Publication Search

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.

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.

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.

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.

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 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.

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.

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%.

The legal arrangement of cannabis in Indonesia is based on Article 28H paragraph (1) of the 1945 Constitution of the Republic of Indonesia which guarantees the right to health services, elaborated through Law Number 35 of 2009 concerning Narcotics and Law Number 17 of 2023 concerning Health. Cannabis is classified as a Group I narcotic prohibited for health services under Article 8 paragraph (1) of Law Number 35 of 2009, while Article 139 of Law Number 17 of 2023 requires that the use of medicines containing narcotics may only be carried out based on a prescription from medical personnel. At the same time, Canada through the Cannabis Act (S.C. 2018, c. 16) and Uruguay through Ley No. 19.172 (2013) apply fundamentally different legal arrangements for cannabis. This research uses normative legal research methods with a comparative law approach, applying the criminal policy framework of Marc Ancel and the law enforcement theory of Joseph Goldstein. The results show: (1) cannabis arrangement in Indonesia is prohibitive through Article 8 paragraph (1) of Law Number 35 of 2009, while Article 6 paragraph (3) opens a mechanism for reclassification through Ministerial Regulation; (2) Canada through the Cannabis Act applies a regulated market model with a CAD 11.4 billion legal industry and a 70% reduction in arrests, while Uruguay through Ley No. 19.172 applies a state monopoly with an 85% reduction in arrests without an increase in problematic use; (3) fundamental differences in legal systems, political systems, socio-cultural backgrounds, religion, and narcotics policy philosophy mean that the Canadian and Uruguayan models are not relevant to be directly applied in the Indonesian criminal law system.

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.

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.

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 develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

Islamic religious education at the Madrasah Diniyah level often relies on passive, one-directional teaching methods that reduce madrasah student engagement and long-term retention. This community service study aimed to improve the quality of Fiqh instruction at Madrasah Diniyah Al-Ishlah Kalirejo, Pasuruan, by implementing kinesthetic games, Small Group Discussion (SGD), and visual teaching aids within the framework of Kitab Mabadi’ Fiqhiyyah. A qualitative descriptive approach was employed through four participatory observation sessions, with visual documentation serving as primary data. The program was implemented in four thematic meetings covering funeral prayer procedures, tayammum, hajj simulation (tawaf), and najis classification. Findings indicate that kinesthetic games effectively reduced affective barriers and increased student focus, while the SGD model with a 1:8 mentor-to-student ratio enabled precise procedural correction for motor-based worship practices. The use of concrete teaching aids successfully transformed abstract Fiqh concepts into tangible, memorable knowledge. This study concludes that the integration of active learning methods, small-group mentoring, and visual media within traditional Islamic education settings can significantly enhance student engagement and comprehension. These findings offer a replicable pedagogical model for Madrasah Diniyah educators seeking to modernize instruction while preserving classical curriculum integrity.

The Qur'an al-Karīm employs at least four principal terms in referring to human beings, namely al-basyar, al-insān, al-nās, and banī Ādam. These terms are not merely synonymous; rather, each represents distinct yet complementary dimensions of humanity in constructing a holistic concept of the human being. This study aims to: (1) analyze the semantic meanings of these four terms based on mufrodat studies, Makkiyah-Madaniyah classification, and asbābun nuzūl; (2) compare the interpretations of classical scholars - Al-Ṭabarī, Ibn Kathīr, Al-Qurṭubī, and Fakhr Al-Rāzī - with those of contemporary scholars - Sayyid Quṭb, Ibn ‘Āshūr, M. Quraish Shihab, and Buya Hamka; and (3) formulate their implications for Islamic education. This research employs a library research method using the tafsīr maudhū‘ī approach integrated with Izutsu’s semantic analysis model. The findings reveal that al-basyar represents the physical-biological dimension of human beings; al-insān represents the spiritual dimension in relation to ‘ubūdiyyah toward Allah; al-nās represents the social-collective dimension; and banī Ādam represents the intellectual-rational dimension inherited from Adam through the divine gift of teaching al-asmā’ (Qur'an 2:31). Collectively, these four dimensions provide fundamental implications for the development of objectives, curriculum, methodology, and evaluation within holistic and comprehensive Islamic education.

Drug availability at community health centers is an important indicator of health service quality. Drug shortages or excesses may affect service effectiveness and budget efficiency. This study aimed to determine the level of drug availability at the South City Community Health Center and the Piloloda'a Community Health Center in 2024 based on compliance with the formulary, demand, receipt, and drug availability categories. This study used a descriptive analytical method with a cross-sectional approach. Data were obtained retrospectively from the 2024 Drug Use Report and Request Sheet (LPLPO). Data analysis was conducted by calculating the percentage of compliance with the formulary, demand, and receipt, and by determining drug availability levels using the Indonesian Ministry of Health (2010) formula and the classification of Carolien et al. (2017). The results showed that formulary compliance was 82% at the South City Community Health Center and 67% at the Piloloda'a Community Health Center, both below the 95% standard. Drug demand compliance scores were 151% and 199%, exceeding the 100–120% standard, while drug receipt compliance scores were 71% and 56%, below the 100% standard. Drug availability categories varied from adequate and insufficient to excess stock, with most drug items classified as insufficient stock. In conclusion, drug management at both community health centers has not been fully optimal. Improved coordination between community health centers and pharmaceutical facilities is needed to maintain stable drug availability and support service needs.

This study analyzes visual signs in the Charlie Chaplin animations Safari at The Park and The King in The Ring using Charles Sanders Peirce’s semiotic framework, focusing on icons, indexes, and symbols. Film and animation communicate meaning through visual elements such as gestures, facial expressions, movements, and character interactions, making them rich for semiotic analysis. Using a descriptive qualitative method, the research identified and categorized visual signs in both animations. Results show that icons, which resemble real-world objects, dominate by establishing story settings natural safari environments in one animation and competitive boxing arenas in the other. Indexes reveal cause-and-effect relationships, demonstrating how gestures, expressions, and actions convey danger, fatigue, or emotional shifts. Symbols convey conventional or cultural meanings, such as Charlie Chaplin’s bowler hat and cane representing his comedic identity, a championship belt symbolizing victory, or a rose indicating affection. While both animations use the same types of signs, the intensity and focus vary with the narrative context: Safari at The Park emphasizes situational and natural elements, whereas The King in The Ring highlights competition and emotional reactions. This study confirms that Peirce’s triadic model effectively explains how meaning is constructed in animation through dynamic visual communication.

Group-based learning in the classroom plays a vital role in enhancing social interaction, individual responsibility, as well as students' critical thinking and collaborative skills. However, its implementation often faces challenges such as the dominance of certain members, social loafing, low participation, and interpersonal conflicts that hinder group effectiveness. This study aims to comprehensively examine the dynamics of learning groups by integrating four key aspects: the concept of group dynamics based on the Tuckman model, the characteristics of effective groups in cooperative learning, group formation techniques, and conflict management strategies. The research utilizes a qualitative approach with a literature study method, analyzing 25 sources including nationally accredited journals, academic books, and theses published between 2020 and 2024. Data analysis was conducted through reduction, thematic classification, content analysis, and conceptual synthesis. The results indicate that effective group dynamics can be achieved through the Tuckman stages, the application of the five elements of cooperative learning, the selection of appropriate group formation techniques with risk mitigation, and the implementation of the Thomas-Kilmann conflict management styles.The scientific contribution of this research is the development of an integrative model based on these four aspects, which serves as a conceptual framework to strengthen collaborative learning practices in the classroom. Practical implications include the formation of ideal groups consisting of 4–5 students, the establishment of initial group contracts, the use of dual assessment rubrics (individual and group), and peer evaluation mechanisms to enhance accountability and reflection.

The death of a Madrasah Tsanawiyah (MTs) student allegedly linked to police action raises significant legal issues concerning the limits of the use of force and the construction of criminal liability. This study aims to reformulate the elements of assault resulting in death by integrating the objective element (actus reus) and the subjective element (mens rea) within the framework of the doctrines of dolus and culpa. It also seeks to develop a model of criminal liability analysis that is more transparent, accountable, and oriented toward the protection of a child’s right to life. This research employs a normative juridical method using statutory, conceptual, and case approaches, supported by a literature review of legislation, court decisions, and criminal law scholarship. Data are analyzed qualitatively through grammatical, systematic, and teleological interpretation. The findings indicate that proving the act and the resulting death alone is insufficient without clearly establishing the form of fault. The distinction between dolus eventualis and culpa lata constitutes a decisive factor in determining the classification of the offense and the degree of criminal liability. Ambiguity in identifying the spectrum of fault may lead to sentencing disparities and weaken the principle of geen straf zonder schuld (no punishment without fault). Therefore, this study proposes a reconstruction of the elements of the offense that places proof of mens rea at the center of assessing police accountability while ensuring the protection of the child’s right to life.