Based on observations made by researchers, FinTech which is common and is being used by the people of Indonesia is the first generation for payment, purchase of goods and services, one of which is the Fund Application. In choosing the application to be used, it is usually considered, comfort, safety, accuracy of the transaction, comfort, and various promotions. But some users are hesitant in using the application because of some reviews from the application that show positive and negative ratings. With the number of reviews paid in the comments column provided by the Google Play Store in the Funds Application needed to classify the reviews given as positive or negative. The research used was an experimental method using the Naive Bayes Classifier Algorithm and K-Nearest Neighbor. Regarding testing on the Fund Application has the best testing value of 84.76%. Then it can be considered a review that can be approved from positive reviews by other users and if there are negative reviews it will be input to the company to develop and develop the product.

Determining the status of poor families as recipients of assistance is very important so that poverty reduction assistance from the government can be channeled on target. Data mining utilizes experience or even mistakes in the past to improve the quality of the model and the results of its analysis, one of which is the ability possessed by data mining techniques, namely classification. The purpose of this study was to test K-Fold Cross Validation in the K-Nearst Neighbors algorithm in predicting receipt of village aid funds. In the beneficiary dataset used in this study, there were 159 records or tuples with four attributes (house condition, income, employment and number of dependents). The new data category prediction is done by using the Euclidean Distance manual calculation stage of five different K values. While using the Rapidminer application aims to test the accuracy of the dataset in five different K values. The results show that with K=15 and K=30 the new data (D160) has a "Not Eligible" category with an accuracy of 100%. Then with K=45, K=60 and K=75, the new data (D160) has the category "Eligible" with an accuracy rate of 81.25%.

An online marketplace site is a shopping place that is currently popular with the community because it offers a variety of convenience and one of the marketplace apps is Shopee. Some people are satisfied with the service provided by the Shopee app. But unisex some people who give complaints about this application. User-provided response to Shopee app in the Comments field of Shopee Google Play Store can be analyzed for negative and positive sentiments. This research aims to assist Shopee’s management of the positive or negative opinions of application users and can provide empirical evidence for related theories so that it can be used as a donation of thought for the development of theories Next. With the number of reviews shown, you need an analysis that can classify these reviews into positive or negative classes. The method used for the sentiment analysis of Shopee app reviews is the Naive Bayes algorithm obtaining an accuracy yield of 96,667%

At this time the development of information technology is growing very rapidly. Directly or indirectly affect various aspects of life, including the business world. Almost all aspects have used computers as a tool to provide convenience for companies. UD Dwi Surya Aluminum and Glass Yogyakarta need an information system that support and provide satisfactory service for the customer. For that the author tries to make Thesis web-based sales information system on UD Dwi Surya Aluminum and Glass which until now has not been computerized. Sales system running on the UD is still conventional and data processing inside the UD is still manual, such as recording payment transactions and making reports that are still recorded in a book. The data storage in the book is considered less effective because of the data search process that takes a long time and often error recording due to human error. And Marketing of goods made UD Dwi Surya Aluminum and Glass around the scope of Yogyakarta. The design of the sales information system in this UD uses the waterfall method. With this sales website is the best solution to solve existing problems in this company, as well as with this system can improve sales service, especially on product offerings and facilitate in processing sales data that exist in the UD. A computerized system is better than a manual system, a sales system that is now more conducive than the previous system.

A new paradigm emerged in the field of education along with the development of increasingly advanced technology, namely the emergence of an electronic learning system known as e-learning. One of the e-learning media that is currently popular in Indonesia is Ruangguru online learning application that can be accessed through smartphone. The purpose of this study is to analyze the factors that influence student’s acceptance of Ruangguru application using Technology Acceptance Model (TAM) method. The 5 (five) constructs of the TAM research model were used, namely Perceived Usefulness, Perceived Ease of Use, Attitude Toward Using, Actual System Use, and Behavioral Intention of Use. The data collection used is an online questionnaire with purposive sampling technique. The results show that Attitude Towards Using did not have a significant effect on Behavioral Intention of Use, meaning that the students’ attitude towards using of did not affect students' intentions in using Ruangguru application. At the same time, the relationship of Perceived Ease of Use to Perceived Usefulness is the most significant influence where the ease in using Ruangguru application makes users feel that Ruangguru application is useful.