One form of information technology advancement is the use of AI in website development, one of which is ChatGPT website. ChatGPT has a poor user experience in various aspects, so it is necessary to evaluate the user experience on the ChatGPT website using User Experience Questionnaire (UEQ). The attractiveness variable obtained a positive evaluation value of 1.503. The perspicuity variable obtained a positive evaluation value of 1.661. The efficiency variable obtained a positive evaluation value of 1.615. The dependability variable obtained a positive evaluation value with an overall average value of 1.286. The stimulation variable obtained a positive evaluation value of 1.182. The novelty variable obtained a positive evaluation value of 0.942. The ChatGPT website has shown good quality because it has a positive evaluation value from user assessments. However, in the attractiveness, dependability, and novelty variables, there are still several items that get neutral ratings. So product improvements are still needed to increase user satisfaction.

This study aims to evaluate the quality of Mobile JKN application services using 6 dimensions of E-GovQual, and provide recommendations for improving service quality based on the results of Importance Performance Analysis (IPA) analysis. This research uses a quantitative descriptive method. Questionnaires were distributed by visiting BPJS Kesehatan Tigaraksa Branch Office and Metro Hospitals Cikupa. Then, 110 respondents were obtained who had filled out the questionnaire online via Google Forms and were included in the category of Mobile JKN application users. The questionnaire instrument test was conducted on 55 respondents to test its validity and reliability. Data processing was carried out by conducting descriptive statistical tests, R-Square tests, hypothesis tests (significant), as well as gap analysis (GAP) and Importance Performance Analysis (IPA) quadrants. The results of the coefficient of determination (R-Square) show that overall, the variables of ease of use, reliability, trust, content and appearance, functionality and interaction and citizen support significantly affect service quality by 88.6%. Based on the performance gap value (GAP) and assessment expectations of -0.19, there is a main priority scale in quadrant A to make improvements, namely on the information displayed in the Mobile JKN application is precise and detailed (EF 4), all features in the Mobile JKN application function properly (FI 1), the Mobile JKN application has displayed all the information needed by users (CA 1), information about the scope of health and information has provided clarity (CA 2), the accuracy of the summary of information data and graphics and colors in the system (CA 5), and BPJS Kesehatan staff show a responsive attitude in solving my problem (CS 1). These recommendations are top priorities for improving the service quality of the Mobile JKN application.

The significance of the tourism sector in contributing to the economy of the Special Region of Yogyakarta, which is a major destination with millions of tourists every year. The rapid growth of tourism demands the integration of information technology, especially in the development of recommendation systems, to improve visitor experience and satisfaction and support sustainable destination management. The main problem faced is the need to develop a recommendation system that is able to provide optimal suggestions for tourist locations and accommodations, considering various attributes such as price, facilities, location, and user personal preferences. The problem-solving approach taken in this study is the application of the Multi-Attribute Utility Theory (MAUT) Method. The research steps involve identifying problems, needs, and key attributes, collecting tourist location and accommodation data through Google Maps and Google Business Data. Furthermore, the raw data is subjected to processing and pre-processing before the MAUT model is developed. This model is implemented into a web-based application which is then tested using blackbox testing to determine whether the system is running properly. With the hope of creating a smart and sustainable tourism ecosystem, this study is innovative in using MAUT to improve the quality of recommendations in the Yogyakarta tourism industry.

PT. Telkom Akses (PTTA) has constraints in the employee leave application management system, this is because the employee leave application process is still done manually and difficulties in determining the level of importance for employees who apply for leave. Therefore, a web-based application was built using FMCDM and the SAW Method. This study aims to improve efficiency and effectiveness in managing the employee leave application system by utilizing the Fuzzy Multi-Criteria Decision Making (FMCDM) approach using the Simple Additive Weighting (SAW) method at PT. Telkom Akses (PTTA). In order to make it easier for employees to carry out the deduction process and obtain complete and detailed information and rules related to deductions, the personnel department is expected to be able to create an online information system. To help with this, one of the fields of technology that can be utilized is soft computing technology. There are various methods that can be used to solve problems, one of which is the Fuzzy Multi Criteria Decision Making (FMCDM) method and the Simple Additive Weighting (SAW) method. The results of the study indicate that the web-based application that has been designed can help solve the problems faced by PT. Telkom Akses Medan STO Simpang Limun in terms of managing the employee leave application system and helping to determine the level of importance and ranking for employees who apply for leave accurately and efficiently, and the results of the application of FMCDM and the SAW Method in determining employee leave permits are that out of 7 employees who apply for leave, 2 employees get the highest rating results. The conclusion of this study is to help decision makers in determining employees who get leave permits based on relevant and transparent assessments of each employee who applies for leave.

Fulfilling nutritional needs with the Recommended Dietary Allowances (RDA) shows the average value of the number of vitamins, protein and other nutrients the body needs to function properly. Each person's nutritional needs are different, so the RDA can be calculated based on different age groups and gender. In general, there are still many people who do not know the nutritional value of food and consume food without considering whether it meets the body's needs. This usually happens because calculating the RDA value is not yet familiar to do. Efforts are needed to increase awareness about the importance of a balanced diet through RDA calculations. Calculation and determination of nutritional status using the RDA number serves as a measuring tool to monitor whether a person's nutritional intake is in accordance with daily needs. This research developed a web-based application to calculate RDA numbers and group RDA numbers into nutritional status of less, enough, or more. Determination of nutritional status is carried out using the fuzzy tahani method, by displaying the results in percentage form, so that users can easily see the proportion or percentage of nutritional status obtained. This application not only calculates nutritional values, but provides a food record feature to help users manage healthy eating patterns.

Diabetic retinopathy is a serious complication of diabetes that can lead to blindness if not detected and treated early. Automated detection of diabetic retinopathy requires effective feature extraction techniques to enhance diagnostic accuracy. This study aims to develop a method for detecting diabetic retinopathy by utilizing Local Binary Pattern (LBP) combined with wavelet transform, and then classifying the extracted features using Support Vector Machine (SVM). The approach includes feature extraction from retinal images using LBP and wavelet transform. The extracted features are subsequently classified with SVM to evaluate performance in detecting diabetic retinopathy. Analysis results show that the dominant feature is found in the fifth row with a value of 0.57006, indicating the effectiveness of the LBP method in feature extraction. The developed model demonstrates high performance with an accuracy of 95.59%, precision of 96%, recall of 97.96%, and F1-score of 96.97%. The combination of feature extraction methods with SVM proves to be effective and reliable in detecting diabetic retinopathy, offering low error rates and high accuracy, thus potentially serving as a valuable tool in clinical diagnosis

Batik is one of the cultural heritages that must be preserved and developed. every business actor must experience very tight competition conditions, namely in the batik clothing business. Customer satisfaction is a very important aspect to pay attention to in order to increase business profits. The purpose of this research is to make it easier for Batik Berjaya Labuhanbatu Utara to find out what are the factors that affect customer satisfaction for the future development of Batik Berjaya Labuhanbatu Utara's business, and to find out the rediction results by looking at the accuracy of Naïve Bayes so that Batik Berjaya Labuhanbatu Utara can meet customer satisfaction. This research uses the Naive Bayes Algorithm Method whose results can later facilitate batik business managers in making decisions and improving the quality of products and services provided to customers. By calculating the classification results using the Naive Bayes algorithm with a total of 132 data, as a manual calculation, 30 data are used, namely, 80% of the training data totaling 24, 20% of the test data totaling 6 obtained accuracy 83.33%, precision 100%, recall 75%, specifity 100% and f1-score 85.71. Using the Naive Bayes algorithm helps classification, data in the form of attributes and labels are held training data and test data, where training data labeling must be determined at the beginning in the form of categorical information, namely satisfied and dissatisfied, then predictions are made based on the highest data to get satisfied and dissatisfied labels from test data.

The leading cause of human death is heart disease (HD) worldwide which generally occurs when the heart is unable to push enough fresh, oxidized blood to the rest of the body. This disease makes it more difficult for the heart muscle to pump blood efficiently and causes chest pain, chest pressure, shortness of breath, pain in the neck and jaw. The aim of this research is to compare and obtain the best accuracy results from the three methods used, namely Random Forest, Extra Trees Classifier and Naïve Bayes. The results of this research prove that the Extra Trees Classifier method with an accuracy of 86.93% has higher results compared to the Naïve Bayes method with an accuracy of 84.21%, and the Random Forest Classifier method with an accuracy of 84.21%. Meanwhile, the AUC results obtained by the Extra Trees Classifier method are higher than other methods with an AUC of 93.81%.

This research compares the performance of two sentiment analysis algorithms, namely VADER (Valence Aware Dictionary and Entiment Reasoner) and RoBERTa (Robustly Optimized BERT Pretraining Approach), using a dataset of public opinions regarding climate change on twitter. Analysis is carried out to determine the sentiment distribution of the tweets described, whether they are positive, negative or neutral. In addition, this research identifies the keywords that appear most frequently from the collection of tweets that have been analyzed. Time series analysis was also carried out to see the distribution of sentiment over 12 months. The relationship between the two models was evaluated using matrix scatter plot analysis for tweets per two months, to assess the correlation and consistency of sentiment results between VADER and RoBERTa. The results show that VADER is more effective in situations that require rapid responses to changes in public sentiment, while RoBERTa is superior in in-depth analysis of more complex and ambiguous content.