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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..    

This research aims to classify news headlines into clickbait and non-clickbait using the Multinomial Naive Bayes method. The data used comes from the dataset CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines. The research process involves stages of data collection, preprocessing, feature extraction, model training, model evaluation, and result analysis. The test results show that the Multinomial Naive Bayes algorithm consistently produces an accuracy rate of around 78%. Optimization using Grid Search did not result in an accuracy improvement. However, there was an improvement in the recall value for the non-clickbait class from 76% to 80%. The best parameter found was an alpha of 0.15. Therefore, the Multinomial Naive Bayes algorithm can be effectively used to address the problem of classifying clickbait news headlines, with the potential to contribute to clickbait prevention efforts in the future.

The insurance business within an insurance company offers insurance products owned by the insurance company. In every insurance product there is a premium payment and the premium is the income of an insurance company at the rate of the amount insured. The problem that PT BNI Life Insurance has is that there are many stops in premium payments such as policy redemptions due to errors in the benefits received or incorrect selection of the insurance product, this can reduce the achievement of targets for an insurance company. The aim of this research is to find out the best classification algorithm compared between K-Nearest Neighbor and Naive Bayes to predict the type of insurance product that customers will choose. In this research, data mining methods are applied to compare two different methods, namely the K-Nearest Neighbor method and the Naïve Bayes method. The level of accuracy results for the K-Nearest Neighbor method is 80% and the Naïve Bayes method is 70.53%, which means that the K-Nearest Neighbor method is the best method to apply to an insurance product classification system based on the demographics of prospective customers.

The process of identifying the ripeness level of cayenne peppers is an important step in cultivation and post-harvest handling. Dependence on the quality factors of farmers, such as visual diversity and differences in ripeness perception, results in subjective harvest outcomes. This manual process is also prone to inconsistent results, as humans have time limitations, fatigue, and sometimes lack concentration when sorting for long periods. To minimize these issues, technological intervention is needed to mechanically classify the ripeness level of cayenne peppers. This research aims to develop a classification model for the maturity level of cayenne pepper plants. This research proposes the use of the CNN method for feature extraction and KNN for data classification based on the features extracted by CNN. From the test scenarios carried out, the classification carried out by KNN based on CNN feature extraction got the best accuracy of 99.33%, while the CNN classification model got the best accuracy of 87.33%.

The main objectives are to identify emotion patterns hidden in K-Pop music based on audio features extracted from the Spotify API and to build an emotion classification model that can predict the emotions of K-Pop songs.In this approach, the K-Means algorithm is used to cluster K-Pop songs based on audio features such as energy, valence, tempo, danceability, and speechiness. The clustering results reveal several main groups that represent variations in musical characteristics and emotions. Next, the C4.5 algorithm was used to build an emotion classification model based on the clustering results. The C4.5 model showed high performance with accuracy reaching 99.48% on a 90:10 dataset split, 99.21% on an 80:20 split, and 98.95% on a 70:30 split.The Streamlit application was developed to visualize emotion predictions from K-Pop songs with a web-based user interface. In addition, Ngrok was used to provide remote access to this application, allowing users to test and use the application remotely.The results of this study show that the combination of K-Means and C4.5 can effectively cluster and classify emotions in K-Pop music, providing valuable insights into the musical characteristics that influence emotions. This application has the potential to be used in further analysis, development of intelligent features in music applications, and improvement of user experience in listening to K-Pop music.

This study aims to apply the K-Nearest Neighbors (KNN) algorithm to predict stunting status in young children based on height and weight data. Stunting is a growth failure condition caused by chronic malnutrition that negatively impacts children's physical and mental development. The dataset includes height, weight, and stunting status of children. The results show that the KNN model with k=3 achieved 100% accuracy on the test data. Evaluation using the confusion matrix and classification report indicates perfect precision, recall, and F1-score for each class. Data normalization with StandardScaler improved the model's performance by ensuring all features are on the same scale. The KNN algorithm proves to be a simple yet effective method for predicting stunting, demonstrating significant potential for early detection and health intervention in children. This study recommends using a larger and more diverse dataset, as well as incorporating additional relevant features to enhance model accuracy. Implementing the model in a web or mobile application is also suggested to assist healthcare professionals in the field.

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.

Indonesia is a tropical country, yet its highlands can produce one of the economically valuable types of subtropical fruit, one of which is the strawberry. Strawberry plants are highly profitable fruit plants and come in many variations. Their small and attractive shape, along with their sweet and fresh taste, make these fruits appealing. In Indonesia, strawberry farmers perform interspecies crossings, leading to the growth of many varieties. Indonesia, particularly West Java, is one of the environments suitable for strawberry growth, with Lembang in West Bandung Regency being a prime example. Therefore, this research aims to compare plant cultivars by combining the morphological characteristics of three varieties obtained from two locations, La Fressa and Bukit Strawberry, Lembang. The method used for identifying the diversity of strawberry plant characteristics is the FGD (Focus Group Discussion) method, which is then described specifically according to the morphology of the obtained plant varieties.      

In the era of rapid digitalization, understanding consumer behavior through data is becoming increasingly important for retail businesses. Shopping trends, such as those contained in this study, provide in-depth insights into various aspects of consumer behavior, from demographics to purchasing preferences and patterns of discount usage. This data is invaluable in formulating effective marketing strategies, improving customer experience, and optimizing business operations. The data used in this study included a variety of relevant variables, such as age, gender, location, product categories purchased, number of purchases, payment methods, and frequency of purchases. This information allows for a comprehensive analysis of how these factors affect consumer spending decisions. For example, analytics can reveal seasonal trends in purchases, product color and size preferences, and the impact of discounts and promo codes on sales volume. In addition, this dataset also reflects the changes in consumer behavior that have occurred over the past few years. Quantitative methodology is a research approach used to collect and analyze numerical data to understand patterns, relationships, and events in a given population. Data is collected from various sources such as online sales transactions, consumer surveys, Naive Bayesian algorithms are applied to the dataset that has been processed. The data was divided into two sets: training (80%) and testing (20%).