Sentiment analysis is currently widely used in natural language processing or information retrieval applications. Sentiment analysis analysis can provide information related to outstanding financial news headlines and provide input to the company. Positive sentiment will also have a good impact on the development of the company, but negative sentiment will damage the company reputation. This will affect the company development. This study compares machine learning methods on financial news headlines with n-gram feature extraction. The purpose of this study was to obtain the best method for classifying the headline sentiment of the company financial news. The machine learning methods compared are Multinomial Nai¨ve Bayes, Logistic Regression, Support Vector Machine, multi-layer perceptron (MLP), Stochastic Gradient Descent, and Decision Trees. The results show that the best method is logistic regression with a percentage of f1-measure, precision, and recal of 73.94 %, 73.94 %, and 74.63 %. This shows that the n-gram and machine learning features have successfully carried out sentiment analysis.

The world today has a rapidly growing human population whose daily needs are certainly increasing. Supermarket is a place to fulfill daily needs. When going to the supermarket, we have to spend a lot of time both shopping and also queuing at the cashier. This turns out to be a problem for customers because it can take up customers time which is also experienced by one of the largest supermarkets, namely Hypermart. Not only a problem for customers, but it can also create a threat to Hypermart companies. However, with advances in technology and information systems, the world is growing to adapt to current conditions, namely by improvising. The improvisation carried out in this research is the payment transaction business process at Hypermart, namely by implementing the Self-CheckOut system by replacing the old trolley with a "Hyper Smart Cart" as an improvisation which will certainly answer all existing problems.

CS is one of the most important functions of any client-related organization, whether a business or a school (customer service). Notably from the committee responsible for student selection, CS, on the other hand, has a very limited capacity to be handled by humans, which can reduce university satisfaction. Therefore, we require technological assistance, which in this case takes the form of an AI-based chatbot. The objective of this study is to design and develop a chatbot system utilizing NLP (natural language processing) to aid the CS of the new student admissions committee at Pahlawan Tuanku Tambusai University in answering questions from prospective new students. The employed method is dice similarity weighted by TFIDF. The results of the conducted tests indicated that the recall rate was 100 percent and the precision reached 76.92 percent. The evaluation results indicate that the chatbot can effectively respond to questions from prospective students.

This study aims to determine and analyze the effect of service quality on customer satisfaction. The analysis in this study uses the independent variable of service quality. The dependent variable is customer satisfaction. The sample of this research is the students of the Master Institute. The sample is carried out using a non-probability sampling technique, which is sampling that does not provide equal opportunities or opportunities for each member of the population to be selected as a sample. Data collection techniques were carried out by means of questionnaires and interviews. The total population at the Master Institute is 3004 and the sample obtained is 97 people. The results obtained that the t value is known to have a t-count of 7.920 > t-table 0.199, so it can be concluded that the service quality variable (X) has a significant effect on the consumer satisfaction variable (Y). The results obtained that the R2 value of the coefficient of determination is found in the Adjusted R Square of 0.391. This means that the ability of the independent variable to explain the dependent variable is 39.1%, the remaining 61.9% is explained by other variables not discussed in this study.

Tomatoes (Lycopersiconeculentum Mill) are vegetables that are widely produced in tropical and subtropic areas. According to (Harllee) tomatoes are grouped into 6 levels of maturity, namely green, breakers, turning, pink, light red, and red. One way that can be used to classify the level of maturity of tomatoes in the field of informatics is to utilize digital image processing techniques. This study classifies the maturity of tomatoes using K-Nearest Neighbor (KNN) based on the Red Green Blue and Hue Saturation Value color features. The KNN algorithm was chosen as a classification algorithm because KNN is quite simple with good accuracy based on the minimum distance using Euclidean Distance. The research conducted received the highest accuracy result of 91.25% at the value of K = 7 with the test data 80. This shows that the KNN algorithm successfully classified the maturity of tomatoes by utilizing the color image of RGB and HSV.

This study aims to determine and analyze the effect of ownership structure and debt policy on firm value. Effect of ownership structure on firm value with debt policy as an intervening variable. The population in this study were LQ45 companies in the form of financial reports downloaded from www.idx.co.id, namely 45 companies. then the companies studied were lq45 companies as many as 27 companies. The analytical method used is path analysis. The results of this study indicate that ownership structure affects firm value. Furthermore, ownership structure influences firm value with debt policy as an intervening variable. ownership structure does not affect debt policy. Debt policy does not affect the value of the company.