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Mental health is a state of well-being in which a person is aware of his or her abilities, can cope with normal life stresses, can work productively and contribute to his or her community. Mental health encompasses emotional, psychological and social well-being, and affects how a person thinks, feels and acts. It also determines how a person handles stress, relates to others and makes decisions. Prediction methods that can identify the level of mental health of students are important as a preventive measure. One promising method in this regard is the Naïve Bayes Method. This method has the advantage of being able to solve classification problems on complex datasets, such as student mental health data involving many independent variables. An expert system is a system that attempts to adopt human knowledge into computers so that computers can solve problems as is usually done by experts. The purpose of this study was to find out how to predict the level of mental health of students towards the end of school using the Naïve Bayes method. The results of this study are that the prediction of the level of mental health of students towards the end of school using the Naïve Bayes method can be used and the system created works well, without having to consult a doctor or psychologist.

Abstract Databases stored on storage media are rarely used by most of their users and even within a certain period of time the data is deleted because it is considered trash and only fills up the storage media. This assumption is not entirely true, because in fact a large database can provide the information needed for various interests, both for business interests in making decisions and for science and research. The development of information and communication technology in this era often called the millennial, information and communication technology is also increasingly advanced and developing and cannot be avoided. Where the development and progress of information and communication technology is growing very rapidly, such as the need for data processing which is increasing every day and if left alone, the data will be useless. By using the Text Mining technique, the classification method, a sentiment will be known to be positive, neutral or negative. One of the algorithms widely used in sentiment analysis is the Naïve Bayes classification method. This study uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotion icon conversion feature (emoticon) to determine the existing sentiment class from tweets about Agu Ate Store. The results of the study show that the Naïve Bayes method without additional features is able to classify sentiment with an accuracy value of 96.44%, while if the tf-idf weighting feature is added along with the emotion icon conversion, the accuracy value can be increased to 98%.

The purpose of this research is to analyze the function of the Kupang City Manpower and Transmigration Office in tackling employment problems and efforts to overcome the obstacles faced in the city of Kupang. This type of research uses Sociological juridical research. The research location was carried out at the Kupang City Manpower and Transmigration Office. The types of data used in this research are primary data and secondary data. Data collection techniques with observation, interviews, and documentation. Data processing techniques with editing, data classification, data systematization, data reduction, and data verification. Data analysis techniques are analyzed using qualitative descriptive juridical methods. The result of this research is that the Kupang City Manpower and Transmigration Office (Disnakertrans) has a strategic function in managing and overcoming labor problems in the region. Disnakertrans is responsible for implementing labor policies that include job placement, transmigration, employment expansion, and problem solving in industrial relations. The main functions of the Disnakertrans include guidance, supervision, control, public services, program development, budget supervision, as well as policy development and dispute resolution. There are 2 factors that inhibit the Disnakertrans in tackling labor issues in Kupang City, namely the budget and human resources. Efforts need to continue to be made to provide training, direction, and guidance to human resources so that they can work more professionally, purposefully, and responsibly in accordance with their duties.  

This research analyzes the types of illocutionary acts. Therefore, the problem of this research is "What types of illocutionary acts are found in the Nightbooks movie?. The aim of this research is to describe the types of illocutionary acts found in the Nightbooks movie. Data is analyzed through several steps including identification, analysis, classification, description and drawing conclusions. The author uses descriptive qualitative research to collect data, through several steps including watching movie, listening to speech in movie, identifying and classifying data. After investigating the types in the movie, the author found several findings. The illocutionary acts in the movie can be analyzed into several types, namely 5 commissive acts, 4 representative acts, 6 directive acts, 4 expressive acts and declarative acts. In summary, the illocutionary acts used in the movie aim to analyze the types of illocutionary acts used in the Nightbooks movie.

The North Sumatra Aceh National Sports Week (PON) which will be held in September 2024 in Papu, North Sumatra has drawn many pros and cons from the public. This topic allows the public to provide criticism, suggestions and opinions regarding the 2024 North Sumatra Aceh PON. Instagram is a popular social media for conveying public opinion. The sentiment analysis process can find and resolve problems based on public opinion on social media such as Instagram. The classification method used in this research is the Naïve Bayes Classifier. Datasets can be obtained from the data crawling process using the Google Chrome extension: IGCommentExport. The data is labeled positive, neutral, or negative. The results of the labeling process showed 770 negative data, 256 neutral data and 920 positive data. Then pre-processing is carried out on the data that has been previously labeled, and a word weighting process is also carried out using TF-IDF. After that, modeling was carried out using the Naïve Bayes Classifier and the final process was evaluation-testing. The high accuracy results from the fourth experiment which compared 90% of the training data with 10% of the testing data resulted in an accuracy of 75%. Meanwhile, the sentiment test results show that positive sentiment is more numerous than negative sentiment and neutral sentiment.

The research titled “Examining Social Dynamics by Using Slang expressions in The Movie Scripts Entitled Barbie” adopts a qualitative approach to scrutinize the slang utilized by characters in the Barbie Movie, aiming to dissect the social dynamics depicted in the script. The study's main objective is to classify the diverse slang terms and comprehend their effect on the characters' social interactions and bonds. Employing Chapman’s (1988) classification of primary and secondary slang, the study organizes the slang found in the screenplay. It probes into how these terms influence character evolution, social ties, cultural variety, identity, and self-assertion. This examination underscores the role of slang in enriching thematic representation and advancing the movie's storyline. The results indicate that slang in "Barbie" serves beyond mere colloquialisms; it's a strategic narrative device that deepens character depiction and accentuates the intricate social interplay within the plot. The research deduces that slang is pivotal in crafting the movie's social milieu, offering profound insight into the personas and their interrelations, thereby enriching the audience's grasp of the cultural and societal contexts portrayed in the film.

According to academic data, student math ability tests at MTSS PAB 5 Klambir Lima yield mixed results. There are students who understand math well, but there are also those who have difficulty understanding the mathematical concepts themselves. Math teachers at this school have difficulty designing lessons that can meet the needs of students with different levels of understanding. So, it is necessary to group student data to produce educational decision-making and improve learning effectiveness, such as through data mining. Data mining is a semi-automated process that uses machine learning techniques, mathematics, statistics, and artificial intelligence to identify and organize information contained in large databases. The process of finding information can be done by determining the decision rule based based on the level of student understanding in mathematics lessons using the Decision Tree Algorithm C4.5 method. The use of the Decision Tree algorithm C4.5 aims to make it easier to determine decision rules based on gender, Predicate, teacher teaching methods, student learning interest, and level of understanding. Based on the results of the study, it was found that if the teacher's teaching method is good, the predicate value is B, the student's learning interest is less interested, and the gender is male, then the student's level of understanding in mathematics lessons is not understood.

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%.

This research is motivated by the problem of the influence of menu innovation on guest satisfaction at Pandan View resort and restaurant. This type of research is quantitative with a causal associative form. This research was conducted at Pandan View Mandeh resort and restaurant. The aim of this research is to evaluate the influence of menu innovation at Pandan View Mandeh resort and restaurant. The population in this study was 29,100 people, with a sample of 98 respondents. Data collection used a questionnaire with a Likert scale. Using descriptive analysis and classification of interval class scores using the SPSS version 26 program. Based on the research results, it is known that the overall indicator data with a result of 123.90 is categorized as very good. The size indicator of 12.16 is categorized as good, the price indicator of 15.92 is categorized as good, the appearance indicator of 15.43 is categorized as sufficient, the product availability indicator of 11.69 is categorized as sufficient, the guest satisfaction indicator of 34.35 is categorized as sufficient, the suitability of expectations indicator of 11.65 is categorized as sufficient, the indicator of interest in returning to visit is categorized as sufficient, the availability indicator is recommended 11.16 is categorized as sufficient. The results of this research also show that there is a significant influence of menu innovation (X) on guest satisfaction (Y). The Adjusted R square value of 0.406 indicates that these variables explain 40.6% of guest satisfaction, while the remaining 59.4% is influenced by other factors not examined in this research. Furthermore, F is 67.178 with a significance level of 0.000 <0.05 indicating a strong relationship between variables.

This research is motivated by the problem of the influence of aesthetic labor on positive emotions at Saung Labuan Sundai Resort. This type of research is quantitative with a causal associative form. The study was conducted at Labuan Sundai Resort. The purpose of this study was to evaluate the influence of aesthetic labor on positive emotions at Saung Labuan Sundai Resort. The population in the study was 2,070 people taking a sample of 94 respondents. Data collection through a questionnaire with a Likert scale. Using descriptive analysis and interval class score classification through the SPSS program version 26.00 based on the results of the study, it is known that the overall indicator data with a result of 102.94 is categorized as very good. The aesthetic nature indicator with a result of 12.84 is categorized as very good. The aesthetic requirements indicator 21.69 is categorized as very good. The service encounter indicator 16.94 is categorized as very good. The happiness indicator 10.88 is categorized as sufficient. The satisfaction indicator 14.52 is categorized as sufficient. The love and affection indicator 10.75 is categorized as sufficient. The interest indicator 15.37 is categorized as good. The results of the study also showed a significant influence of aesthetic labor (X) on positive emotion (Y). The Adjusted R Square value of 0.778 indicates that these variables explain 77.8% of positive emotion, while the remaining 21.2% is influenced by other factors not examined in the study. Furthermore, the F value of 326.449 with a significance level of 0.000 <0.05 indicates a strong relationship between variables.s.

The continued advancements in Internet of Things (IoT) and Machine Learning (ML) technologies have led to their adoption in various domains including in industries for predictive maintenance among other applications. Given the resource constraints of IoT devices, they cannot process the resource-intensive ML algorithms hence data collected by the devices are first sent to the cloud where the algorithms are hosted for processing and inference with the results being sent back to the devices for action and/or notifications. The need to transmit data to the cloud for processing leads to increased costs, energy consumption, and high latencies affecting the implementation of the solution. Interestingly with Tiny Machine Learning (TinyML), it is possible to develop algorithms enabling edge inference on resource-constrained devices. From existing review papers, the researchers were not able to find, a comprehensive review with a focus on this area showing the need for a targeted review that can shed light on how TinyML can be tailored for predictive maintenance tasks in industries. This study therefore presents a systematic literature review of the application of TinyML in predictive maintenance in industrial settings. TinyML overview and its benefits are presented, a TinyML process flow is proposed and various use cases and their classifications have been presented. Through this exploration, the study shows the critical need for TinyML-driven solutions in predictive maintenance, identifies the existing challenges, and proposes a roadmap for future research.

Knowledge is the result of curiosity through the process of perception, especially the sense of sight and hearing of certain objects. The D-III Medical Records and Health Information Study Program is a program that prepares students skilled in managing health information management. The aim of the research is to determine the competency description of Esa Unggul University Medical Records and Health Information students. Descriptive research method with a quantitative analysis approach. The population in this study were Esa Unggul University Medical Records and Health Information students from the 2020 and 2019 regular and parallel classes with a total of 94 students. Research results: the characteristics of the majority of students were female (73.6%), student age ≤ 22 years (63.7%), the largest campus base was regular Kebon Jeruk (51.6%), the largest class of 2020 (73.6%). The results for competency in Clinical Classification Skills, Codification of Diseases and other Health Problems, and Clinical Procedures showed that the percentage of competent was 51.3%, while the percentage of incompetent was 48.7%. Competency in the Application of Health Statistics, Basic Epidemiology and Biomedicine obtained a competent percentage of 49.9% while the percentage of incompetent was 50.1%. RMIK Service Management Competency obtained a competent percentage of 50.3% while the percentage of incompetent was 49.7%. Conclusion: The number of competent Medical Records and Health Information students was 55 students and 36 students who were not competent. Suggestion: Registration officers improve and understand more in serving and conveying more precise and clear information. It is recommended to maintain and improve the quality of learning and increase student learning motivation in order to increase the percentage of competency.

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).  

Safety Risk Identification and Handling of Auxiliary Engine Overhaul on Ship is a process that aims to identify potential risks and hazards associated with repairing or maintaining auxiliary engines on ships, as well as developing appropriate handling strategies to mitigate those risks.  Safety Risk Identification is carried out with several things, namely Safety Analysis, Hazard Identification, Risk Evaluation, Risk Classification. The handling that can be done in the axuliary egine overhaul is Planning, Education and Training, Use of Equipment and Technology, Supervision and Control, Evaluation and Repair. By identifying safety risks and implementing appropriate handling strategies, vessels can minimize the potential for accidents and injuries during the auxiliary engine overhaul process. This not only protects the ship's crew but also ensures the smooth operation of the ship overall. This study adopts a qualitative approach, conducted through verbal interviews with engine crews and direct observation during the implementation on board. The findings of the study show that safety risks and handling of auxiliary egine overhaul on ships are essential for identifying and maintaining repairs to ship engines.

The development of Jayapura City as the capital of Papua Province has grown into a region very rapidly from being a modern city. The classification of areas in Jayapura City is included in the heterogeneous area group where the types of population come from various tribes and regions that live in Jayapura City. The city of Jayapura has land and sea areas with different areas, and has residential characteristics as a traditional village. The settlements of Kampung Enggros and Kampung Tobati are located around the coast of Yotefa Bay, including in the South Jayapura District and Abepura District of Jayapura Municipality, where the community builds their settlements above sea water. One thing that residents will face is the relationship between humans and the places where they live, which cannot be separated from nature. The research methods used to achieve the objectives of this research are comparative analysis and qualitative methods. The comparative analysis method displays settlement patterns with comparisons between 2019-2023 in the form of images.  Qualitative descriptive analysis method by presenting actual settlement development problems in the field in the form of pictures. The research results show that there are 3 factors for settlement development in Enggros and Tobati Villages, namely, Community, Protection and Network. Society is changing from traditional to modern, for example traditional houses become modern houses that keep up with the times. The network turns into a spatial structure in a pattern that is influenced by the economic aspects of indigenous communities. The main thing is natural factors because in the general view of indigenous Papuans, what is meant by their home is the natural surroundings where they can live.

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.

Efficient data management is essential for organizations, especially in the face of large volumes of data. Digitization of archives through document collection, scanning, and manipulation can improve operational efficiency, reduce the risk of data loss, and facilitate information access. operational efficiency, reduce the risk of data loss, and facilitate access to information. This research focuses on implementation of a document classification system in BKPSDM Nabire using VBA and Microsoft Excel to improve efficiency of data processing. The method used is the waterfall model, which includes the needs analysis stage, system design, implementation, and testing. The results of the analysis show that manual document management has many weaknesses, such as difficulty in conveying information and lack of supporting applications. Solution The proposed solution is a Visual Basic and Excel-based system, which enables automatic document processing, improve data accuracy and consistency, as well as facilitate search and decision making. System testing using the black-box method shows valid results, ensuring that the system runs according to the expected functional specifications. functional specifications. The implementation of this system is expected to make a significant contribution in improving the efficiency, accuracy, and productivity of document management. efficiency, accuracy, and productivity of document management in BKPSDM Kabupaten Nabire.    

This study compares the performance of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) algorithms in detecting and classifying smoking activities. Using an image dataset containing two classes, Smoking and Non-Smoking, this research implements transfer learning using the InceptionResNetV2 model for CNN and the SVM method. Evaluation results show that CNN has higher accuracy compared to SVM in detecting smoking activities. This research contributes to the development of surveillance systems for smoke-free areas in smart cities.

This research was conducted with the aim for analyzing and mapping problems in the implementation of halal certification for micro and small enterprises (UMK) in Indonesia. This research uses a qualitative method with a literature study approach, namely by searching for articles via Google Scientist with the keywords halal certification problems, then reviewing and mapping the research findings obtained. The results of this research show that from the 12 papers reviewed, 4 (four) classifications of problems in the implementation of halal certification policies can be mapped, namely first, regulatory aspects, second, perceptions and behavior of MSE actors, third, knowledge aspects and fourth, support system aspects. The efforts put forward by researchers from the four problem maps are (1) carrying out evaluations and reviews of regulations, (2) carrying out comprehensive, systematic and measurable outreach and education efforts, and (3) improving existing infrastructure and personnel in the implementation of halal certification.

Nowadays, advances in information and communication technology have had a major impact on various sectors of life, including in the field of trade and e-commerce. However, as it happens in the scope of social media, TikTok Shop is also dealing with challenges and changes. One of the issues that is sticking out and is currently hot is the permanent closure of the TikTok Shop feature that has occurred in Indonesia. This research will carry out several processes starting with data collection, then data labeling, data preprocessing, data sharing, weighting training data, using the Naive Bayes method, and ending with testing. In this study, the results of the implementation that has been made or built are discussed. The method used is the Naive Bayes Classifier Method to classify training data as much as 800 data, which is 80% of the total data. Then, testing is carried out using 200 testing data, which is 20% of the total data. The evaluation results show an accuracy value of 73%. In addition to the accuracy value, this research also recorded the precision, recall, and F1 score values. The classification that appears most often and contributes the highest in these values is the Positive classification as much as 420 data or 42% of the total data used..