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The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.

This research examines the range of phrase types appearing in Kompas.com news articles published in the “Jagat Literasi” section during the August–September 2025 period. It seeks to identify and categorize various phrase structures, such as nominal, verbal, adjectival, prepositional, pronominal, numeral, and adverbial phrases, found within the selected articles. In addition to classification, the study provides a qualitative explanation of the structural characteristics, functions, and usage tendencies of the most frequently occurring phrase types in the corpus. Employing a qualitative research design, this study relies on textual data as its primary source, generating descriptive findings derived from careful observation and analysis of written materials. All identified phrases are interpreted and systematically described to offer a comprehensive understanding of how they function in digital news discourse. The findings highlight the dominance and distribution of several phrase categories and reveal patterns that characterize language use in literacy-themed journalism. Academically, this study contributes to syntactic research by offering a reference for further investigations on phrase usage in online media and by enabling comparative analysis across different platforms or sections. Practically, it enhances readers’ and students’ awareness of effective, precise, and stylistically appropriate language use, supporting improved writing practices aligned with established linguistic conventions.

The idea of functions in mathematics and how they are used to build different mathematical models are methodically examined in this publication. Functions are basic mathematical constructs that show relationships between two or more variables in explicit equations, tables, or graphs. The fundamental building blocks of mathematics are functions, which enable the representation of variable interdependencies in a variety of formats, including formal mathematical expressions, data tables, and graphs. The classification of function types, such as linear, quadratic, and exponential, and their corresponding uses in the domains of physics, economics, and epidemiology are the main topics of this study, which takes a descriptive and exploratory approach.This article illustrates how knowledge of functions greatly aids processes through a review of the literature and an examination of secondary sources from current textbooks and academic publications. of judgment, forecasting, and analysis. In both academic and professional contexts, mathematical modeling based on functions has demonstrated efficacy in accurately and efficiently representing real-world occurrences. Thus, the significance of incorporating functional thinking into STEM education and multidisciplinary practice is emphasized in this essay.

The rapid growth of e-commerce in Indonesia, particularly the Shopee platform, has generated a large volume of user reviews on the Google Play Store, which can be analyzed to understand consumer sentiment. This study aims to compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms in binary sentiment classification (positive and negative) on Shopee reviews, as well as to statistically test the significance of their differences using One-Way ANOVA. A total of 400,498 reviews were collected via web scraping, preprocessed through text normalization, tokenization, and Indonesian language stemming, and then feature-extracted using TF-IDF and Count Vectorizer. Evaluation results show that SVM achieved an accuracy of 91.77%, precision of 91.49%, recall of 91.77%, and F1-Score of 91.56%, while RF achieved an accuracy of 90.07%, precision of 91.68%, recall of 90.07%, and F1-Score of 90.55%. ANOVA confirmed that the performance difference between the two algorithms is statistically significant (p-value = 0.0007) with a large effect size (η² = 0.1815). Therefore, SVM is recommended as a more optimal and consistent algorithm for automated sentiment analysis of Indonesian e-commerce reviews, while also providing a replicable methodological framework for similar future research.

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.

Since the announcement of the policy plan for a name transfer system in the sale of used mobile phones, the issue has attracted widespread public attention and discussion. People have expressed their opinions on social media platforms, particularly TikTok. This study aims to classify the sentiment of TikTok users using Naive Bayes and Support Vector Machine (SVM) algorithms. The data were collected through a comment scraping technique on related content.The research stages include text preprocessing, sentiment labeling into positive, negative, and neutral categories, and feature extraction using TF-IDF. The classification process employs Naive Bayes and Support Vector Machine algorithms, which are then evaluated based on accuracy, precision, recall, and F1-score. The results of this study indicate that both methods are capable of classifying sentiment effectively. However, the Support Vector Machine method is superior to the Naive Bayes method with an accuracy rate of 99.57% compared to 94.30%. This study is expected to help the government understand public responses to the planned policy of the used mobile phone name transfer system.

This study evaluates public sentiment toward Constitutional Court Decision No. 90/PUU-XXI/2023 regarding the age limit for presidential and vice-presidential candidates, a controversial issue closely related to Indonesia’s democratic dynamics. Understanding public opinion on Twitter, as a major platform for political expression, is essential for informing electoral policy formulation. Data were collected using Tweet Harvest through Google Colab and analyzed using the Naïve Bayes algorithm as the primary sentiment classification method, with RapidMiner employed to support and streamline the analytical process. The analysis process included data cleaning, text normalization, stopword removal, manual labeling of 80 tweets as training data, and automatic sentiment classification to identify positive and negative sentiments. From a total of 151 analyzed tweets, 84 (55.63%) were classified as negative and 67 (44.37%) as positive, with the model achieving an accuracy of 66.67%. These findings suggest a tendency toward public opposition to the decision, reflecting dissatisfaction among Twitter users. The study demonstrates that Naïve Bayes is reasonably effective for sentiment classification with limited datasets and provides insights for policymakers in understanding public responses to election-related regulations.

State revenue holds a vital position in sustaining national development and the functioning of government, with taxation serving as the primary contributor to Indonesia’s State Budget (APBN). The substantial reliance on tax income obliges the government to manage the taxation system in an optimal, efficient, and sustainable manner. Nevertheless, the attainment of tax revenue targets in practice remains challenged by various issues, including structural, administrative, and strategic limitations. This study seeks to examine the role of strategic tax management in supporting the achievement of state revenue objectives. The research adopts a literature review approach by analyzing textbooks, national and international scholarly journals, official government publications, and relevant regulatory frameworks. The data are analyzed using a descriptive qualitative method through processes of classification, comparison, and synthesis of findings from previous studies. The findings reveal that strategic tax management has a crucial influence on enhancing state revenue performance through coherent policy formulation, flexible strategy execution, and ongoing performance assessment. The integration of information technology, the reinforcement of tax administration, and the improvement of taxpayer compliance emerge as key determinants in achieving revenue targets. Accordingly, strategic tax management constitutes a fundamental tool for ensuring fiscal resilience and promoting sustainable national development.

This study examines the semantic aspects of the poetry anthology Penyair Tanpa Bait by Ahmad Zayn, a contemporary poetic work characterized by free structure and the absence of conventional stanzas. The background of this research is rooted in the need to understand how meaning is constructed in modern poetry that departs from traditional poetic forms. The objective of this study is to describe and analyze lexical, grammatical, and referential meanings contained in the poems in order to reveal their overall semantic structure. This research employs a qualitative descriptive method with a semantic analysis approach. The data consist of words, phrases, and poetic lines selected from the anthology, which are analyzed through stages of data identification, classification, and interpretation of meaning. The findings indicate that the poems predominantly utilize lexical meanings as the foundation of interpretation, while grammatical structures and referential meanings function to strengthen symbolic and contextual meanings. The absence of stanza divisions encourages a continuous flow of meaning that emphasizes semantic cohesion rather than formal structure. The implications of this study contribute to the field of linguistic and literary studies by enriching semantic analysis of contemporary Indonesian poetry and providing a framework for interpreting non-conventional poetic texts.

Popularitas platform gim Roblox di Indonesia tercermin dari tingginya volume interaksi pengguna di media sosial TikTok. Namun, data komentar yang masif tersebut bersifat tidak terstruktur dan banyak menggunakan bahasa tidak baku (slang), sehingga menyulitkan pengembang untuk mengidentifikasi preferensi pemain secara spesifik terhadap genre tertentu. Penelitian ini bertujuan untuk menganalisis sentimen pengguna dan melakukan studi komparatif preferensi terhadap tiga kategori gim utama dalam ekosistem Roblox, yaitu Roleplay, RPG, dan Horror. Metodologi penelitian dimulai dengan pengumpulan 3.000 data komentar melalui teknik scraping pada video TikTok yang diunggah periode Juni hingga Desember 2025. Data tersebut melalui tahapan pra-pemrosesan yang komprehensif, meliputi pembersihan data, normalisasi teks berbasis kamus slang, dan ekstraksi fitur menggunakan TF-IDF. Klasifikasi dilakukan menggunakan algoritma Multinomial Naïve Bayes dengan penerapan teknik oversampling untuk menangani ketidakseimbangan kelas data. Hasil pengujian model menunjukkan performa yang baik dengan tingkat akurasi global sebesar 77,78% dan nilai Presisi pada kelas Negatif mencapai 91%. Temuan penelitian mengungkapkan bahwa distribusi sentimen global didominasi oleh sentimen Netral (40%) dan Negatif (33%), sedangkan sentimen Positif hanya 26%. Analisis komparatif menunjukkan bahwa genre RPG memiliki rasio sentimen negatif tertinggi akibat keluhan teknis seperti lag, sedangkan genre Horror mencatat sentimen positif yang signifikan di mana respon emosional "takut" diinterpretasikan sebagai kepuasan. Kesimpulannya, stabilitas teknis dan pengalaman emosional menjadi faktor determinan utama dalam preferensi pemain.

This study applies the Vision Transformer (ViT) method to soil-type classification and evaluates its accuracy using digital images. The Vision Transformer (ViT) is a Deep Learning architecture that uses self-attention to extract global features from images, enabling it to recognize texture and color patterns more comprehensively than other convolutional methods. The dataset used consists of eight soil types, each containing 77 image data in “.jpg” format. Each image was processed and augmented to increase the dataset to 700 images per soil type. This was done to prevent overfitting. The ViT model was trained using a 90%/10 % split of the training and validation datasets. The results show that the Vision Transformer (ViT) architecture achieves an accuracy of 71.4%, with a small difference between training and validation accuracy, indicating that the ViT method generalizes well. This method successfully distinguishes soil types such as alluvial, andosol, laterite, and limestone based on subtle visual differences. These results demonstrate that the Vision Transformer (ViT) is effective for soil type classification and has the potential to serve as a basis for developing a digital image-based soil type classification system to support research and precision agriculture.

User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

Crime, an unlawful act that contradicts ethics and norms, has now become a primary factor for the police in Lampung province. This presents a challenge for the police institution in predicting high crime rates. However, there are still many crimes that have not become the main focus of problem-solving at the Lampung Regional Police.This research aims to identify the types and criminal acts of crime with the highest recorded incidence in a crime dataset by performing classification using the Naïve Bayes algorithm. The data was obtained from investigators at the Directorate of General Criminal Investigation of the Lampung Regional Police, with a total of 12,034 JTP (Total Criminal Acts) and 7,518 PTP (Crime Resolution) data points for each type of crime, distributed across the Regional Police, City Police, and District Police throughout Lampung province. The classification process using the Naïve Bayes algorithm reveals the relationship between the work unit (Satker) and the type of crime handled, thereby identifying crime patterns based on the location where they are handled. The results of the research, which involved converting numerical data into binomial (binary) form using the "Numerical to Binominal" feature in Rapid miner, show that the analysis and modeling process, especially in algorithms like Naïve Bayes or decision trees, is more effective when using data in a binary format. Thus, the initial dataset can be visualized in the form of a , with the size of the text varying according to the level of each high-incidence crime; the larger the text, the more frequently or significantly the crime occurred or was reported. The application of this method can help in identifying patterns, dominant trends, and areas of focus for more targeted law enforcement efforts or crime prevention policies.

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 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.

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.