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Mining activities in the Raja Ampat area have sparked various public reactions, both supportive and critical, particularly on social media platforms such as Twitter. This study aims to analyze public sentiment regarding the mining operations by employing two classification algorithms. A total of 500 tweets related to Raja Ampat were collected from the X platform, and after data cleaning, 168 were identified as positive sentiments and 303 as negative. Sentiment analysis was conducted using text mining techniques by comparing two algorithms: Support Vector Machine (SVM) and Naïve Bayes. To address the issue of data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The analysis results showed that SVM achieved an accuracy of 80%, outperforming Naïve Bayes, which reached only 68%. This indicates that SVM performed better in classifying sentiment. Additionally, the application of SMOTE effectively enhanced both algorithms’ abilities to detect positive sentiment, as reflected in the precision, recall, and F1-score metrics. For SVM, precision reached 85%, recall 80%, and F1-score 80%, while Naïve Bayes recorded a precision and recall of 69%, and an F1-score of 68%.

This study explores the integration of the Grounded Theory (GT) approach, developed by Anselm Strauss and Barney Glaser, into thematic hadith studies (maudhū‘ī) as a methodological alternative for extracting meaning and constructing theory from hadith texts. This approach is considered capable of producing more contextual, relevant, and applicable understandings of contemporary social issues such as justice, leadership, and environmental ethics. By employing GT’s systematic stages open coding, axial coding, and selective coding hadith research goes beyond mere thematic classification and evolves into a theorization process directly rooted in hadith data. While GT offers advantages in flexibility and contextual depth, its application also faces epistemological and technical challenges, particularly in terms of data validity, interpretive bias, and the integration of modern qualitative methods with the principles of ulūm al-ḥadīth. Therefore, an interdisciplinary approach is necessary, involving collaboration between hadith scholars and social researchers, alongside strong mastery of qualitative methodology. In conclusion, GT is not a replacement for classical methods, but a complementary tool that broadens the horizons of hadith studies. By combining the strengths of the Islamic scholarly tradition and contemporary methodologies, this approach has the potential to enrich Islamic epistemology and to present hadith as a living, dynamic, and relevant source of values for modern society.

This study aims to evaluate user sentiment toward the Liputan6.com application available on the Google Play Store. In the digital era, user reviews serve as a significant indicator in assessing the quality of an application. However, the inconsistency between rating scores and review content renders manual analysis less objective. To address this issue, a machine learning approach was adopted by comparing two algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). A total of 2,500 reviews were collected through a web scraping process and automatically labeled based on the rating (positive if ≥ 3, negative if < 3). The data preprocessing stages included cleaning, case folding, tokenizing, stopword removal, and token filtering. Subsequently, word weighting was carried out using the TF-IDF method, followed by classification using 10-Fold Cross Validation in RapidMiner. The evaluation results indicate that, in the positive class, NB demonstrated superior precision (89.47%), whereas SVM achieved higher recall (98.94%) and F1-score (90.96%). In the negative class, SVM performed better in terms of precision (66.15%), while NB attained higher recall (65.65%) and F1-score (36.34%). Further evaluation based on AUC and accuracy positioned SVM in the good category (AUC 0.842; accuracy 83.82%), while NB was categorized as fail (AUC 0.505; accuracy 60.87%). Overall, SVM is considered to be more effective than NB.

This study presents an advanced AI-powered framework to detect and prevent cyberbullying across diverse social media platforms using a multiclass classification approach. Addressing the growing complexity and linguistic diversity of online abuse, the research integrates various machine learning (RF, LR, SVM) and deep learning (Bi-LSTM, BERT) models trained on a balanced dataset covering bullying categories based on religion, age, ethnicity, gender, and neutral content. Data preprocessing, tokenization, feature extraction via TF-IDF and CountVectorizer, and class balancing using SMOTE were applied to enhance model accuracy. The proposed system further supports real-time detection through social media APIs, offering dynamic monitoring and intervention capabilities. Among the tested models, Random Forest and BERT achieved the highest classification performance with 94% accuracy. Despite its robust architecture, limitations include dependence on English-language datasets, exclusion of multimodal data (e.g., memes, audio), and API restrictions that challenge scalability. Future development will focus on incorporating vision-language models and optimizing the system for real-time, multilingual, and multimodal environments. This study contributes to digital safety efforts by proposing a scalable and adaptive detection system suitable for safeguarding users from evolving forms of online harassment.

This study is a literature study that aims to analyze the practice of withholding and collecting Income Tax (PPh) Article 23 in the context of tax regulations in Indonesia. Data were obtained through a literature review of laws and regulations, tax textbooks, and relevant scientific journals. The results of the analysis indicate that although the provisions of PPh Article 23 have been regulated in detail, there is still the potential for differences in interpretation in practice, especially regarding the classification of tax objects and withholding rates. This study recommends increasing the socialization of regulations and simplifying tax administration in order to improve taxpayer compliance.

This study aims to analyze the derivational affixes found in the Regina Caeli speech delivered by Pope Leo XIV. The main focus of the research is to identify the types of derivational affixes and determine their frequency, including both prefixes and suffixes. The research applies a mixed methods approach, combining both quantitative and qualitative descriptive methods. The quantitative aspect involves identifying and counting the types and frequency of derivational affixes found in Pope Leo XIV’s speech at Regina Caeli. The qualitative aspect focuses on analyzing the contextual meaning and function of those affixes within the speech. Data collection techniques include textual analysis.. Based on the analysis, a total of 13 types of derivational affixes were identified, consisting of one prefix and 12 suffixes. Furthermore, derivational affixes in this study are classified into four types: noun, verb, adjective, and adverb derivation. This study is expected to encourage English learners to understand and apply derivational affixes effectively, as doing so can significantly enrich vocabulary and enhance linguistic competence by recognizing root words and how new forms are created. This classification helps English learners expand vocabulary and understand how word forms are systematically built, thus enhancing their language skills.

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.  

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.

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.

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.