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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.

One form of text that can express emotions is lyrics. Lyrics are a type of literary work expressed in the form of words, the contents of which can express the songwriter's personal feelings, thoughts, and emotions. Therefore, the lyrics can be used as an object of research on the classification of emotions. The classification of song lyrics really requires bi-LSTM to be the input value when classifying data in the form of song lyrics in order to get high accuracy results. This research was carried out systematically and the results were measurable. Descriptive qualitative research was used in this research. The results of identification based on case studies and statistics show that the reviews of popular topics are identical. The classification of song lyrics really requires bi-LSTM to be the input value when classifying data in the form of song lyrics in order to get high accuracy results.

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.

This study aims to analyze the use of Bloom's Taxonomy in identifying the difficulty level of questions in mathematics in elementary schools. Bloom's taxonomy is a classification framework used to classify learning objectives into six different levels, namely knowledge, understanding, application, analysis, synthesis, and evaluation. The research subjects were 15 students in class V at SDN Karet 2, Tangerang Regency. The research method used was content analysis, in which questions in mathematics textbooks for elementary schools were analyzed based on Bloom's Taxonomy categories. The data collected included the difficulty level of the questions based on Bloom's taxonomy and the distribution of the difficulty levels of the questions in mathematics textbooks. The results showed that the level of difficulty of the questions in mathematics in elementary schools varied and could be classified based on Bloom's taxonomy. Most of the items focus on levels of knowledge and understanding, with a few items testing students' abilities to apply, analyze, synthesize, and evaluate mathematical concepts. This shows that there is a tendency to emphasize understanding concepts rather than applying concepts in learning mathematics in elementary schools.

Technological developments today make it easy for people to use social media as a means of expressing opinions, including Twitter. The case study taken by the researcher is the sentiment towards the performance of the mayor of Medan. The case was taken because it was widely discussed by Indonesian people, especially the city of Medan on Twitter social media. One of the uses of this research is to find out the trend of Twitter user comments on the performance of the mayor of Medan by conducting a sentiment analysis. Sentiment will be classified as positive, negative and neutral. The algorithm used in sentiment analysis is Naïve Bayes. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The results of this study are using the SMOTE method, the training and testing ratio is 80:20 because it has the highest accuracy, which is 78% compared to other ratios. The prediction results resulting from the classification turned out to be more dominant towards neutral labels. In addition to classifying for sentiment analysis, this study also measures the performance of the model created. The results showed that the Naïve Bayes algorithm has a precision value of 78%, a recall of 78%, and an f1-score of 77%.

The rapid growth of e-commerce in Indonesia has generated a massive and continuous volume of product reviews. This user-generated content is vital for business intelligence, yet its sheer scale makes manual analysis inefficient, subjective, and practically impossible. Automated sentiment analysis is therefore crucial for businesses to efficiently understand customer feedback and market perception. This research addresses this gap by implementing the Naïve Bayes Classifier (NBC) algorithm to automatically classify the sentiment of Indonesian-language e-commerce product reviews. This study utilized a dataset of 2,000 reviews collected from a major e-commerce platform's "Electronics" category. The data underwent critical text preprocessing stages (case folding, tokenizing, stopword removal, and stemming using the Sastrawi library) to handle the complexities of informal Indonesian text. The dataset was split using an 80/20 ratio, resulting in 1,600 training reviews and 400 testing reviews. Model performance was then evaluated using a Confusion Matrix, focusing on the key metrics of Accuracy, Precision, and Recall. The test results showed excellent performance, achieving an Accuracy of 90.00%, Precision of 91.93%, and Recall of 95.00%. These results demonstrate that the Naïve Bayes algorithm, when supported by robust preprocessing, is a highly effective, reliable, and computationally efficient method for this task, providing a valuable tool for e-commerce stakeholders.