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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.

Sentiment analysis on social media is widely used to represent public perceptions of sports performance, particularly in international competitions. This study aims to analyze the sentiment of YouTube user comments regarding the performance of the Indonesian National Football Team during the FIFA World Cup 2026 Asian Qualifiers. The data were collected from user comments on videos related to the matches and analyzed using a machine learning–based sentiment analysis approach. Sentiment classification was performed using the Naive Bayes algorithm. The results indicate that the proposed approach is able to effectively identify public sentiment toward the national team’s performance during the qualification matches. The findings of this study are expected to provide insights into public perceptions and contribute to sentiment analysis research in the field of sports.

Savings and Loan Cooperatives (KSP) play a vital role in expanding community access to capital, especially within the informal sector. Nevertheless, non-performing loans remain a persistent challenge that can threaten liquidity and long-term institutional sustainability. KSP CU Mera Ndi Ate faces similar issues, which are assumed to stem not only from administrative weaknesses but also from members’ perceptions and behavioral factors. This research aims to examine the potential causes of non-performing loans through text-based sentiment analysis using an unsupervised learning approach. A quantitative method with a data mining framework was applied. Data were gathered through interviews, observations, documentation, and 200 customer opinion texts processed using the Orange Data Mining application. The analytical stages included preprocessing, corpus development, feature extraction, sentiment clustering, and visualization. Because the dataset lacked predefined labels, unsupervised learning was used to identify naturally emerging sentiment patterns. Findings reveal a predominance of critical sentiments related to credit assessment procedures and service quality. The highest sentiment score (75) concerned insufficient creditworthiness evaluation, followed by concerns about service efficiency (66.6667). These insights suggest that improving assessment accuracy and service quality may help reduce non-performing loans.

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.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

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.

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.

The revocation of Pinkflash's cosmetic product distribution license by the Food and Drug Supervisory Agency (BPOM) has once again attracted public attention after the brand experienced a similar incident in 2024. This crisis raises questions about Pinkflash's public relations strategy in maintaining consumer trust and preserving its brand reputation. This study aims to analyze Pinkflash's public relations strategy and measure the impact of the crisis on public perception through social media sentiment analysis using Brand24. The research method used is a descriptive qualitative approach. The main data in this study was obtained from official documents by BPOM and official brand statements, which were then analyzed using content analysis and sentiment analysis using Brand24. The results of the study show that Pinkflash's crisis response only temporarily dampened negative sentiment. The root of the problem lies in weak governance, which led this study to conclude that reputation can only be rebuilt through comprehensive improvements to the quality of its systems and supply chain, with a commitment to honest operations as the key.

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.

Bea Cukai has recently been in the public spotlight, especially regarding the supervision of goods from abroad. News and public responses regarding Bea Cukai's supervision create pros and cons, thus triggering a variety of responses from the public. This study aims to analyze the sentiment of Indonesian people towards the performance of Bea Cukai in monitoring goods from abroad by utilizing Twitter social media. In this research, the Support Vector Machine (SVM) algorithm is applied to classify public comments on Twitter into positive or negative sentiments. Through the crawling process carried out from June 1, 2023, to May 12, 2024, 9,051 entries of data were collected. The analysis results showed an accuracy of 93.87%, precision 94%, recall 93%, and F1-score 94%. These results show that the SVM method is effective in analyzing public sentiment, especially related to Bea Cukai's supervision.

Sentiment analysis of guest reviews is a crucial aspect in improving the quality of hotel services. This study aims to analyze the sentiment of guest reviews regarding the services of Grand Diamond Hotel Yogyakarta using a machine learning approach with the Support Vector Machine (SVM) algorithm. SVM was chosen because it can handle high-dimensional data such as text and is capable of forming an optimal separating hyperplane between sentiment classes. The research data was obtained through web scraping from Traveloka, yielding 1,119 reviews, which were processed through preprocessing, translation, and sentiment labeling using the TextBlob library. After TF-IDF weighting, the data was divided into 80% for training and 20% for testing. The linear kernel SVM model achieved 80% accuracy in classifying the reviews into positive, negative, and neutral categories. The results of this study were implemented in a web-based application equipped with data visualization and model evaluation features, allowing hotel management to efficiently monitor and analyze guest sentiment and support data-driven service quality improvement.

JakLingko is a digital-based public transportation integration system developed to facilitate access to various transportation modes in Jakarta. Along with the increasing number of users, reviews on the JakLingko application reflect user experiences and perceptions. This study aims to analyze the sentiment of user reviews on the Google Play Store using the Naïve Bayes method. Data collection was conducted through web scraping, resulting in 3,260 reviews. The data were preprocessed, sentiment-labeled, and classified using Orange Data Mining. The research applied a quantitative experimental approach with a machine learning framework. The classification results showed that neutral sentiment dominated user reviews, followed by negative and positive sentiments. The Naïve Bayes model achieved 100% accuracy based on the confusion matrix and other evaluation metrics such as precision, recall, and F1-score. The findings highlight that Naïve Bayes can be a reliable approach for analyzing public opinion and serve as a reference for evaluating and improving digital service applications.

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.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.

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

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.

Communication is the process of exchanging information, ideas, thoughts, and feelings between individuals or groups through the use of words, signs, or actions. This process can take place verbally or non-verbally and involves various media and channels, such as face-to-face conversations, writing, gestures, facial expressions, and digital technology. This research was conducted at STMIK Kaputama Binjai, namely the WhatsApp group between lecturers and students. This study uses the Support Vector Machine (SVM) method. SVM is a type of supervised learning machine learning that requires sample data. Support Vector Machine (SVM) is an algorithm developed by Boser, Guyon, and Vapnik in 1992. Support Vector Machine (SVM) has a concept that is combined with previous computational theories. This method can transform training data into higher dimensions using non-linear patterns. The results of the Support Vector Machine method classification with a total of 16 positive sentiments, 40 neutral sentiments and 71 negative sentiments. Accuracy value 67%, margin error 39%. Positive prediction precision 75%, neutral prediction precision 83% and negative prediction precision 88%..

This research uses a deep learning-based sentiment analysis approach with several main stages, namely data collection, preprocessing, model preparation, and model building. In addition, this research also evaluates the impact of data splitting techniques on the model's performance during the training process.The evaluation results show that the LSTM-GRU model achieved the best performance on the character aspect, with an F1-score of 0.72 in the 90:10 splitting scenario. Meanwhile, the lowest F1-score was found in the editing aspect, with a value of 0.51 in the 80:20 splitting scenario. These findings indicate that the model is more effective in recognizing sentiment in narrative aspects compared to technical aspects. Furthermore, the data splitting technique significantly influences model performance, both in determining accuracy levels and in optimizing the model's effectiveness in identifying sentiment patterns more accurately.

This research explores the factors influencing the adoption of Islamic digital banking among millennials in Indonesia. Employing a qualitative approach through a comprehensive literature review, the study analyzes existing research on Islamic digital banking adoption, focusing on academic journals, conference proceedings, and industry reports. The findings reveal that perceived ease of use and usefulness of digital banking services are crucial. Additionally, social influences, such as peer and family recommendations, and personal factors, including demographics and cultural background, significantly impact adoption rates. This research provides valuable insights for Islamic banks in Indonesia to develop targeted strategies for millennial engagement. By understanding these influencing factors, Islamic banks can tailor digital banking services to meet the specific needs and preferences of this demographic, thereby enhancing market penetration and fostering growth within the evolving digital banking landscape.