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The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

The revised Bloom's Taxonomy is a framework of thinking that is widely used in education to design learning objectives, assessments, and curricula. The revised taxonomy by Anderson and Krathwohl changes the classification from nouns to operational verbs, starting from low to high cognitive levels: remember, understand, apply, analyze, evaluate, and create. This study aims to analyze the application of creative ability (C6) in Islamic Jurisprudence learning at MI Nurul Qodim, especially in the context of implementing the Revised Bloom's Taxonomy. This study uses a descriptive qualitative approach with observation, interviews, and documentation techniques. The results of the study indicate that students' creative abilities in Islamic Jurisprudence learning have not yet developed optimally. This is due to the learning method that still focuses on basic concepts in textbooks and the limited experience of students in solving contextual problems. Lower-grade students are still dominant in playing, while upper-grade students begin to show their ability to argue and solve simple problems. Therefore, a more innovative and contextual learning approach is needed to hone high-level thinking skills, especially in the realm of creating (C6).  

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.

Political dynasty is a political power exercised by a group of people who are related by family, with the aim of obtaining power and ensuring that this power remains within the group by passing it on to other family members. This study conducts a sentiment analysis on comments related to the Supreme Court decision which is believed to pave the way for Kaesang Pangarep in support of Jokowi's political dynasty. Sentiment analysis is carried out using the Naive Bayes method, a commonly used algorithm for text classification based on probability. The data used consists of comments from videos taken from social media platforms. These comments are then categorized into positive, negative, and neutral sentiments. The results of the study show the distribution of public sentiment towards this issue, providing an overview of how the public responds to the decision. The Naive Bayes method is chosen for its simplicity and its ability to provide reasonably accurate results in text analysis.

Land classification for oil palm plantations is an important topic in agricultural and plantation development. In this research, the local binary pattern (LBP) method and support vector machine (SVM) classification were used to identify oil palm plantations from aerial photography images. The main challenge in this process is accurately distinguishing oil palm fields and forests that have similar patterns and colors in satellite images. The LBP method is used to extract important texture features from images, while SVM is used to build a classification model based on these features. The test results show that using this method provides an accuracy value of 83.33% in the classification of oil palm land images. The development of oil palm plantations in Indonesia is becoming increasingly important as investment prospects strengthen. This research helps develop image classification technology to support the agricultural industry.

Blood type identification is an important step to ensure the safety of blood transfusion. Direct observation of blood samples that have been tested with anti-A and anti-B serum can lead to errors in identification due to lack of accuracy or haste in observing. Recognition of blood type can be determined from different textures of blood samples that have agglutination or non-agglutination. This research proposes the application of the GLCM method to obtain texture characteristics found in blood samples based on statistical values, namely contrast, correlation, energy and homogeneity. The calculation results of the feature values ​​​​obtained show that the distribution of values ​​tends to be separate for agglutinating and non-agglutinating blood samples. So it can help the classification or identification process to determine blood type.    

The objective of this research is to find out whether character education in the "English for Nusantara" textbook for seventh-grade students is in accordance with the current curriculum. This research analyzed character education contained in the reading and conversational texts of the textbook. The research methodology used a descriptive qualitative approach and content analysis technique. The characters found in the textbook were classified into six dimensions of the Pancasila Student Profile. The results showed that the six dimensions of the Pancasila Student Profile were found. The dimension of having faith, fear of God Almighty, having a noble character 33.2%, global diversity 12.9%, mutual cooperation 16.7%, independent 20.4%, critical reasoning 7.5%, and creative 9.3%. In the six dimensions of the Pancasila Student Profile, which have 20 elements, only 19 were found, while one element that was not found was intercultural communication and interaction. The characters in this book are mostly integrated implicitly. The primary focus dimension of the “English for the Nusantara” textbook was having faith, fear of God Almighty, and having noble character. In addition, the main focus element in this textbook was the manner towards others. Based on the classification of character education stated by Prismarani (2014), this textbook scored 95% and was classified as ‘very high’ quality.

Understanding the concept of Justification by Faith is crucial for Christians, as it enables them to gauge their comprehension and application of this doctrine in their lives. Many Christians have yet to fully grasp this foundational aspect of their faith. This study aims to elucidate the true significance of Justification by Faith by comparing the perspectives of Paul and James, which at first glance, seem contradictory. This research employs a literature review approach, involving the identification, classification, and analysis of relevant literature on the topic of Justification by Faith according to Paul, James, and Calvin. The study involves a hermeneutical analysis of biblical texts, a comparison of theological perspectives, and a systematic organization of findings. Paul asserts that justification occurs through faith, independent of works, as an act of God's grace (Romans 3:28, 4:5). The law reveals human sinfulness and the need for divine justification through faith in Christ. James emphasizes that genuine faith is demonstrated through works (James 2:24). He argues that faith without works is dead and insists on the necessity of works as evidence of true faith. Calvin integrates both perspectives, emphasizing that justification by faith is inseparable from the process of regeneration by the Holy Spirit. He views faith as a gift from God that leads to good works, the fruit of genuine faith. Justification by Faith is an act of God that can only be achieved through His grace and the sacrifice of Jesus Christ (Romans 4:5; 5:6). Faith is rooted in the truth of God's revelation in Christ, culminating in belief in His crucifixion and resurrection. Both Paul and James agree that faith and works are essential in a genuine response to God, with good works being the inevitable result of true faith. This study underscores the interconnectedness of faith and works, aligning with Martin Luther's assertion that good works are the fruit of righteousness. The comprehensive understanding of Justification by Faith involves recognizing it as a divine act that provides hope for salvation through faith, a gift from God facilitated by the Holy Spirit (Ephesians 2:8; Galatians 5:22).  

This study aims to develop a web-based malware detection system using Convolutional Neural Network (CNN) utilizing the IoT23 dataset. Malware is malicious software that can exploit security vulnerabilities in computer systems, steal data, and degrade performance. The implementation of this detection system involves CNN, capable of extracting important features from both visual and textual data, applied to malware classification. The IoT23 dataset comprises 23 scenarios of IoT network traffic, including traffic from malware-infected devices. The study results show that the developed web application can detect malware attacks with accuracy, precision, recall, and F1-score of 99% on separate data scenarios. This CNN-based detection system has proven effective in identifying and classifying malware attacks, contributing to the enhancement of network and device security.  

Alzheimer's and Parkinson's diseases are neurodegenerative conditions that affect the brain, with Alzheimer's causing cognitive and behavioral decline, while Parkinson's leads to motor and non-motor impairments. Both diseases have significant impacts on the health and quality of life of patients, with prevalence increasing in recent years. Although the exact causes of these diseases are still unknown, MRI (Magnetic Resonance Imaging) is widely used to detect brain activity and serves as one of the diagnostic methods. With technological advancements, intelligent systems in image processing for image classification have been extensively used and have become a popular field due to their ability to replicate human visual capabilities. Image classification is performed using various supervised learning machine learning algorithms based on the shape, texture, and color of the images. This study employs two Convolutional Neural Network (CNN) architectures, ResNet50 and GoogLeNet, to compare the performance of these models in classifying MRI scans of patients with Alzheimer's and Parkinson's diseases. The results show that the ResNet50 model outperforms the GoogLeNet model, with parameters set to 100 epochs, a batch size of 128, a learning rate of 0.0001, and the Adam optimizer, achieving an accuracy rate of 90%.

In the rapidly developing digital era, social media such as Twitter has become part of everyday life and facilitates the rapid dissemination of information, including information about criminals. This research aims to analyze public sentiment towards information about criminals spread on Twitter using the Naive Bayes algorithm. This algorithm was chosen because of its simplicity and effectiveness in text classification. Data was collected through a crawling process from Twitter, followed by a preprocessing stage to remove noise. The research results show that public sentiment towards information about criminals on Twitter is divided into three categories: positive, neutral and negative. After classification, it was found that neutral sentiment increased significantly to 63.4%, while positive and negative sentiment decreased to 10.5% and 26.1%. These findings indicate that people tend to be more careful in reacting to sensitive information. This research provides important insights for related parties in managing information about criminals on social media and can be a reference for developing further policies and strategies.

The rapid growth of social media platforms has generated massive volumes of unstructured textual data containing valuable information about public opinions and sentiments. Extracting meaningful insights from this data has become increasingly important for decision-making in various domains, including business, politics, and social analysis. This study aims to evaluate the effectiveness of deep learning techniques for sentiment analysis of social media data, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. A quantitative experimental approach is employed, where datasets are preprocessed through text cleaning, tokenization, and feature representation using word embeddings. The models are trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that all models perform effectively in sentiment classification tasks, with the hybrid CNN-LSTM model achieving the highest performance due to its ability to capture both local textual features and long-term contextual dependencies. This demonstrates that combining CNN and LSTM architectures enhances classification accuracy compared to individual models. Furthermore, the findings confirm that deep learning approaches are more robust in handling the complexity and noisiness of social media data compared to traditional methods. This study contributes to the development of more adaptive and accurate sentiment analysis models and highlights the potential of hybrid deep learning architectures for real-world applications.

GoPay as one of the digital payment applications in Indonesia faces challenges in understanding user perceptions in the midst of fierce competition. This study aims to develop a user review sentiment analysis model by comparing two approaches to text representation, namely TF-IDF and BERT, as well as two machine learning algorithms, namely Random Forest and Logistic Regression. Review data is obtained from the Google Play Store and processed through pre-processing, feature extraction, and sentiment modeling. The results showed that the combination of BERT + Logistic Regression provided the best performance with an F1 Score of 0.86, showing the superiority of BERT in understanding the semantic context compared to TF-IDF. An important feature analysis identifies financial-related words such as "duitnyaapakah" and "kompensasi" as key issues. This research makes a practical contribution by helping app developers improve the user experience through prioritizing relevant features and solutions to key problems complained of.

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.

Biometric features that can be used for identification include iris, voice, DNA and fingerprints. Fingerprints are the most widely used biometric feature because of their uniqueness, universality and stability. Fingerprint recognition can be grouped into two different forms of problems, namely verification and identification. Verification is comparing one fingerprint with another fingerprint. Meanwhile, identification is matching an input fingerprint with fingerprint data in the database. Thus, identification can be interpreted as an extension of verification carried out by comparing one fingerprint to many fingerprints. Identification is inherently more complex than verification. The problem increases as the number of fingerprint datasets increases, resulting in an increase in the time required for the identification process. However, there is a way to overcome this complexity, namely classification. Apart from that, evolutionary algorithm optimization can also be carried out. The Chromosome Algorithm is an improvement on the evolutionary algorithm with a separate local search process. The memetic or chromosomal algorithm is a simple algorithm with reliable performance that can provide accurate solutions to problems in the real world. The current challenge is with the increasing growth of datasets (more than 106) which include the process of clustering text analysis, molecular DNA simulation, feature selection, and forecasting, handling large-scale optimization such as complex simulations, data mining, quantum chemistry, spectroscopic analysis, geophysical analysis, drug discovery, and fingerprint recognition studies. Chromosome algorithms have proven to be very competitive in large-scale optimization because they are based on stochastic algorithms that do not require gradient information.  

The focus of this study is on the illocution of the anecdote text in the learning module of the Class X Indonesian High School Language Learning module compiled by Indri Anatya Permatasari. The existence of this study is used as a basis for explaining the intent of anecdotal texts as well as raising some issues around the community to be understood wisely through understanding the intent of anecdotal texts. In addition, the study also aims to provide an understanding of the classification of illocution speech in anecdotal text in the Indonesian language learning module of class X SMA compiled by Indri Anatya Permatasari. The methods used in this study are two, namely descriptive and qualitative, and use a pragmatic theoretical approach. The data used is in the form of anecdotal text in the X class of the Indonesian language learning module, which is then analyzed according to the classification of illocution speech. The data source in this study is anecdotal text in the learning module. The data collection for this study uses a listening or reading technique and notes. In his data analysis, use agility and body techniques. As for data presentation, it uses formal and informal techniques. The results of the data analysis found the action of assertive, directive, expressive, declarative, and commissive illocumentation. The most-finding data is assertive illocution speech, while the least is commissionive illocution speech. Hopefully, this article can provide an understanding of the classification of illocution speech in anecdotal text in a learning module.

The focus of this study is on the illocution of the anecdote text in the learning module of the Class X Indonesian High School Language Learning module compiled by Indri Anatya Permatasari. The existence of this study is used as a basis for explaining the intent of anecdotal texts as well as raising some issues around the community to be understood wisely through understanding the intent of anecdotal texts. In addition, the study also aims to provide an understanding of the classification of illocution speech in anecdotal text in the Indonesian language learning module of class X SMA compiled by Indri Anatya Permatasari. The methods used in this study are two, namely descriptive and qualitative, and use a pragmatic theoretical approach. The data used is in the form of anecdotal text in the X class of the Indonesian language learning module, which is then analyzed according to the classification of illocution speech. The data source in this study is anecdotal text in the learning module. The data collection for this study uses a listening or reading technique and notes. In his data analysis, use agility and body techniques. As for data presentation, it uses formal and informal techniques. The results of the data analysis found the action of assertive, directive, expressive, declarative, and commissive illocumentation. The most-finding data is assertive illocution speech, while the least is commissionive illocution speech. Hopefully, this article can provide an understanding of the classification of illocution speech in anecdotal text in a learning module.

This article examines drama texts that focus on pragmatic aspects, namely illocutionary speech acts. The texts studied are drama texts listed in the Indonesian Language Book Class VIII Curriculum 2013. The background of this article is based on the diversity of the application of illocutionary speech acts in the drama text. The purpose of this article is to analyze illocutionary speech acts in drama texts. The method used in this research uses a methodological approach, namely descriptive qualitative, and a theoretical approach, namely pragmatics. The data of this article consists of a group of sentences that form a paragraph taken from the drama text in the Indonesian Language Book Class VIII Curriculum 2013 and studied commensurate with the classification of illocutionary speech acts. The data source comes from the drama text in the Indonesian Language Book Class VIII Curriculum 2013. The techniques used are note-taking techniques and listening techniques or reading data which are data collection techniques. The data analysis technique in the research uses agih and padan techniques. Data presentation techniques with informal techniques. From the existing data, the conclusion is that there are several forms of speech acts in the drama text, namely: representative/assertive, expressive, commissive, directive, and declarative illocutionary speech acts contained in the drama text. This article has benefits for students because it can provide knowledge about the classification of illocutionary speech acts.

This article examines drama texts that focus on pragmatic aspects, namely illocutionary speech acts. The texts studied are drama texts listed in the Indonesian Language Book Class VIII Curriculum 2013. The background of this article is based on the diversity of the application of illocutionary speech acts in the drama text. The purpose of this article is to analyze illocutionary speech acts in drama texts. The method used in this research uses a methodological approach, namely descriptive qualitative, and a theoretical approach, namely pragmatics. The data of this article consists of a group of sentences that form a paragraph taken from the drama text in the Indonesian Language Book Class VIII Curriculum 2013 and studied commensurate with the classification of illocutionary speech acts. The data source comes from the drama text in the Indonesian Language Book Class VIII Curriculum 2013. The techniques used are note-taking techniques and listening techniques or reading data which are data collection techniques. The data analysis technique in the research uses agih and padan techniques. Data presentation techniques with informal techniques. From the existing data, the conclusion is that there are several forms of speech acts in the drama text, namely: representative/assertive, expressive, commissive, directive, and declarative illocutionary speech acts contained in the drama text. This article has benefits for students because it can provide knowledge about the classification of illocutionary speech acts.

This study analyzes how the translation techniques is applied in the bilingual textbook Science Biology for Junior High School.The study was conducted qualitatively. Data was collected by selecting complex sentences from chapter six and seven.Then,I analyzed and categorized the translation techniques used according to the classification of Molina and Albir. The data is then calculated to determine the main translation techniques.The findings indicated that in this textbook,there are many translation techniques that contained in a complex sentences. As a result of the study 40 complex sentences were found in the sixth and seventh chapter.This textbook has nine translation techniques there were: literal translation, borrowing, adaptation, reduction, particularization, amplification, generalization, transposition and description. From 40 complex sentences it can be concluded that there are 15 complex sentences of literal translation,4 complex sentences of borrowing,1 complex sentence of adaptation,2 complex sentences of reduction,3 complex sentences of particularization, 7 complex sentences of amplification,2 complex sentences of generalization,1 complex sentence of transposition,5 complex sentences of description. Literal translation is the most numerous technique in the text book. So , over all the concept of a sentence can be easily understood and the knowledge can be developed from daily experiences.