Used Car Price Prediction in Surabaya Using Random Forest Regressor Algorithms

The use of private vehicles during the Covid-19 pandemic has increased because private vehicles, especially cars, are considered as the safest mode of transportation to maintain distance and prevent transmission of the Covid-19 virus. Based on data from two different Indonesian secondary car market place, a comparison of a price sample of Car X in the city of Surabaya with the specifications for the 2015 to 2018 car years with car milage under 1000 kilometers, the used cars have a variety of prices hence a used car price prediction system is needed so that people can find out the average price of used cars sold in the market. In this study the author will use the Random Forest Regressor as a machine learning algorithm to predict the price of a used car with a dataset from the AtapData website. The reason for choosing the Random Forest Regressor is because the algorithm has the power to handle large amounts of data with high dimensions with categorical and numerical data types. The evaluation method used in this study is the Root Mean Absolute Error which produces a value of 0.55612 for validation data and 0.56638 for testing data, while the evaluation proceed with Mean Absolute Error produces a value of 0.45208 for validation data and 0.47576 for testing data. © 2022 IEEE.

USED CAR CLUSTERING in BANDUNG CITY USING K-PROTOTYPES ALGORITHMS

Since the case of the 2019 Coronavirus Disease pandemic or commonly referred to as Covid-19, the use of public transportation has slowly begun to become an option as transportation to reduce the spread of the corona virus cluster, therefore some people prefer to buy private vehicles. However, due to the increasing price of cars, some people prefer to buy used cars. On the used car buying and selling platform, OLX Autos Indonesia, the demand for used cars increased by 15% to 20%. Therefore, this study was conducted to determine the characteristics of the cluster formed from the used car sales dataset taken from AtapData (atapdata.ai). AtapData is an open data site in Indonesia that can be used for research related to Data Science. This cluster model was created using the K-Prototypes algorithm, Silhouette Score and Davies Bouldin Index to evaluate the resulting cluster results. This clustering model will produce three clusters. The results of the three clusters will have one thing in common, namely brands that dominate sales, including Toyota, Honda, Daihatsu, Nissan, and Mitsubishi. Clustering evaluation using the Silhouette Score method produces a value of 0.7744140503593034. And for the evaluation of the Davies-Bouldin Index it produces a value of 0.4999221950856398. © 2022 IEEE.

Comparison of K-Nearest Neighbor and Logistic Regression Algorithms on Sentiment Analysis of Covid-19 Vaccination on Twitter with Vader And Textblob Labeling

In this analysis, the methods used are the K-Nearest Neighbor classification method and the Logistic Regression classification method with data taken on the twitter application. This study examines the level of accuracy in public sentiment regarding covid-19 vaccination with positive and negative labels. The AUC value in the KNN algorithm with TextBlob labeling is 0.765 with and 0.76S for VaderSentiment labeling are both included in the fair classification criteria. Meanwhile, the Logistic Regression algorithm produces an accuracy of 84.97% with a ratio of 90:10 for Labeling TextBlob, while for Labeling VaderSentiment with a ratio of 90:10 results in an accuracy of 85.22%. Both algorithms are validated using K-Fold Cross Validation with a fold count of 10. The comparison results obtained when conducting an evaluation with the confusion matrix showed that the Logistic Regression algorithm with VaderSentiment labeling had the highest accuracy value compared to the K-Nearest Neighbor algorithm with TextBlob and VaderSentiment labeling. © 2022 IEEE.

Design and Implementation of User Interface and User Experience in Online Sales Applications At Sugosha Pharmacy With User Centered Design Method

The health sector, such as pharmacies, is currently growing in line with technological developments from year to year. The existence of online drug distribution proves the development of the health sector, especially pharmacies. Circular Letter Number HK.02.01/MENKES/303/2020 to prevent the spread of COVID-19 from supporting online drug distribution, increase sales and prevent the spread of COVID-19, of course, needs to be done in all pharmacies, including Sugosha Pharmacy, which currently does not have a drug sales application. Based on these problems, Sugosha Pharmacy requires an online sales application, namely an e-commerce website. User interface and user experience are needed to design an e-commerce website to look attractive and easy to use. To design and implement the design, user-centred design is used. User-centered design is an iterative design process that requires designers to focus on users and their needs at every phase of the design process. This research produces the user interface and user experience of the Sugosha Pharmacy e-commerce website for sellers and buyers and a front-end for the Sugosha Pharmacy e-commerce website for sellers. System Usability Scale used to test the user interface design and user experience of the Sugosha Pharmacy e-commerce website. The Usability Scale system results in the seller's part design being used well and the buyer's part design being used very well. Then for the front-end of the e-commerce website, black box testing is carried out, producing features that run according to the design.

Analysis of Student Learning Pattern in Learning Management System (LMS) using Heuristic Mining a Process Mining Approach

The COVID-19 pandemic has had a significant impact on educational organizations, one of which is the Telkom University educational institute. Telkom University using Learning Management System (LMS) during pandemic covid-19. The use of an LMS will have a huge number of data records stored in the form of event logs. Opportunities to process data recorded in event logs must be maximized to improve Telkom University's LMS continuously. In this study, the process mining method conducted using event logs from Website Application Development (WAD) and Enterprise System (ES) courses to compare student learning patterns between programming courses and non-programming courses. Process mining conducted using PROM 5.2 and the Heuristic Miner plugins. The modeling process uses a heuristic miner algorithm because of its ability to present the main behavior recorded in the event log well by analyzing conformance checking. The result of conformance checking shows a fitness value of 0.887 for WAD and 0.847 for ES. It means the process model can represent the event log well. WAD's value of advanced behavioral appropriateness and degree of model flexibility are 0.594 and 0.411, whereas ES 0.181 and 0.155. The value of the structure shows 1.0 for the two subjects. After obtaining a process model, the next step is to analyze student learning patterns based on the frequency of access to the LMS. With this research, it is hoped that this research can contribute to adding new insights regarding the use of event logs in the field of education and knowing the activities carried out by students through LMS learning media, as well as providing visualization of student behavior patterns in the LMS (Learning Management System) of Telkom University. © 2021 IEEE.