning the Conservation of Natural Resources and Ecosystems. Sociologically, the judge sees the impact of the defendant's actions on society and the environment. From a philosophical perspective, punishment is seen not as revenge, but an effort to educate the defendant not to repeat his actions. Finally, the judge sentenced him to 1 year and 4 months in prison and a fine of Rp25,000,000, subsidiary to 1 month in prison if the fine is not paid. The suggestion is that the government, law enforcement and stakeholders are expected to increase education to the community, especially around national parks/protected forests, not to trade in protected animals. This is important to prevent similar crimes and maintain the existence of protected animals in their habitat.    

Hypertension is a disease that often causes death and complications related to cardiovascular disease. The prevalence of hypertension in Indonesia is 34.11%. One of the factors that influence the incidence of hypertension is high levels of the triglycerides in the blood (hypertriglycerida). This study aims to determine the description between triglycerides levels and the incidence of hypertension in the community of Bandar Buat Village. This research was a descriptive study with 29 respondents taken using total sampling techniques from secondary data of community service in the village of Bandar Buat in 2019. Data were selected based on inclusion criteria and exclusion criteria, the data were processed in table to generate frequency and percentages distribution. The results showed that the majority of respondents aged 18-50 years (55.1%), were women (58.6%). The results showed that respondents' triglycerides levels were dominated by hypertriglycerida (58.6%) and blood pressure classification was dominated by normotension (55.2%). Based on age, most people with hypertriglycerida and hypertension are above 50 years old. Based on sex, hypertriglycerida and hypertension both are sufferers more frequent in men. The number of percentage were 66.7%; 75% respectively. The conclusion of this research is the triglycerides level of respondents is dominated by respondents with hypertriglycerida, the majority are aged>50 years, male sex. The incidence of hypertension is high, with majority sufferers aged>50 years, male sex.

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.

This study examines the intersection of sentiment discourse in news texts and user comments on Facebook Detikcom, focusing on government policies during the Covid-19 pandemic. The research explores institutional discourse in news articles that align with government narratives, while user comments reflect a spectrum of responses, from support to opposition. Using qualitative content analysis, this study applies critical discourse analysis (CDA) and sentiment analysis to examine linguistic strategies, ideological framing, and sentiment polarity. The data were taken from news texts and the comment section on Facebook Detikcom, collected through documentation and following several stages of observation, careful reading, selection, and classification. The findings show that social media transforms news consumption into a participatory discourse. This indicates that traditional narratives are challenged and reinterpreted by users. Sentiment clustering and engagement metrics further shape the visibility and influence of competing ideologies. This study contributes to digital discourse research by demonstrating that sentiment functions as an ideological tool in crisis communication. The analysis also highlights the evolving role of social media in public discourse and emphasizes the need for critical engagement with online news narratives and user-generated content.

Health risk stratification is crucial for preventive healthcare, yet existing models often rely on binary classification generalized disease prediction, neglecting personalized health indicators and graded risk levels. Many studies apply feature selection techniques like Relief and Univariate Selection without quantifying the weighted impact of features. To address these gaps, this study introduces a Big Data-driven Health Index (HI) framework using PySpark for scalable health risk stratification. The HI is computed as a weighted sum of health-related features using SHAP Analysis, XGBoost, Random Forest, and Correlation Analysis. PySpark enables efficient processing of large-scale health data, and individuals are classified into Low and High Risk. Optimal classification thresholds are determined using the Youden Index from the ROC curve to balance sensitivity and specificity. Personalized health recommendations are generated based on risk categories to guide preventive interventions. Performance evaluation reveals that Correlation Analysis achieves 100% precision and 98.90% recall, outperforming other methods. SHAP prioritizes recall but has low precision, while XGBoost and Random Forest improve precision but struggle with recall. By leveraging Big Data techniques with PySpark, this study enhances computational efficiency, scalability, and classification accuracy, addressing prior research limitations and providing a robust data-driven approach to personalized health monitoring.

Communication is the process of exchanging information, ideas, thoughts, and feelings between individuals or groups through the use of words, signs, or actions. This process can take place verbally or non-verbally and involves various media and channels, such as face-to-face conversations, writing, gestures, facial expressions, and digital technology. This research was conducted at STMIK Kaputama Binjai, namely the WhatsApp group between lecturers and students. This study uses the Support Vector Machine (SVM) method. SVM is a type of supervised learning machine learning that requires sample data. Support Vector Machine (SVM) is an algorithm developed by Boser, Guyon, and Vapnik in 1992. Support Vector Machine (SVM) has a concept that is combined with previous computational theories. This method can transform training data into higher dimensions using non-linear patterns. The results of the Support Vector Machine method classification with a total of 16 positive sentiments, 40 neutral sentiments and 71 negative sentiments. Accuracy value 67%, margin error 39%. Positive prediction precision 75%, neutral prediction precision 83% and negative prediction precision 88%..

This study examines the education of Ahlus Sunnah Wal Jamaah theology, focusing on its concept, classification, and implementation in Muslim life. Using a literature review method, this research explores primary Islamic sources, such as classical theological books, along with modern academic references that explain the 20 Attributes (Sifat 20) in Ahlus Sunnah Wal Jamaah doctrine. The analysis is conducted descriptively and critically to understand how this theology is taught and applied in various aspects of life. The findings reveal that theological education plays a crucial role in shaping Muslim beliefs and character while maintaining relevance in the Islamic education system. This study also recommends more effective teaching strategies for comprehending and internalizing Ahlus Sunnah Wal Jamaah theology.