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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.

  This research aims to conduct a literary psychological study of the novel entitled Hai, Luka by Mezty Mez. This novel was written by a famous author who describes the complexity of the emotions and psychological conditions of the main character. This research explores the psychological elements contained in the novel and analyzes their influence on the character development of the main character. The method used in this research is qualitative method. The results of the research show that the classification of emotions in Dante's character is in the form of guilt, buried guilt, self-punishment, shame, sadness, hatred and love. In this case, the novel Hai, Luka provides a deeper understanding of the complexity of the characters and psychological conditions depicted through literary works. This research contributes to the field of literary psychology studies by revealing how authors use psychological elements to shape characters and stimulate emotional responses in readers    

The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.

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.

Japanese papaya leaves (Cnidoscolus aconitifolius) contain active compounds such as flavonoids, tannins, saponins, alkaloids and terpenoids which have the potential to have antibacterial activity. The aim of this research is to determine the antibacterial activity of Japanese papaya leaves against Staphylococcus aureus bacteria. Extraction of Japanese papaya leaves has been carried out using the maceration method and 96% ethanol solvent. The antibacterial activity test used the agar diffusion method using a cylindrical plate. Sterile distilled water was used as a negative control while as a positive control the antibiotic ciprofloxacin was used. The concentrations of Japanese papaya leaf ethanol extract used in testing were 10%, 20%, and 30%. The research results showed that Japanese papaya leaf ethanol extract with a concentration of 30% had the widest inhibition zone diameter, namely 17.296 mm. Meanwhile, at a concentration of 20%, the average inhibitory zone diameter was 15,222 mm, and at a concentration of 10%, the average inhibitory zone diameter was 13,018 mm. These three concentrations were included in the strong category based on Greenwood classification.  

Batik culture is very strong in Indonesia, this is the reason that batik can be found throughout the archipelago, with unique characteristics that distinguish it in each region. However, people are often confused and find it difficult to recognize one type of batik from another. One of the famous types of batik motif is Batik Parang. This research aims to establish a Convolutional Neural Network (CNN) model to classify Batik Parang and help people distinguish it from other batik motifs. Deep learning, particularly CNN, was chosen because it has a high accuracy rate in image classification. A quantitative Experimental design is used, using a dataset of 100 batik images evenly divided into two classes, namely Batik Parang and not Batik Parang. The dataset is divided into two categories, namely training data and testing data, with a data ratio of 80:20. Thus, by using Convolutional Neural Network (CNN), the classification between Batik Parang and not Batik Parang produces an accuracy of 95%, with the use of epoch = 118 and batch_size = 100.

This research allows an automated system for detecting classified means of transportation in Medan City traffic using the YOLOv8 algorithm. The YOLOv8 algorithm is used to detect transportation objects with accuracy that is many times better than other object detection algorithms and with good accuracy after training with various data sets. The use of this algorithm provides an effective solution for handling congestion in the form of increasing the number of vehicles and less orderly traffic users in the city of Medan. The placement of each transportation object in the image to be tested by the system has an influence on the shape accuracy of the object detection results by the algorithm.

The inner conflict of the main character in the novel entitled Sebening Syahadat by Diva Sinar Rembulan is interesting to research through the study of literary psychology. The inner conflict experienced by the main character in the novel begins when the main character finds the woman of his dreams and searches for an identity that he has not yet believed in. The aim of this research is to describe the inner conflict experienced by the main character of the novel Sebening Syahadat by Diva Sinar Rembulan. This type of research is a literature study. The approach used is a literary psychology approach. The method used is a qualitative method. The data collection technique used is a documentation technique in three ways, namely, literature study, reading technique, and note-taking technique. The instrument used is a data card for the classification of intrinsic elements and forms of inner conflict. The analysis technique used is content analysis technique. The presentation of the results of data analysis is carried out descriptively. The results of this research found intrinsic elements in the form of characters and characterization, plot and setting. The second thing was found to be 47 data on the form of inner conflict in the form of (6) anxiety, (1) obsession, (3) frustration, (12) guilt, (7) hurt, (1) fear, (5) inability, (12) angry. This research can be used as an alternative teaching material at the high school/vocational school level.

Mushrooms are very diverse with characteristics of each type, there are 1,433,800 types of mushrooms that have not been recognized. In this study, researchers used the Neural Network and Deep Learning Inception V3 methods as a feature extraction process in images to classify mushroom images based on genus with the Orange Data Mining application. There are 9 genera of mushrooms used in this study, namely Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, and Suillus. The total dataset used is 2,700, with 300 images for each genus. The test uses the cross-validation method which is applied to the confusion matrix to get precision, recall, F1-score, and accuracy values. In this study, the final classification results were obtained with an accuracy of 82.5% and the genus Boletus mushroom obtained the best results with an accuracy of 98.9%.

Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN InceptionV3 model and combine it with three data augmentations to recognize up to 100 butterfly species. To curb overfitting, this study employs a series of data augmentation techniques. In parallel, we refine the InceptionV3 model by reducing the number of layers and integrating four new layers. The test results demonstrate that our proposed model achieves an impressive accuracy of 99.43% for 15 classes with only 10 epochs, exceeding prior models by approximately 5%. When extended to 100 classes, the model maintains a high accuracy rate of 98.49% with 50 epochs. The proposed model surpasses the performance of standard pre-trained models, including VGG16, ResNet50, and InceptionV3, illustrating its potential for broader application.

Data mining is a technology that plays an important role in supporting data-driven decision making, especially in complex and dynamic higher education environments. In the context of education management, the ability to predict student graduation is an essential aspect because it can help institutions plan strategic steps, intervene earlier, and optimize academic resources. This study aims to apply the C4.5 decision tree algorithm to build a student graduation prediction model based on academic data. The research dataset includes key variables such as Grade Point Average (GPA), total Semester Credit Units (SKS) taken, and student attendance rates during lectures. The analysis was conducted using the C4.5 algorithm, which is known for its high level of interpretability, making the model results easy to understand by policy makers. The test results showed an accuracy of 84.6%, indicating that this method has the potential to support data-based academic management systems. These findings are expected to serve as a basis for educational institutions to improve the effectiveness of monitoring and evaluating the student learning process.

Active learning is optimizing the use of all the potential possessed by students, so that all students can achieve satisfactory learning outcomes according to their personal characteristics. In this mini-research task, the author discusses active learning strategies using the card short method. The purpose of this study was to determine the effect of active learning strategies using the card short method on student learning outcomes. The method used in this study is the method of direct observation or survey in Class XI IPA 3 SMAN 2 Percut Sei Tuan. Data collection techniques used pretest and posttest and the results showed that there were differences in the effect of active learning strategies using the cardhort method with the blood circulatory system material. The card sort learning method with the technique of the games is expected to help students understand the lesson and understand the classification of the circulatory system material. Based on the results of research conducted at SMA Negeri 2 Percut Sei Tuan, there are differences in the influence of active learning strategies using the cardshort method with blood circulatory system material.

The aim of this research is to analyze the simultaneous significant influence of salary and incentives on employee work productivity in the Promotions Division of CV. Lentera Galuh R Tasikmalaya. Salary has a significant partial influence on employee work productivity in the Promotions Division of CV. Lentera Galuh R Tasikmalaya, while Incentives also have a significant partial influence on employee work productivity in the same division. The research method used in this study is a quantitative method with a causal associative analysis approach. The research sample consists of 39 employees of CV. Lentera Galuh R Tasikmalaya. Data were collected through questionnaires distributed to employees in the division. The analytical tool used in this research is multiple regression analysis conducted using SPSS 25.0. The research results indicate that Salary has a classification of Very Strong, Incentives have a classification of Very Strong, while Employee Work Productivity also has a Very Strong classification. The findings of this study are expected to provide a better understanding of the influence of salary and incentives on employee work productivity in CV. Lentera Galuh R Tasikmalaya and offer recommendations to the management for enhancing employee performance and productivity.

Pronunciation plays an important role in communication. It provides the different meaning of English words when they are pronounced differently especially English diphthongs. Producing errors in pronouncing English diphthongs cause misunderstanding in communication. The research was aimed to know(1) the commonmispronounced diphthongs by the second semester of English Study Program’s students atintermediate speaking class and (2) the causation of errors in pronouncing English diphthongs. The mixed method research was used to get the data. The qualitative data were taken based on the observations, documentations, and interviews, meanwhile the quantitative data were taken based on the errors of the students on the tests. The results of the research were described by the percentages. The students commonly made diphthong errors as follows/uә/= 19.3%, /eә/= 21.3%, /ei/= 20%,/aʊ/= 13.10%, and/ɪә/= 26.2%.The errors happened in the classifications ofsubstitution = 46.9%, insertion = 13.8%, and omission = 39.3%. It happened because of the influence of inter-lingual and intra-lingual factors.

Diabetes Mellitus is a hazardous disease, and according to the World Health Organization (WHO), diabetes will be one of the main causes of death by 2030. One of the most popular diabetes datasets is PIMA Indians, and this dataset has been widely tested on various machine learning (ML) methods, even deep learning (DL). But on average, ML methods are not able to produce good accuracy. The quality of the dataset and features is the most influential thing in this case, so deeper investment is needed to examine this dataset. This research will analyze and compare the PIMA Indians and Abelvikas datasets using the Random Forest (RF) method. The two datasets are imbalanced, in fact, the Abelvikas dataset is more imbalanced and has a larger number of classes so it is be more complex. The RF was chosen because it is one of the ML methods that has the best results on various diabetes datasets. Based on the test results, very contrasting results were obtained on the two datasets. Abelvikas had accuracy, precision, and recall, reaching 100%, and PIMA Indians only achieved 75% for accuracy, 87% for precision, and 80% for the best recall. Testing was done with 3, 5, 7, 10, and 15 tree number parameters. Apart from that, it was also tested with k-fold validation to get valid results. This determines that the features in the Abelvikas dataset are much better because more complete glucose features support them.

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.

Convolutional neural network (CNN) is a deep learning (DL) model that has significantly contributed to medical systems because it is very useful in digital image processing. However, CNN has several limitations, such as being prone to overfitting, not being properly trained if there is data duplication, and can cause unwanted results if there is an imbalance in the amount of data in each class. Data augmentation techniques are used to overcome overfitting, eliminate data duplication, and random under sampling methods to balance the amount of data in each class, to overcome these problems. In addition, if the CNN model is not designed properly, the computation is less efficient. Research has proved that data augmentation can prevent or overcome overfitting, eliminating duplicate data can make the model more stable, and balancing the amount of data makes the model unbiased and easy to learn new data as evidenced through model evaluation and testing. The results also show that the custom convolutional neural network model is the best model compared to ResNet50 and VGG19 in terms of accuracy, precision, recall, F1-score, loss performance, and computation time efficiency

Phishing is a crime that uses social engineering techniques, both in deceptive statements and technically, to steal consumers' personal identification data and financial account credentials. With the new Phishing machine learning approach, websites can be recognized in real-time. K-Nearest Neighbor(KNN) and Naïve Bayes (NB) are popular machine learning approaches. KNN and NB have their own strengths and weaknesses. By combining the two, deficiencies can be covered. So this study proposes to combine K-Nearest Neighbor with Naïve Bayes to classify phishing websites. Based on the results of the accuracy test of the combination of KNN with k=8 and Naïve Bayes, a maximum accuracy of 93.44% is produced. This result is 6.25% superior compared to using only one classifier.

Plant disease is one of the problems in the world of agriculture. Early identification of plant diseases can reduce the risk of loss, so automation is needed to speed up identification. This study proposes a custom-designed convolutional neural network (CNN) model for plant disease recognition. The proposed CNN model is not complex and lightweight, so it can be implemented in model applications. The proposed CNN model consists of 12 CNN layers, which consist of eight layers for feature extraction and four layers as classifiers. Based on the experimental results of a plant disease dataset consisting of 38 classes with a total of 87,867 image records. The proposed model can get high performance and not overfitting, with 97%, 98%, 97% and 97%, respectively, for accuracy, precision, recall and f1-score. The performance of the proposed model is also better than some popular pre-trained models, such as InceptionV3 and MobileNetV2. The proposed model can also work well when implemented in mobile applications.

Research on the Identification of Drug Related Problems (DRPS) has been carried out in Congestive Heart Failure (CHF) patients at the Outpatient Installation of RSUD dr. Soekardjo City of Tasikmalaya used a descriptive method of medical record data of CHF patients retrospectively. This study aims to determine the frequency and categories of DRPs that occur based on the Pharmaceutical Care Network Europe V9.0 classification. The results showed that out of 80 patients, 288 DRPs were found consisting of 4 symptoms (indications) that were not treated (1.4%), adverse drug events may occur as much as 1 event (0.3%), inappropriate drug combinations or drug interactions as many as 279 events (96.9%), and the dose was too high in 4 incidents (1.4%), so that it can be concluded that the categories of causes and problems of DRPs that most often occur in RSUD dr. Soekardjo City of Tasikmalaya in 2022, namely drug interactions.