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

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

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

Inclusive education aims to provide equal learning opportunities for all students, including those with special needs, within regular educational settings. However, mathematics learning in inclusive classrooms remains challenging because mathematical concepts are often abstract and require logical reasoning that may not be easily accessible to learners with diverse cognitive characteristics. Ethnomathematics has emerged as an alternative approach by integrating cultural practices, local wisdom, and students’ daily experiences into mathematics instruction, creating more meaningful and accessible learning environments. This study aims to analyze the development, implementation patterns, opportunities, and research gaps related to ethnomathematics in inclusive mathematics learning. A literature review method was employed by examining scientific publications from 2020–2025 obtained from Google Scholar, Scopus, ERIC, Springer, and ProQuest databases. Data were analyzed through content analysis involving reduction, classification, interpretation, and synthesis. The findings indicate that ethnomathematics has been implemented through cultural artifacts, digital teaching materials, and project-based contextual learning. The approach supports inclusive learning through multi-representational access, instructional adaptations, scaffolding strategies, and collaborative teaching practices aligned with Universal Design for Learning principles. Furthermore, ethnomathematics enhances students’ motivation, conceptual understanding, mathematical literacy, and cultural identity. Nevertheless, studies focusing on disability-specific adaptations and long-term learning outcomes remain limited and require further investigation.

Early detection of a potential heart attack is a crucial step in preventing sudden death from heart disease. This research aims to develop an Internet of Things (IoT)-based health monitoring system capable of measuring vital body data in real time and predicting the likelihood of a heart attack from CSV data obtained from sensors, integrated through RapidMiner as learning data using a machine learning algorithm, the Support Vector Machine (SVM). The system was built using an ESP32 microcontroller connected to a MAX30102 sensor to measure heart rate and finger oxygen levels (SpO₂), as well as a DHT22 sensor to measure temperature and humidity. The resulting data is sent to the Blynk application to display real-time data according to its parameters. The initial prediction logic was developed using a rule-based method based on medical thresholds for four vital parameters. The data was then used to train an SVM model as a classification system to detect potential heart attacks. Test results showed that the system can identify abnormal conditions with a good level of accuracy and provide early warnings based on changes in vital parameters in real time. This system is expected to be an initial solution for personal health monitoring, especially for individuals at risk of heart disease. It can be further developed with cloud integration and automatic notifications to users' devices.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.

This research aims to analyze how gender and culture are represented in the Grade VIII Indonesian language textbook of the Merdeka Curriculum. The focus is on various aspects of gender representation, such as the depiction of characters, their roles, activities, attributes, social status, gender equality, and stereotypes related to gender. Additionally, the study explores cultural representation, which encompasses cultural forms, diversity, local traditions, context, ways of presentation, and the cultural values expressed in the textbook's texts and illustrations. The methodology employed is descriptive qualitative research with a content analysis framework. Data were gathered through documenting and note-taking methods on the content of the textbook, followed by an analysis process that includes identification, classification, interpretation, and drawing conclusions. Findings indicate that the gender representation in the textbook predominantly portrays men as leading figures in public roles, leadership, and decision-making, whereas women are mainly shown in nurturing, domestic, and supportive capacities. On the other hand, the cultural representation illustrates the variety of Indonesian culture by showcasing regional customs, languages, art forms, traditional cuisine, practices, and societal norms. The study concludes that although the textbook presents cultural diversity adequately, there is a need for improvement in gender representation balance to better reflect equality values in the educational experience.

Sign language recognition is one of the areas of image recognition and image processing technology that is developing rapidly in human-computer interaction. This technology really helps the deaf and speech impaired in communicating with non-disabled people. This research aims to examine the optimization of an object tracking system in sign language using the Gaussian Mixture Model (GMM) and Kalman Filter by including the Region of Interest (ROI). The proposed system consists of three main components, namely hand detection, object extraction, and classification. Hand detection is done using the Kalman Filter to track hand movements accurately. Next, Region of Interest (ROI) features, such as shape, direction and movement features, are extracted from the detected part of the hand. These features are fed into a Gaussian Mixture Model (GMM) classifier, which can recognize sign language based on the extracted features. With the combination of GMM and Kalman Filter in this research, it can increase accuracy in object tracking, reduce interference from the background, and ensure the tracking focus remains on important objects. The dataset used is in the form os SIBI alphabet symbols, namely A-Z with the amount of data for each class, namely 620 images. Based on the research result, model testing using GMM, Kalman Filter and ROI produces higher accuracy of 99%, while model testing using GMM and ROI produces accuracy of 90%.

Assessment practices in Indonesian language learning at vocational high schools are still predominantly oriented toward written tests and cognitive achievement, resulting in limited measurement of communicative competence, language politeness, and self-control. This condition is not fully aligned with the characteristics of vocational education, which prepares students to meet workplace demands through professional communication skills and strong personal character. This study aims to develop an integrative authentic assessment model based on language politeness and self-control in Indonesian language learning at vocational high schools. The study employed a qualitative approach using library research by analyzing 16 relevant references consisting of journal articles, policy documents, and supporting academic sources. Data were analyzed through identification, classification, comparison, interpretation, and conceptual synthesis. The findings reveal that the proposed model should integrate four principal dimensions: linguistic knowledge, language skills, language politeness, and self-control. The model may be implemented through contextual assessment tasks such as presentations, job interview simulations, customer service practices, digital portfolios, peer assessment, and reflective evaluation. This model offers a more comprehensive assessment framework and may serve as a practical guideline for teachers in evaluating students’ academic competence, communication ethics, and work readiness. The study contributes to the development of character-based assessment innovation in vocational education..

This research is based on the importance of studying Malay-Jawi religious manuscripts as a source of transmission of Islamic teachings in the archipelago, particularly in the field of monotheism. The study aims to examine the textual content of Jawi manuscripts containing the treatise of monotheism, especially the concept of the sentence of monotheism and the attributes of twenty, and to explain their position in the intellectual tradition of Malay Islam. The research employs This research is based on the importance of studying Malay-Jawi religious manuscripts as a source of transmission of Islamic teachings in the archipelago, particularly in the field of monotheism. The study aims to examine the textual content of Jawi manuscripts containing the treatise of monotheism, especially the concept of the sentence of monotheism and the attributes of twenty, and to explain their position in the intellectual tradition of Malay Islam. The research employs a qualitative method with a philological approach and content analysis. Primary data consist of Jawi manuscripts, while secondary data are obtained through library research. Data were collected through documentation and literature review and analyzed descriptively. The findings reveal that the manuscripts contain systematically arranged monotheistic teachings, including the meaning of lā ilāha illa Allāh through the principles of negation and affirmation, as well as the concept of faith involving the heart, speech, and actions. The manuscripts also explain the twenty attributes within the classifications of nafsiyah, salbiyah, ma‘ani, and ma‘nawiyah, reflecting the theological framework of Ahlussunnah wal Jama‘ah. These manuscripts function as both religious texts and pedagogical media, highlighting the importance of preserving Nusantara Islamic manuscripts as part of the region’s intellectual heritage.

The concept of naskh and mansukh is one of the most crucial methodological instruments in the discipline of Ushul Fiqh, serving to dissect the dynamics of Islamic law determination (tasyri') diachronically. This article aims to comprehensively analyze how the mechanism of naskh operates within the Al-Qur'an and Hadith and its juridical implications on the process of istinbath (deduction) of Islamic law. The urgency of this study lies in the fact that a flawed understanding of the abrogated verses can lead a mujtahid to establish laws that are juridically expired. Utilizing a qualitative-normative research method with a socio-historical approach, this article explores the classifications of naskh, ranging from the sharp debate over the Sunnah's authority to abrogate the Al-Qur'an to the fundamental differences between naskh, takhshish, and taqyid. The analysis also encompasses a comparative study of the views among the four major schools of thought (Hanafi, Maliki, Syafi'i, and Hanbali) in responding to conflicting evidences. The findings indicate that naskh is not an indication of inconsistency within Divine revelation, but rather a manifestation of the principles of tadarruj (gradualism) and taysir (facilitation) that accommodate the mental readiness of the community and the welfare of human beings. Practical implications of this concept are found in the evolution of laws concerning the direction of the qiblah, the iddah period, and the prohibition of khamr. Through a profound understanding of naskh, Islamic law demonstrates its elasticity in addressing contemporary challenges without losing its divine substance. In the modern era, this principle can be actualized in national legislative drafting through gradual regulatory methods.

This study aims to examine how nepotism can manifest through the role of informal institutions and its influence on administrative integrity within the bureaucracy. The method used is literature analysis by examining various related scientific references, such as books, journal articles, and research, which are then analyzed descriptively and analytically through identification, classification, and data integration. The research findings indicate that nepotism does not only arise from weaknesses in the official system, but is also strongly influenced by the existence of informal institutions such as personal networks, social norms, and organizational culture. This practice tends to persist within a system because it gains social recognition, making it difficult to overcome solely with regulations. The consequences include a decline in employee professionalism, weak accountability, and erosion of administrative integrity, which impacts on reduced public trust in government institutions. The implications of this study indicate that a comprehensive approach is crucial in bureaucratic reform, through strengthening the official system and changing organizational cultural values ​​to produce transparent, accountable, and dignified government management.

Recent Internet of Things (IoT) intrusion detection studies have reported near-perfect benchmark performance for Distributed Denial of Service (DDoS) detection, yet limited attention has been given to understanding how different traffic representations contribute to the detection process under highly imbalanced traffic conditions. This study presents an ablation-driven analysis to investigate the contribution of statistical and temporal representations for large-scale IoT DDoS detection using the CICIoT2023 dataset. Three experimental scenarios are evaluated, including statistical representation, temporal sequence representation, and hybrid statistical–temporal representation. Temporal representations are learned using a one-dimensional Convolutional Neural Network (1D-CNN) with lag-based traffic sequences, while ensemble tree-based classifiers are employed for final classification and representation analysis. In addition, multiple ablation configurations are designed to evaluate the impact of temporal dependency modeling and feature engineering strategies on detection performance. Experimental results show that statistical traffic representations remain highly effective for DDoS detection on CICIoT2023, achieving 99.36% accuracy and 99.31% weighted F1-score in the statistical representation scenario. Feature importance analysis further indicates that engineered statistical features contribute substantially more to the classification process than CNN-based temporal representations. Although temporal modeling captures sequential traffic behavior, its contribution is relatively limited and mainly acts as a complementary representation. Furthermore, the hybrid configuration produces only marginal improvements over the statistical representation alone. These findings highlight the importance of representation-level analysis for understanding the actual contribution of statistical and temporal modeling in modern IoT intrusion detection systems beyond relying solely on benchmark accuracy.

The optimal therapeutic impact of local vaginal drug delivery systems is strongly influenced by the physical characteristics balance of Solid Vaginal Suppositories. A comprehensive review regarding the comparison of mechanical profiles, specifically melting time and crushing strength parameters, from various base classifications constitutes the primary objective of this literature research. The implementation of a Literature Review study design was executed through the extraction of empirical data from twelve experimental journals published within the last ten years. Excessively rapid phase transformation characteristics at physiological basal temperatures and low compression resistance were consistently demonstrated by lipophilic bases such as Oleum Cacao. The risk of structural deformation during the distribution process is highly susceptible to unmodified lipid preparations. High surface elasticity accompanied by a delay in molecular hydration duration reaching 120 minutes was recorded in the utilization of Glycerinated Gelatin Base. Structural rigidity exceeding 4 kgF and disintegration time efficiency under 60 minutes were optimally demonstrated by Polyethylene Glycol (PEG) Base. An enhancement in mechanical resistance against external shocks during the storage period is offered by the thorough modification of the synthetic polymer ratios. Therefore, the determination of the PEG base as the most optimal material is recommended to maintain the quality stability of pharmaceutical products. Compendial regulation standards regarding the physical strength testing of pharmaceutical preparations must be obeyed by every institution to ensure long-term treatment effectiveness. Thus, the alignment between active substance release duration and physical preparation endurance can be realized for absolute patient comfort.

Radio Gagak Rimang is a Local Public Broadcasting Institution (LPPL) owned by the Blora Regency Government that serves the function of disseminating public information, preserving local culture, and providing entertainment for the community. Objectively and systematically measuring listener satisfaction is a fundamental necessity to maintain sustainability and improve broadcast quality. This study applies the Decision Tree algorithm with the Gini Index criterion to classify listener satisfaction levels based on three main attributes: signal quality, content quality, and presenter quality. Data were collected through a survey of 200 respondents in Blora Regency using a Likert scale questionnaire (1-5). The satisfaction target variable was classified into three classes: Fairly Satisfied, Satisfied, and Very Satisfied. The data were divided with an 80:20 ratio for model training and testing. The evaluation results show that the Decision Tree model achieved an accuracy of 90.00% on the test data and an average accuracy of 88.00% on 10-fold cross validation. The presenter quality attribute had the highest contribution to the classification process at 47.13%, followed by signal quality at 32.60%, and content quality at 17.97%. These findings demonstrate that the Decision Tree algorithm is effective as a decision-support tool for evaluating local radio listener satisfaction.

This study aims to determine the soil classification of rock disintegration products based on the Unified Soil Classification System (USCS) and analyze its relation to sample depth variations on the lowwall slope of Pit North, PT Karya Putra Borneo, Kutai Kartanegara Regency, East Kalimantan. Soil samples were obtained through the Slake Durability test, simulating rock weathering from wetting and drying cycles, producing fine particles classified as weathered soil. These samples were analyzed for physical properties using Atterberg Limits tests and Grain Size Analysis. Observation point coordinates were X 508523.011 m, Y 9922791.186 m, at an elevation of 87.548 m. Drilling indicated soil material at 0–1.5 m depth; claystone with coal fragments at 2.97–4.44 m; siltstone with coal fragments at 4.44–10.55 m; and claystone at 12.05–29.36 m. USCS classification showed the materials were dominated by fine-grained soils: clay (CL) and silt (ML), with minor silty sand (SM). Correlation with borehole depth revealed no significant changes in soil classification, indicating that depth variations primarily affect soil physical properties rather than its classification type.  

This study aims to examine the utilization of classification codes in the sorting of records and non-records. The sorting of records and non-records is an important stage in information management. However, in practice, it is often not carried out systematically, resulting in the mixing of records and non-records with different values. This condition leads to disorganized records management and difficulties in information retrieval. This study employs a descriptive qualitative method with a library research approach. The data were obtained from scientific journals, books, and relevant regulations related to records management. Data collection was conducted through literature review, while data analysis was carried out descriptively through data grouping and interpretation. The results show that classification codes are used as a basis for distinguishing and grouping records and non-records according to organizational functions and activities, thereby supporting better organization in the sorting process. The implication of this study indicates that the use of classification codes supports more structured and systematic records management.