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The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

The discourse on the attributes of God has developed to include the issue of His physical attributes, as described in the texts, which state that God has hands, a face, a chair, a throne, and so on. This article employs a literature study as its method. Literature data are secondary sources, meaning the researcher obtains material indirectly and not from original, first-hand sources. Such sources may contain the biases or perspectives of their authors, and the researcher does not always have full control over how the data were collected or organized according to their original purpose. The results of this study indicate that the existence of God’s attributes is clearly explained by Abduh: the attributes that must be believed by the faithful are derived from the guidance of reason and the information provided by Islamic law. Regarding the classification of God’s attributes, there are 20 attributes that are obligatory for God, 20 that are impossible for God, and attributes that are jaiz (possible) for God. Summarizing the attributes of God mentioned in Surah Al-Qashash verses 68–70: God is the Creator, God is free to choose, God is Most Holy, God is All-Knowing, God is One, God is worthy of praise, God is Most Wise, and to God all things will return. One of the characteristics of a believer is to affirm and have certainty in the existence of God while distancing themselves from ideologies that negate or oppose God.

Sentiment analysis is a technique in Natural Language Processing (NLP) used to identify user opinion tendencies based on textual reviews. This study analyzer user reviews of the Maxim application on the Google Play Store and compares three Machine Learning algoritmhs-Naïve Bayes, Support Vector Machine (SVM), and CatBoost-in classifying sentiment. The research stages include data collection, text preprocessing, feature extraction using TF-IDF and Chi-Square, class balancing using SMOTE, and performance evaluation through Accuracy, Precision, Recall, and F1-Score. ANOVA is used to examine the influence of feature selection on model performance. The results show that each model exhibits different performance level across the tested feature combinations. The CatBoost achieved the highest accuracy of 99,26% and demonstrating the most stable performance. Meanwhile, the Naïve Bayes and SVM models experienced performance decreases experiments, especially after applying SMOTE. These findings indicate that the choise of algorithm, feature extraction method, and class balancing technique significantly affects classification outcomes. Overall, CatBoost is identified as the best-performing model, providing more consistenst classification result in accordance with the characteristics of the user reviews.

Budget efficiency is an important issue in state financial management because it is directly related to government spending priorities and their impact on public service programs. Discussions about budget efficiency policies are widespread on social media platform X, generating diverse public responses, thus necessitating an automated approach to understand public opinion trends more quickly and objectively. This research aims to analyze the sentiment of Indonesian people toward budget efficiency policies and compare the performance of the Naïve Bayes and Support Vector Machine (SVM) algorithms in classifying sentiment. The research data used 10,909 Indonesian-language tweets sourced from a public dataset, which were then processed thru the preprocessing stages including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling is performed automatically using the Indonesian Sentiment Lexicon (InSet) approach to categorize data into positive, negative, and neutral sentiments. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and then the data was divided into training and testing sets with an 80:20 ratio. Model performance evaluation was conducted using a confusion matrix and the metrics of accuracy, precision, recall, and F1-score. The research results show that sentiment distribution is dominated by negative sentiment at 56.78%, followed by positive sentiment at 37.40%, and neutral sentiment at 5.83%. In the classification stage, SVM performed best with an accuracy of 86%, while Naïve Bayes achieved an accuracy of 74%. These findings indicate that SVM is more optimal for sentiment classification on social media text data and can be utilized to more effectively support the analysis of public response to budget efficiency policies.

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Social care values ​​are moral principles that emphasize empathy, altruism, solidarity, mutual cooperation, justice, and tolerance in social life. These values ​​form the foundation for individuals to understand the feelings of others, help selflessly, cooperate, be fair, and respect differences. This study aims to describe the representation of social care values ​​in Wattpad short stories in the family drama genre, identify the most dominant values ​​and the context in which they emerge, and explain the relevance of these findings to learning and character development in students. The study used a descriptive qualitative method because it focuses on the meaning of the text, rather than quantitative measurement. Data were collected through listening and note-taking techniques by carefully and repeatedly reading the short stories to identify sections of the text that contain social care values. Data included words, phrases, sentences, dialogue, and narratives that depict character actions, conflicts, or interactions reflecting empathy, altruism, solidarity, mutual cooperation, justice, and tolerance. All quotations were recorded on a coding sheet to organize the data systematically. Data analysis was conducted using the Miles and Huberman technique, which includes data reduction, classification, and interpretation. In the reduction stage, relevant data was selected and then grouped according to social awareness value categories. Next, interpretation was conducted to explain the representation of these values ​​in the storyline and relationships between characters. 

The rapid expansion of e-commerce in Indonesia has resulted in a significant rise in the number of customer reviews, which serve as a valuable source of insight for understanding consumer satisfaction. This study aims to classify or identify sentiments from product reviews on the Tokopedia platform into three categories, using the Support Vector Machine algorithm. The classification method data were ethically collected through web scraping and include review text, ratings, and the number of “likes.”  The preprocessing stage involved several NLP techniques such as pre-procesesing data representation was generated using the Term Frequency–Inverse Document Frequency method, while the issue of class imbalance was addressed using the Synthetic Minority Over-sampling Technique.  Based on the test results, the SVM model achieved an accuracy of 79.48% on the test data using a linear kernel, showing the best performance in classifying positive sentiments. However, the classification of neutral and negative sentiments still requires improvement. This study demonstrates that the combination of the TF-IDF method, additional numerical features, and data balancing techniques can produce an an efficient sentiment analysis model within the e-commerce domain.

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered

This study aims to explore the relevance of Pupuh Kasmaran Mocoan in Lontar Yusup Banyuwangi to the culture of the Banyuwangi community through a literary anthropology approach. Pupuh Kasmaran Mocoan is part of the literary heritage of East Java and is preserved in Lontar Yusup Banyuwangi, reflecting the cultural values, traditions, and worldview of the local society. The research employed a qualitative descriptive method using reading and data classification techniques based on cultural elements. Data were analyzed by identifying cultural aspects embedded in the text and examining their relevance to the social and cultural life of the Banyuwangi community. The findings reveal that Pupuh Kasmaran Mocoan is not merely a literary work but also a representation of the cultural complexity of Banyuwangi society. It contains various cultural values, including social norms, customs, beliefs, and moral teachings that continue to be relevant in contemporary community life. These findings demonstrate that traditional literary works can serve as important cultural documents for understanding local identity and preserving regional wisdom. The study contributes to a deeper understanding of the cultural elements reflected in Lontar Yusup Banyuwangi and emphasizes the significance of literary anthropology in interpreting traditional literature. Furthermore, the findings provide valuable insights for the preservation and development of regional literature while enriching the discourse on literary anthropology.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

The rapid growth of social media as a primary channel for information dissemination has triggered a significant surge in the distribution of hoaxes, potentially damaging social order, instigating mass disinformation, and threatening national security. This research aims to design an intelligent algorithm for hoax detection by integrating a critical thinking approach into Natural Language Processing (NLP)-based text processing. The algorithmic model is built using a combination of linguistic features, argument logic, and cognitive indicators such as the detection of unsubstantiated claims, identification of source bias, and evidence testing. To ensure accountability and transparency of the system, an Explainable AI (XAI) approach is applied so that classification results can be understood by non-technical users. The research results show that integrating critical thinking significantly improves detection accuracy to 93.1%, with an increase in precision and recall for detecting hoaxes based on emotional narratives. Beyond technical aspects, this model aligns with the mandate of Law of the Republic of Indonesia Number 11 of 2008 concerning Information and Electronic Transactions (ITE Law), particularly Article 28 paragraph (1), which prohibits the dissemination of false and misleading news that harms the public. Therefore, this system is not only scientifically relevant but also supports law enforcement and strengthens digital literacy in the post-truth era. These findings are expected to be a strategic contribution to the development of an ethical, critical, and responsible digital ecosystem.

The advances in the movie industry have led to the rapid rise of streaming platforms, becoming increasingly popular among people. With countries around the world constantly producing movies, subtitles play an essential role in making films more accessible to a broader audience. Subtitling strategies can be defined as a translation practice involving the display of written text, usually at the bottom of the screen, to convey the original dialogues of the speakers and the information contained in the soundtrack. This study uses data from the lk21 website and analyzes it using Gottlieb’s subtitling strategies theory, which consists of ten specific methods for audiovisual translation: Expansion, Paraphrase, Transfer, Imitation, Transcription, Deletion, Dislocation, Condensation, Decimation, and Resignation. A descriptive qualitative approach was adopted in this study with observation, classification, and note-taking of each subtitle, suitable for the research, and describing the subtitling strategies applied in the movie. The findings show that seven strategies were applied in the Indonesian subtitles, namely paraphrase, transfer, condensation, imitation, deletion, dislocation, and expansion. Among them, paraphrase emerged as the most frequently used strategy, likely because figurative or culture-specific expressions in the source text often required adaptation to preserve their meaning for Indonesian viewers. On the other hand, expansion was identified as the least applied strategy, suggesting that the original dialogues rarely demanded additional explanation. Overall, the strategic use of these methods contributed to producing subtitles that were coherent, culturally adapted, and highly accessible to the target audience.

This research aims to analyze GoPay user sentiments on the X social media platform (formerly known as Twitter) using the Naive Bayes Classifier algorithm. Sentiment analysis was conducted to understand user perceptions and satisfaction levels towards GoPay digital payment services based on their shared comments and reviews. Data was collected through a tweet crawling process containing the keyword "GoPay" within a specific period. The research stages included data preprocessing (case folding, tokenizing, filtering, and stemming), sentiment labeling (positive, negative), word weighting using TF-IDF, and classification using the Naive Bayes algorithm. The results showed that from a total of 1,431 analyzed tweets, 797 data contained positive sentiments, and 643 data contained negative sentiments. With a classification accuracy rate reaching 82.94%. The most frequently positively commented factors included ease of use and offered promotions, while the main complaints were related to technical issues and customer service. This research provides insights for GoPay developers to improve services according to user feedback.  

The rapid growth of e-commerce platforms has significantly transformed the way consumers share and access product feedback. One of the widely used platforms in Indonesia is Shopee, where customers actively provide reviews of various products, including local skincare brands such as Kahf facial wash. Customer reviews on e-commerce platforms contain valuable information that can be analyzed to understand consumer opinions and preferences. Sentiment analysis, as a branch of natural language processing, enables the classification of textual data into categories such as positive, negative, or neutral. This study aims to classify Shopee user sentiments regarding Kahf facial wash products by implementing the Multinomial Naïve Bayes algorithm, a well-known probabilistic classifier suitable for text categorization. The research methodology consisted of several preprocessing stages, including data cleansing, case folding, tokenizing, stopword removal, and stemming, to prepare raw review texts for further analysis. For feature representation, the Term Frequency–Inverse Document Frequency (TF-IDF) method was applied to capture the importance of words across documents. To evaluate the classification performance, K-Fold cross-validation was employed with K values of 4, 5, 6, and 10 to ensure model reliability and robustness. Considering the issue of imbalanced datasets in user-generated reviews, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized to balance the distribution of sentiment classes. Based on the confusion matrix, the Multinomial Naïve Bayes algorithm demonstrated effective performance in classifying sentiments, achieving satisfactory levels of accuracy, precision, and recall across different folds. These results indicate that the algorithm is capable of handling sentiment analysis tasks for local product reviews effectively. The findings of this study are expected to provide meaningful insights for businesses in understanding consumer perceptions, thereby supporting decision-making processes in product development, marketing strategies, and customer engagement for local brands.

This study aims to analyze the sentiment of customer reviews of the Grand Jatra Hotel Pekanbaru on the Google Review platform using the Naïve Bayes algorithm. Social media and online review platforms are increasingly becoming the primary source of information for potential customers in making purchasing decisions, particularly in the hospitality sector. Therefore, sentiment analysis of customer reviews is crucial for understanding consumer perceptions and providing strategic input for hotels in improving service quality. The research data was collected using web scraping techniques to obtain publicly available customer reviews. The obtained data was then processed through text preprocessing stages including case folding, tokenizing, normalization, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF-IDF) method was then used to weight each word, so that more relevant words have a greater influence in the classification process. The sentiment classification process was carried out into two main categories, namely positive and negative. The Naïve Bayes model was trained using training data and then tested with test data to measure the algorithm's performance in classifying sentiment. The evaluation results show that the model built is able to achieve an accuracy level of 98%, with a precision value of 97% and a recall of 100% in the positive class, and 92% in the negative class. These findings confirm that the Naïve Bayes algorithm can be effectively used in analyzing customer sentiment towards hotel services and facilities. Practically, the results of this study are expected to provide insight for the management of Grand Jatra Hotel Pekanbaru in understanding customer perceptions, identifying service strengths and weaknesses, and formulating more targeted marketing strategies. In addition, this study can also be a reference for the development of similar studies in the hotel industry and other service sectors.

This study aims to utilize data mining techniques, specifically the K-Nearest Neighbors (KNN) algorithm, to classify leaf diseases in sugarcane (Saccharum officinarum). Early and accurate detection of leaf disease types is a crucial step in prevention and control strategies, thereby reducing potential crop losses caused by pathogen attacks. Leaf diseases in sugarcane, such as leaf scald, rust, and mosaic virus, are known to affect photosynthesis, inhibit growth, and reduce the quality and quantity of sugarcane produced. The classification process in this study was carried out through image analysis of infected sugarcane leaves, where features such as color, texture, and shape were extracted using digital image processing techniques. The KNN algorithm was chosen because of its non-parametric nature, ease of implementation, and its ability to provide accurate classification results even with limited data size. The working principle of KNN is to determine the class of a new sample based on the majority class of its k nearest neighbors in the feature space, making it very suitable for the case of leaf disease image classification. In addition to building a classification model, this study also examines disease prevention strategies based on the identification results. These strategies include the use of disease-resistant sugarcane varieties, the implementation of appropriate planting patterns, land moisture management, regular plantation sanitation, and the measured and environmentally friendly use of pesticides or fungicides. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess model effectiveness across various data scenarios. The results of this study are expected to not only contribute to the development of decision support systems for farmers and related parties but also support the application of artificial intelligence-based technology in the agricultural sector.

This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

The formation of new lexical items through the combination of two or more lexemes is referred to as compounding, a significant process within the field of morphology. As noted by O’Grady (1996), compound words are typically categorized into two types: endocentric and exocentric compounds. Katamba (1994:320) emphasizes that endocentric compounds exhibit greater productivity compared to their exocentric counterparts. This study aims to examine the morphological structures of endocentric compound words found in a specific object—namely, the speech text School Strike for Climate. Employing a qualitative-descriptive method, the research analyzes and interprets the semantic classification of the identified compounds. The speech transcript reveals 26 occurrences of endocentric compounds, all of which function as compound nouns and consist of nominal components.

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.

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%.