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The aim of this research is to describe and analyze the form of directive speech acts and the background of the speech in the short story collection Rokat Tase by Muna Masyari. This research is a type of qualitative research. The approach used is a sociopragmatic approach. The data collection techniques used are listening techniques and note-taking techniques. The instrument used is a data card for the classification of forms of directive speech acts. The analysis technique used is descriptive analysis technique. Presentation of the results of data analysis is carried out informally. The forms of directive speech acts found were (9) directive speech acts of giving advice, (31) directive speech acts of commands, (8) directive speech acts of requests, (3) directive speech acts of ordering, and () directive speech acts of criticism. Meanwhile, the background causes of speech are influenced by physical environmental factors, social influences, culture, social status, social relationships and work. This research can have implications for literature learning at junior high school level.

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.  

Residential is a place to live or everything related to a place to live and in a narrow sense can be interpreted as a residential area or residential building. In connection with the quality of residential facilities, employee satisfaction is an important aspect in improving the quality of the provided residential facilities. If the satisfaction of the residential facilities provided to employees is not met, the employees will feel uncomfortable and will sue the plantation of PTPN IV Marjandi Unit. Analyze the level of employee satisfaction with the quality of the residential provided by the plantation to its employees is The purpose of this study. One of the efforts to find out the satisfaction of employees of residential facilities is to use the C4.5 algorithm classification, where Dataset is obtained through the Questionnaire technique that will be given to employees at PTPN IV Marjandi Unit. As for the satisfaction of employees of residential facilities in terms of aspects (1) water facilities, (2) electricity facilities, (3) kitchen conditions, (4) bedroom conditions, (5) toilet conditions in dwellings inhabited by PTPN IV Unit plantation employees Marjandi. Data processing is assisted using Microsoft Excel software and to validate study data using a rapidminer tool. It is hoped that this research can provide solutions or become a reference for plantations to improve the quality of residential facilities provided to PTPN IV Marjandi Unit employees..

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.

The conflict between Israel and Palestine has been a prominent topic of international discourse for several decades. This dispute spans a century, commencing in 1917 and persisting to the present day. This research delves into sentiment analysis of the Indonesian community concerning the Israel-Palestine conflict through the Twitter social media platform, with a specific focus on boycotting Israeli products. Utilizing Orange and Naive Bayes classification, the study analyzes over 300 datasets of tweets acquired through the scraping process. The objective is to comprehend the nuances, trends, and variations in sentiment among Twitter users regarding the issue of boycotting Israeli products. The results reveal that the majority of the population tends to support the boycott, with a Naive Bayes classification accuracy of 95%, Precission of 96%, Recall of 95%, and F1 Score of 95%. The data preprocessing process, encompassing transformation, tokenization, and filtering, effectively eliminates noise and prepares the data for a more in-depth sentiment analysis.

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 Nutrition Care Center (NCC) is a teaching factory (TEFA) at the Jember State Polytechnic (Polije) which provides assessment and diagnostic services for nutritional problems, comprehensive nutritional assessments, diagnostic services, nutritional counseling, and customized diet plans for clients. Health services, including nutrition, require accurate medical records that include patient identification, examination, treatment, interventions, and other relevant information. However, NCC faces challenges, namely that the medical recording system is still manual paper-based. Medical record numbers do not comply with numbering regulations, age is still used instead of date of birth, the nutritional assessment section is not yet standardized and disease code classification does not yet exist. This manual system is susceptible to damage, loss, and requires dedicated storage time and space. Since it was inaugurated in November 2021, the number of NCC visits has continued to increase both from internal clients and external clients of Polije with initiations with several educational institutions, offices and elderly groups. The use of EMR which is made easier with QR Codes and the application of Whatsapp Bot is the main alternative to build or improve the management of assessment and diagnostic services for nutritional problems according to client needs. Socialization of assistance in implementing Electronic Medical Records (EMR) with QR Code and WhatsApp Bot at NCC aims to make things easier for experts. nutrition to access the client's examination history, allergy history and previous therapy in one view and make it easier for clients to access medical record numbers without a card. This initiative is in line with the Department of Health Research Masterplan 2022 and the development of TEFA services. NCC recognized the need for a more efficient system and proposed implementing EMR with QR Codes and WhatsApp Bots to optimize patient care. This socialization was carried out at the Jember State Polytechnic NCC with the presence of participants from nutritionists, students, the community and administrators from NCC. This socialization obtained results in the form of increasing public understanding of nutritional care, especially in relation to the existence of NCC as a teaching factory at the Jember State Polytechnic, the use of EMR via QR codes and WhatsApp Bot as a solution implemented by NCC in optimizing patient care and increasing the empowerment of partners in using it. and implementation of EMR with QR codes and WhatsApp Bot in patient nutrition care at NCC.

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.

This study is to find out about Parotid Gland Tumors, Salivary Gland Physiology, Definition of Parotid Tumors, Epidemiology, Etiology, Classification, Benign Parotid Tumors, Malignant Parotid Tumors, Clinical Manifestations, Diagnosis, Supporting Examinations, Therapy, Complications and Prognosis. Parotid tumors are tumors that attack the parotid salivary glands. The etiology of salivary gland tumors remains unknown but a multifactorial role is thought to be involved. The most common benign salivary gland tumor in children is parotid gland hemangioma. In adults, benign salivary gland tumors that often occur include pleiomorphic adenomas. Parotid malignant tumors in children are rare and the most common in children is mucoepidermoid carcinoma. The diagnosis of a parotid gland tumor will depend on the history, clinical examination, imaging, and fine needle aspiration biopsy (FNAB). Treatment of parotid tumors is based on appropriate staging and preoperative diagnosis. Facial nerve injuries and Frey's Syndrome are complications that often arise. Most salivary gland tumors are non-malignant and grow slowly. Surgical removal of the tumor usually cures the patient.

As many as 7.3 million people worldwide die from heart disease. This indicates that heart disease is one of the diseases that cause the most deaths. As a preventive effort in handling heart disease, it is necessary to predict heart disease in patients. The classification process to predict heart disease is done using a decision tree. This decision tree is interesting because it is more flexible in providing the advantage of visualizing the advice so that the prediction can be observed. This study uses Heart Disease Prediction Dataset data with a total of 303 data. Then predictions are made using Decision tree so that the accuracy results are 83.60%, precision 89.28%, recall 78.12% and F1 score of 83.33%.

The use of the Decision Tree method in smartphone price classification is the focus of this study. By using the 10 most relevant features and data normalization to achieve scale consistency, the Decision Tree algorithm delivers an average accuracy of 81%. Although some false positives and false negatives occur, the model is able to classify smartphone prices well, especially in identifying low and high prices. These results provide important insights into the features that affect smartphone prices. While there is still room for improvement, this model provides a solid foundation for the smartphone industry to determine prices based on certain specifications. The importance of relevant feature selection and data normalization was revealed in this study. Despite the accuracy reaching 81%, improvements in the classification of medium and high price classes are still possible to reduce prediction errors. This method provides an important basis for the smartphone industry to set prices based on specifications, and data mining techniques such as Decision Tree can be improved to improve the accuracy of future price predictions.

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%.

Scholarships are a form of assistance in the form of educational expenses provided by the government or foundations to students or students who are categorized as from underprivileged families. However, in datermining scholarship recipients, there are still many scholarship recipients who come from wealthy families, while those from less fortunate families do not receive this assistance. This may be due to calculations and data processing that still use manual methods, causing scholarship recipients to not be on target. The purpose of this research is to simplify and minimize calculation errors in determining scholarship recipients for the Smart Indonesia Program (PIP) at SMK Karya Medika. Therefore, for calculating and processing PIP scholarship recipients data, data mining techniques can use the calssification method using the K-NN algprithm. K-Nearest Neighbor is a data classification method that will be used for data objects based on learning data that is closer to the object. In this study using the Confusion Matrix test so as to obtain an accuracy value of 80.00%.     

Di era digital saat ini, kemajuan teknologi informasi mengalami pertumbuhan yang pesat. Salah satu perkembangan yang signifikan terjadi dalam bidang kecerdasan buatan (Artificial Intelligence), yang telah diterapkan luas di berbagai sektor, termasuk analisis data dan pengambilan keputusan. Para peneliti di bidang data mining telah menciptakan beragam algoritma klasifikasi yang meningkatkan proses klasifikasi dengan memanfaatkan atribut numerik dan nominal. Klasifikasi adalah suatu proses analisis data yang menghasilkan model untuk mewakili kelas-kelas dalam data tersebut, seperti yang terjadi pada Decision Tree yang digunakan untuk menganalisis klasifikasi dan pola prediksi data serta menggambarkan hubungan antara variabel atribut  dan variabel target  dalam bentuk struktur pohon. Python, sebagai bahasa pemrograman populer dalam pengembangan kecerdasan buatan, menyediakan pustaka dan framework yang mendukung implementasi algoritma Decision Tree. Dengan menerapkan algoritma Decision Tree untuk klasifikasi data evaluasi mobil menggunakan Python, kita dapat memanfaatkan kekuatan AI untuk memberikan solusi efektif dan efisien dalam pengambilan keputusan terkait evaluasi mobil. Menggunakan dataset Evaluation Car dari UC Irvine Machine Learning Repository, hasil penelitian menunjukkan akurasi sebesar 81%. Confusion Matrix dan laporan klasifikasi menunjukkan performa model yang baik dalam melakukan prediksi.

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.

Abstract.Network anomaly detection is a situation that occurs in network traffic that causes conditions to become abnormal. This research aims to analyze the performance of various machine learning algorithms in network anomaly detection and compare the performance of single classifier algorithms with ensemble learning. This ensemble learning technique has advantages such as increased accuracy and performance, can reduce the risk of overfitting and underfitting by using different subsets and features of data, and can turn weak learning into strong learning. However, on the other hand, this ensemble learning technique also has disadvantages in its use, namely that this ensemble method may not work well with high variance models, as the ensemble method may not be optimized for anomaly detection and that this method can be computationally expensive and time consuming due to the need to train and store multiple models. Some of the techniques used are deep learning, eager learning, lazy learning, bagging, feature selection, boosting, and stacking. In addition to this, this machine learning algorithm has weaknesses, including if any of the data used is incomplete, it will result in inaccurate completion data, making the programming process quite time-consuming. This research can help develop a more effective and efficient network anomaly detection system. The results of this research show that using ensemble learning and feature selection techniques can improve anomaly detection performance by reducing the processing time of redundant data and classification, as well as increasing precision values.    

The purpose of this study is to catalogue the art and music culture education approaches that are in use at SMA Negeri 2 Lubuk Basung. Aspects such as learning objectives, learning materials, methods, musical instruments, learning processes, and evaluation are examined in the musical arts learning activities. The participants in this qualitative descriptive study are SMA Negeri 2 Lubuk Basung eighth-grade students. The researcher assumes the role of the research instrument, supplemented by auxiliary tools such as mobile phones, cameras, and stationery. Data collection was conducted through a systematic review of pertinent literature, observation, interviews, and documentation. Data collection, data classification, data clarification, data analysis, data description, and data conclusion are all components of data analysis techniques. The findings of the study indicate that the teaching modules in the independent curriculum do not provide comprehensive guidance for learning planning, which is influenced by various conditions. Research findings indicate that music culture instructors at SMA Negeri 2 Lubuk Basung have implemented music arts learning strategies consisting of five stages: learning preparation strategies, learning management strategies, strategies for utilising learning media, strategies for implementing model methods and learning approaches, strategies for motivating students, and strategies for evaluative purposes. The purpose of this study is to catalogue the art and music culture education approaches that are in use at SMA Negeri 2 Lubuk Basung. Aspects such as learning objectives, learning materials, methods, musical instruments, learning processes, and evaluation are examined in the musical arts learning activities. The participants in this qualitative descriptive study are SMA Negeri 2 Lubuk Basung eighth-grade students. The researcher assumes the role of the research instrument, supplemented by auxiliary tools such as mobile phones, cameras, and stationery. Data collection was conducted through a systematic review of pertinent literature, observation, interviews, and documentation. Data collection, data classification, data clarification, data analysis, data description, and data conclusion are all components of data analysis techniques. The results of the research suggest that the instructional modules within the self-directed curriculum fail to offer exhaustive direction for the process of learning planning, which is subject to a multitude of factors. Research findings indicate that music culture instructors at SMA Negeri 2 Lubuk Basung have implemented music arts learning strategies consisting of five stages: learning preparation strategies, learning management strategies, strategies for utilising learning media, strategies for implementing model methods and learning approaches, strategies for motivating students, and strategies for evaluative purposes.

Television programming has a profound impact on the formation and development of behavior in people of all ages. Understanding the age categorization code for television broadcast programs and practicing self – censorship on television broadcast programs are two ways to prevent the occurrence of harmful influences.  The purpose of this study is to determine the extent to which the age classification code for television broadcast programs influences parents' self – censorship of their children's viewing.  This study employs a quantitative methodology using survey methods.  Random sampling was utilized to acquire sample data, and questionnaires were used to collect data.  The findings of this study indicate that the age categorization code of television broadcast shows has a favorable influence on parents' self – censorship of their children's viewing.

This literature study aims to analyze and describe the integration of moral values ​​in Islamic Religious Education (PAI) learning based on coastal local wisdom for elementary school students. The focus of this study is to identify moral values ​​that are relevant to the lives of coastal communities, analyze strategies for integrating coastal local wisdom in PAI learning, and describe the potential for developing PAI teaching materials based on coastal local wisdom. The research method used is a literature study with a descriptive-analytical approach. The sources of research data are journal articles, reference books, curriculum documents, and relevant previous research results over the past ten years. Data collection techniques are carried out through identification, classification, and content analysis. The results of the study show that: (1) Moral values ​​that are relevant to the lives of coastal communities include gratitude for the gifts of the sea, honesty, cooperation, courage, patience, and responsibility for the environment; (2) The strategy of integrating local coastal wisdom in Islamic Religious Education learning can be done through the development of contextual teaching materials, experiential learning methods, and authentic evaluation; (3) The development of Islamic Religious Education teaching materials based on local coastal wisdom has the potential to increase the relevance of learning, strengthen local cultural identity, and facilitate the internalization of moral values ​​in elementary school students. This study concludes that the integration of moral values ​​in Islamic Religious Education learning based on local coastal wisdom is a relevant and potential strategy to be implemented in elementary schools in coastal areas, especially at SDN 007 Panipahan.

Music genres are becoming increasingly diverse, and many people listen to music because it has benefits such as refreshing, motivating or therapeutic. However, with the increasing number of genres, some listeners have a tendency towards the type of genre they like. In Indonesia itself, there are several popular music genres such as pop, folk, rock, indie and dangdut. Classification of music genres is an interesting topic when looking at this behaviour. Several approaches to classify popular music genres include audio and tabular data approaches. In this research, classifying music genres using an image approach by implementing SuperTML to change the form of tabular data into image form, which is then trained using a pre-trained CNN Densenet, succeeded in achieving an accuracy of 67%.