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Journal : International Journal Software Engineering and Computer Science (IJSECS)

Sentiment Analysis of Kredivo App Users Using the K-Nearest Neighbor Algorithm Saepudin; Sutisna
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3056

Abstract

In today's technological era, the internet has played an important role in all aspects of human life. This is also what drives various mobile applications to develop very rapidly. Kredivo is an instant credit solution that provides convenience to buy now pay later in a 1-month tenor or 3-month installment tenor with 0% interest. In addition, Kredivo is not only used for shopping purposes, but borrowers can also make withdrawals in the form of cash. However, not all users are satisfied with the service of the application. and the many comments submitted through the Kredivo application review feature on the Google Play Store. Therefore, in this study, researchers tried to conduct a sentiment analysis of Kredivo application users using the K-Nearest Neighbor algorithm. The purpose of this study was to determine the accuracy value produced by the K-Nearest Neighbor algorithm. From testing 1880 data using the cross-validation model, it was found that reviews containing positive sentiment were 62.55% and containing negative sentiment were 37.45%. Evaluation of the classification results using the Confusion Matrix test obtained an accuracy value of 79.36%, with a recall value of 83.08%, precision of 72.15%, and recall (Specificity) of 73.15%, so it can be concluded that the K-Nearest Neighbor algorithm can classify sentiments well using review data on Kredivo application users
Implementation of RFM Analysis to Enhance Sales Patterns of Food and Beverages at Bonjour Café and Resto Using the Apriori Algorithm Sibarani, Julvan Marzuki Putra; Akbar, Yuma; Sutisna; Setiawan, Kiki
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3073

Abstract

The rapid growth of the culinary business has made business competition in this field increasingly tight, so a strategy is needed to increase food and beverage sales patterns. Bonjur Cafe Resto serves many food and beverage menus, but business actors need to try to produce product innovations in order to provide satisfactory service to customers. In this condition, a data processing technique is needed to determine customer segmentation and menu recommendations at Bonjur Cafe Resto. The analysis method used is RFM Analysis by analyzing customer behavior, analyzing purchase transaction data consisting of Recency Frequency Monetary (RFM) attributes and data mining techniques with the Apriori algorithm, where this algorithm is used to determine the most frequently appearing data set (frequent itemset). The results of this study are grouped into five categories of customers based on their purchasing behavior and association rules are formed with predetermined parameters, support 28% and confidence 70%. This can later be a recommendation for a menu combination from the data that has been collected and applied using the apriori algorithm so that it is expected to be used for service evaluation and be able to increase customer satisfaction so that Bonjur Cafe Resto can develop better