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This study aims to analyze and implement the K-Nearest Neighbor (KNN) algorithm in determining the feasibility of sheep selection at PT Arjuna Farm. In the livestock industry, selecting quality animals is very important to increase productivity and efficiency. KNN, as a simple but effective classification method, is used to analyze sheep characteristic data, such as body weight, age, and health, to identify animals that meet the eligibility criteria. The research methodology used in this study includes data collection, pre-processing, and application of the KNN algorithm. Data obtained from PT Arjuna Farm were processed and divided into training data and testing data. The results of the analysis show that the KNN algorithm is able to provide high accuracy in determining the feasibility of sheep selection, with a low error rate.

This study aimed to evaluate the implementation of green accounting at Polrestabes Palembang and its impact on sustainable environmental management. A descriptive qualitative approach was employed, utilizing observation, interviews, and documentation for data collection. The findings revealed that green accounting practices at Polrestabes Palembang were not yet fully integrated into the institution’s financial accounting system. Environmental costs were still recorded under general operational expenses without specific classifications, and there was no systematic measurement or disclosure in accordance with environmental accounting principles. However, several positive initiatives existed, such as energy efficiency, greening efforts, waste management, and food security programs. The study recommends strengthening environmental cost recording systems, integrating data across departments, and providing training to enhance understanding of green accounting. Effective implementation of green accounting is expected to improve transparency, accountability, and institutional legitimacy in supporting sustainable development.

This study aims to determine the physical fitness profile of students at SMP Negeri 1 Eris Tondano. This research employed a descriptive method with a survey technique involving 62 students, consisting of 38 males and 24 females. The data collection instrument used was a 12-minute run test based on the Cooper protocol. The results showed that the average level of physical fitness was classified as “bad” with an overall mean distance of 1606.47 meters. Male students had an average of 1900.84 meters, while female students averaged 1140.38 meters. According to Cooper’s classification, 73% of the students fell into the “bad” category and 27% were in the “reasonable” category. The conclusion of this study is that the physical fitness level of students remains low and requires more structured attention and guidance through physical education programs.  

In Central Aceh Regency, many households still live in uninhabitable conditions. The government is running a program to rehabilitate habitable houses, but the selection of recipients is still done manually, causing inefficiency and inconsistency. This study implements the Extreme Gradient Boosting (XGBoost) algorithm to classify aid recipients automatically and accurately. Using a machine learning approach, data is collected based on variables of structural conditions, building materials, ventilation, lighting, and sanitation. Hyperparameter tuning is performed to optimize model performance. The implementation results show that XGBoost is able to support fair, efficient, and transparent decision making in housing assistance programs.

Conceptual understanding is an important aspect of learning biology because it reflects the extent to which students can internalize knowledge and relate it to real life. This study aims to analyse the level of understanding of the concept of biodiversity of students and identify supporting and inhibiting factors in the biology learning process. The research was conducted in class X of SMA Negeri 1 Pineleng with a descriptive qualitative approach. Data was collected through observation, written tests, questionnaires, teacher interviews, and documentation. Conceptual comprehension is measured using three main indicators: the ability to restate concepts, classify objects based on certain properties, and provide examples rather than definitions. The results showed that 87.5% of students had achieved a good level of understanding of classification indicators and giving examples, but only 50% were able to restate concepts completely. The supporting factors identified include learning motivation, parental mentoring, teacher learning strategies, and student absorption. On the other hand, barriers to understanding include lack of enthusiasm for learning, an unsupportive environment, and a lack of parental involvement. These findings confirm that the success of conceptual understanding is determined not only by cognitive aspects but also by comprehensive pedagogical and social support. This research makes an important contribution to the development of more contextual and participatory biology learning strategies, as well as opens opportunities for further research that explores the use of innovative media in strengthening conceptual understanding.

Low motivation to learn and limited conceptual understanding are significant challenges in learning biology at the junior high school level, especially in abstract material on the classification of living things. This study aims to analyze the influence of the use of animated videos on student learning motivation and learning outcomes. A quantitative approach with a pre-experimental design of one group pre-test and post-test was used in this study. The research sample consisted of 25 students in grade VIII of SMP Negeri 2 East Likupang. Data was collected through observation sheets to assess learning motivation and pre-test and post-test tests to evaluate learning outcomes. The results of the observation showed that the average motivation of students reached 86.32% in the outstanding category. The average pre-test score of 59.57 increased to 81.09 on the post-test, indicating a significant improvement in concept comprehension. The t-test corroborates these findings with a significant value at a 95% confidence level. These results show that animated videos can present a visualization of complex concepts in an engaging and contextual way, thereby increasing students' focus, active participation, and absorption of the material. In addition, the use of this media also supports the development of students' collaborative skills and critical thinking. This research confirms that animated videos are a strategic alternative in effective science learning, especially in school environments with limitations of conventional visual media. It is suggested that the application of this media be expanded to various materials and levels of education.

The performance of data mining techniques has been proven accurate in many studies, but each method in data mining techniques has different accuracy depending on the type of data that is the object of research. Methods in data mining techniques are divided into several functions, namely: clustering, association, classification, and prediction, where each data mining technique objective has a superior method. Therefore, in this case the author will compare the performance of the multiple linear regression method, and neural networks with fuzzy mamdani in predicting the income of PLN Unit Takengon. In several studies, the Backpropagation method shows the highest accuracy compared to other methods. Then the prediction model with multiple linear regression also has the highest accuracy as well as the Fuzzy Mamdani method has high accuracy too. Therefore, the purpose of this study is to compare the three methods, so that it can be determined which method has a higher accuracy value. The results of this study indicate that the Back propagation method has the highest accuracy and the lowest average error, namely a MAPE value of 5.9% with an accuracy of 94.1% and an RMSE of 14398.14, followed by the multiple linear regression method obtaining a MAPE value of 6.9% with an accuracy of 93.1% and an RMSE of 15527.41, then for Fuzzy Mamdani obtaining a MAPE value of 7% with an accuracy of 93% and an RMSE of 16077.76.

Indonesian language has an important role in education, especially in shaping students’ literacy skills through text-based learning. One type of text taught in the Merdeka Curriculum is anecdote text. This study aims to identify and describe the forms of syntactic errors in anecdote texts in the book Cerdas Cergas Berbahasa dan Bersastra Indonesia for SMA / SMK class X. The method used is descriptive qualitative with error analysis approach according to Ellis’ model. The data were collected through listening and note-taking techniques, and analyzed through the stages of identification, classification, and evaluation of errors. The results of the analysis show the existence of various errors, including the use of non-standard words, incorrect word writing, inappropriate diction, excessive word usage, conjunction errors, and punctuation errors. The findings emphasize the importance of improving the quality of language in textbooks, as errors in books can affect students' language skills. Theoretically, this study expands the study of syntactic errors in the narrative text genre. Practically, the results of this study can be a reference for teachers, book writers, and curriculum developers in improving and enhancing the quality of Indonesian teaching materials.

In the digital era like today, traditional games such as forts and gobak sodor are increasingly marginalized, even though these two games have great potential in supporting the motor and social development of elementary school children. This literature study aims to analyze the role of these two games in honing students' gross motor skills through running, jumping, and coordination activities, as well as social skills such as cooperation, communication, and sportsmanship. This research method uses a literature study with several stages, namely the data collection stage, the classification stage, the analysis stage, the synthesis stage, and the interpretation stage of relevant sources. The results of the study indicate that forts and gobak sodor are effective in training children's gross motor skills through dynamic movements such as running and dodging, while developing children's social values ​​through team interaction and collaborative strategies. Integration of traditional games in Physical Education learning in elementary schools can be a holistic strategy to improve students' physical and character development. The suggestion for educators is to modify these games so that they remain relevant to the current context without eliminating their educational value.

Prison, is an institution whose existence is inseparable from law enforcement in Indonesia. Unlike prisons, Rutan functions as a temporary detention place for suspects or defendants who are undergoing the trial process.  Currently, almost all prisons and prisons in Indonesia are experiencing an overcrowding crisis  with an overcrowding rate. 92%. This condition is like a time bomb ready to explode. Extreme overcrowding in prisons and prisons has various negative impacts. Overcrowding in  correctional facilities and prisons is a serious problem that must be overcome immediately. The impact is not only on the health and safety of residents, but also on the effectiveness of coaching and the state budget. This study aims to find out and discuss the impact  of overcrowding on class IIB state prison inmates in Ruteng Regency. This research is an empirical legal research or empirical juridical research supported  by a statuecause  approach using primary data and secondary data collected using observation, documentation and interview techniques conducted with 33 informants. The data obtained was processed using editing techniques, data classification, data verification and description after which it was analyzed in a qualitative descriptive manner. The results of the study showed that (1) Overcrowding in Ruteng Class IIB Prison was caused by several interrelated factors. First, the limited capacity of Ruteng Detention Center. Second, the crime rate has increased in three districts. Third, the penal policy is not yet effective. Fourth, the low legal awareness of the public also plays a role in the increase in crime rates. (2) Overcrowding in Ruteng Class IIB Prison has a significant impact on inmates, especially in fulfilling their rights including the right to inmate health, the right to security, and the right to rehabilitation. Social coaching and reintegration programs cannot run effectively due to limited resources and space.

The present study aims to identify and analyse idiomatic expressions in the lyrics of "Shake It Off" by Taylor Swift. Idioms are a form of non literal expression which often pose a challenge for English language learners, due to the fact that their literal meanings are not easily discernible. Utilising a descriptive qualitative approach, this research analyses idioms based on Makkai's (1972) classification, which includes six types: phrasal verb idioms, tournure idioms, irreversible binomial idioms, phrasal compound idioms, incorporating verb idioms, and pseudo idioms. The findings indicate that the composition is replete with idiomatic usage, reflecting emotional meanings and social messages implicitly. A total of fourteen idioms were identified, which serve to reinforce Taylor Swift's stylistic language and make the song an effective and contextual medium for the purposes of English language learning. It is anticipated that these findings will contribute to the development of language teaching, particularly with regard to the comprehension of idiomatic meaning within the context of popular culture.

This study aims to analyze market sentiment towards Gold Financing Products (PKE) in Islamic banking before and after the Trump Effect phenomenon using the text mining method. This technique involves extracting information from unstructured text data to then be visualized and analyzed using the Natural Language Processing (NLP) approach and a RoBERTa-based classification model. Data was collected through web scraping from the X application with the help of API and processed using Google Colab. From a total of 4,074 tweets analyzed, it was found that the majority of public sentiment was neutral (59%), followed by negative (24%) and positive (17%). This reflects the public's tendency to discuss informatively rather than emotionally, although there was a spike in negative sentiment in certain periods indicating sensitivity to global dynamics, especially the impact of the Trump Effect on gold prices. The resulting wordcloud reveals key topics such as gold prices, buying and selling activities, and institutions such as Pegadaian Syariah and BSI. Terms such as "sharia", "riba", and "principles" emphasize the importance of Islamic financial values ​​in public perception. The results of this study indicate that text mining-based sentiment analysis is effective in capturing the dynamics of public opinion in real-time and can be a strategic tool for Islamic financial institutions in responding to market changes.

The pharmaceutical industry produces solid, liquid, and gaseous waste containing active pharmaceutical ingredients that pose serious environmental risks. These wastes can disrupt ecosystems and accelerate antimicrobial resistance. This systematic literature review examines pharmaceutical waste concepts, classifications, characteristics, and relevant regulatory frameworks. It also addresses ecotoxicological effects on aquatic and terrestrial ecosystems, antibiotic contamination and resistance, water and soil pollution, treatment technologies, industrial policies, and best practices. Findings show that conventional wastewater treatment is largely ineffective at removing pharmaceutical residues, resulting in their presence in surface water, soil, and even drinking water. Compounds such as β-blockers, cytostatics, antibiotics, and hormones harm aquatic life by impairing reproduction and causing mutations. Antibiotic-laden industrial waste contributes to the emergence of resistant bacteria. Recommended treatment methods include biothermal processes, advanced oxidation (e.g., UV/H₂O₂, ozonation), adsorption (activated carbon), coagulation-flocculation, and controlled incineration. Stronger enforcement of hazardous waste regulations (e.g., Government Regulation No. 101/2014, Ministry of Environment and Forestry Regulation No. 56/2015) and adherence to WHO (2025) guidelines are essential. In conclusion, multisectoral collaboration (One Health), improved waste treatment capacity, and adoption of best practices are crucial to preventing pharmaceutical pollution and promoting environmental sustainability.

The problem in this study is the ability of the control object of students at Elementary School 14 Limo Kaum Batusangkar which is still relatively low, this is thought to be caused by many factors. The purpose of this study was to determine the profile of the control object ability of students at Elementary School 14 Limo Kaum Batusangkar. This type of research is descriptive which aims to reveal how the description of the basic movement skills of the control object in students at Elementary School 14 Limo Kaum Batusangkar during the research period from November to January 2025. The place of this research was at Elementary School 14 Limo Kaum Batusangkar. The population in this study were all students at Elementary School 14 Limo Kaum Batusangkar totaling 159 people. The sampling in this study used the Proportional Stratified Random Sampling technique, so that the sample in this study amounted to 32 people. The data collection technique was using the 2nd edition of the Test Of Gross Motor Development or TGMD-2. The data analysis technique used descriptive analysis techniques. Based on the research that the researcher has conducted, it can be concluded that the Control Object Ability Profile of Students Aged 7-9 Years at State Elementary School 14 Limo Kaum Batusangkar has an average of 16.19 or 31% who have object control abilities or are in the "less" classification.

Fiqh al-Lughah is a discipline that examines the philosophical and historical aspects of the Arabic language, including the factors influencing its development. One of the most rapidly growing branches of Arabic language studies is Nahwu (grammar), which plays a crucial role in understanding linguistic structures and rules. This study aims to analyze the relationship between Fiqh al-Lughah and the development of Nahwu and how a deeper understanding of Fiqh al-Lughah contributes to the analysis and application of Nahwu rules. This research employs a qualitative method with a descriptive-analytical approach based on classical and modern literature reviews. The findings indicate that Fiqh al-Lughah significantly influences the formation of Nahwu theories, particularly in the historical development of grammatical rules, word classification, and syntactic concepts in Arabic.

Digital learning applications like HeyJapan are increasingly popular. User reviews on platforms such as Google Play Store contain valuable information on user perceptions and experiences. To process this information systematically, this study employs a Natural Language Processing (NLP) approach to analyze sentiment toward the HeyJapan application. Data was collected using web scraping techniques with Python and the google play scraper library, resulting in 1,000 latest user reviews. The analysis included data collection, preprocessing, sentiment labeling using TextBlob, visualization, modeling with Logistic Regression, and evaluation. After preprocessing, 923 valid reviews were classified into three sentiment categories based on polarity which are positive, neutral, and negative. Results showed 71.4% of reviews positive, 26.1% neutral, and 2.5% negative. Visualizations in pie charts and word clouds provided an overview of user perceptions. Modeling with TF-IDF and Logistic Regression achieved 88% accuracy with the highest f1-score in the positive sentiment category. Evaluation indicates the model is fairly reliable in classifying sentiments, especially for positive and neutral categories, though negative sentiment classification needs improvement. This study shows the NLP approach can evaluate user perceptions of educational applications based on reviews and serve as a basis for improving foreign language learning app quality.

According to a UNESCO report, more than 80% of industrial wastewater containing hazardous substances is discharged without treatment. This research aims to understand the implementation of Permen LHK Number 6 of 2021 in the management of hazardous waste at PT Bina Guna Kimia and the role of the Semarang Regency Environmental Agency (DLH). The research uses a descriptive qualitative approach with empirical juridical methods. Data were obtained through observation, interviews, documentation, and literature studies, and validated by source triangulation and analyzed qualitatively. The results showed that PT Bina Guna Kimia has implemented Permen LHK No. 6 of 2021 well, although there are still challenges such as the lack of understanding of employees about waste classification. The B3 waste management system in this company is quite effective with strict procedures in identification, storage, transportation, recording through logbooks, and real-time monitoring through the SIMPEL application. The company also regularly conducts training and works with certified vendors. DLH Semarang Regency plays an active role through supervision, socialization, compliance audits, coaching, and education to businesses to increase environmental awareness and compliance. This study suggests that DLH and PT Bina Guna Kimia improve education and training programs, and encourage industry players to seek alternative funding for the implementation of environmentally friendly technology. In addition, collaboration between stakeholders needs to be improved to create effective and sustainable hazardous waste management.

 A brand is one of the most valuable intellectual assets in the business sector, serving not only as a product identity but also as a reflection of reputation and quality. However, the increasing phenomenon of brand infringement indicates the urgent need for stronger and more effective legal protection. Brand infringement brings various negative consequences, including loss of consumer trust, unfair competition, and significant financial losses for brand owners. This study aims to analyze brand infringement from a criminological perspective, identify causal factors, and evaluate the role of law in addressing such violations. The research employs normative legal methods through analysis of statutory regulations, legal and criminological theories, and a literature-based approach using secondary data obtained from books, journals, and legal documents. The findings reveal that criminology provides relevant insights through theories such as Rational Choice Theory, which explains that perpetrators calculate the risks and benefits before committing the violation, and Anomie Strain Theory, which highlights how the inability to achieve economic or social goals legally can drive individuals or groups to commit brand infringement. The classification of violations includes imitation, counterfeiting, misuse, and unlawful exploitation of brand rights. Several causal factors were identified, including weak legal supervision, limited enforcement, and the high consumer demand for cheaper counterfeit products. Furthermore, the study underlines the essential role of the criminal justice system in addressing brand infringement through the enforcement of criminal, civil, and administrative sanctions as regulated under Law Number 20 of 2016 on Trademarks and Geographical Indications. In conclusion, brand infringement is not merely a legal issue but also a criminological problem that requires comprehensive handling through effective law enforcement, stronger supervision, and increased public awareness to protect intellectual property and ensure fair business practices.

Handwritten digit recognition is one of the key challenges in the field of digital image processing and artificial intelligence, with significant potential in various applications such as automatic form input systems, handwritten data correction, and attendance systems based on handwriting. This study aims to develop a web-based information system capable of automatically recognizing handwritten digits using the K-Nearest Neighbors (KNN) classification method. The system is designed through several main stages, including image preprocessing (conversion to grayscale, thresholding, and image size normalization), feature extraction using the zoning technique, and classification using the KNN algorithm. This research utilizes the MNIST dataset, which contains thousands of handwritten digit images ranging from 0 to 9. The system is developed using the Google Colab platform, supported by Python libraries such as OpenCV, NumPy, and Scikit-learn. Test results show that the system can achieve an accuracy of over 90% at certain K values, indicating that the KNN method is quite effective and efficient in recognizing handwritten digit patterns. This system is expected to be applicable for various digitization needs of handwritten numbers in education, administration, and information technology sectors

The rapid development of financial technology (fintech), particularly digital wallet applications like OVO, has significantly transformed transaction patterns in society. However, issues such as server instability and unsatisfactory user experiences frequently emerge on social media platforms. This study aims to analyze user sentiments toward OVO on platform X (formerly Twitter) by comparing the performance of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Data were collected through web scraping from 1,000 Indonesian-language tweets containing the keyword "OVO." The research methodology included text preprocessing (data cleaning, tokenization, stopword removal), feature extraction using TF-IDF, and sentiment classification (positive, negative, neutral). Evaluation results demonstrated that SVM achieved the highest accuracy of 85.2%, while Naïve Bayes reached 78.5%. SVM also outperformed in precision (87%) and recall (83%) due to its ability to handle non-linear data. These findings provide actionable recommendations for OVO developers to enhance server stability and features based on user feedback. Additionally, this study serves as a reference for future sentiment analysis research employing algorithmic comparisons.