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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Putri, Margareta Amalia Miranti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders Putra, Rizki Aulia; Putri, Margareta Amalia Miranti; Sinaga, Sri Maharani; Octavia, Sania Fitri; Rachman, Raihan Catur
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 2: PREDATECS January 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i2.863

Abstract

Association rule is a data mining technique to find associative rules between a combination of items. This research aims to apply association rules algorithm in identifying popular topping combinations in food orders. This application aims to help restaurant owners or food businesses understand their customers' preferences and optimize their menu offerings. Data obtained from kaggle, the association rules algorithm is applied to this dataset to identify patterns or combinations of toppings that often appear together in orders. The results of this study show toppings with chocolate as a popular item in orders. These findings can provide valuable insights for food business owners in structuring their menus and determining attractive offers for customers. This study also applied a comparison between the apriori, fp- growth and eclat algorithms, with the result that the best item transaction rule was found: a combination of dill & unicorn toppings with chocolate with 60% confidence. Overall, the application of eclat algorithm in this study provides the best performance with higher execution speed, thus providing insight into customer preferences regarding topping combinations in food orders. Despite the shortcomings of the data form from this study, it is expected to help business owners in optimizing their offerings, increasing customer satisfaction, and improving their business performance.
Application of the Supervised Learning Algorithm for Classification of Pregnancy Risk Levels Dwinnie, Zairy Cindy; Khairani, Luthfia; Putri, Margareta Amalia Miranti; Adhiva, Jeni; Tsamarah, Muhammad Inas Farras
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 1 No. 1: PREDATECS July 2023
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v1i1.806

Abstract

MMR is the number of women who die due to disorders during pregnancy or their treatment (excluding accidents, suicides, or incidental cases) during pregnancy, childbirth, and during the puerperium or 42 days after giving birth. This research aims to classify pregnancy risk datasets, namely to compare the performance of the NBC, K-NN, and SVM methods on the pregnancy risk status dataset and to find out the accuracy comparison of the algorithm results above. From the results of the analysis, it was found that of the three algorithms it resulted in a classification of pregnancy risk levels with the highest value occurring at a high level. To determine the accuracy of the data, a comparison was made between the three algorithms. Based on the confusion matrix namely Accuracy, Precision, and Recall. The results of the comparison can be concluded that the KNN algorithm provides the highest accuracy of 77.55%, NBC of 69.39%, and the lowest accuracy by SVM of 67.35%. These results state that the KNN algorithm classifies pregnancy risk level data better than the other two algorithms
Co-Authors Adhiva, Jeni Dwinnie, Zairy Cindy Khairani, Luthfia Octavia, Sania Fitri Putra, Rizki Aulia Rachman, Raihan Catur Sinaga, Sri Maharani Tsamarah, Muhammad Inas Farras