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All Journal International Journal of Social Science Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Rini Astuti
STMIK LIKMI Bandung
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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GROUPING OF SEWING TOOL ASSISTANCE RECIPIENTS USING K-MEANS CLUSTERING ANALYSIS Nana Suarna; Herman; Nining Rahaningsih; Rini Astuti; Yudhistira Arie Wijaya
International Journal of Social Science Vol. 2 No. 2: Agustus 2022
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/ijss.v2i2.3085

Abstract

Aid programs for underprivileged communities need continuous data collection in overcoming welfare problems, what has happened so far is by providing direct assistance to very poor families in every village in Indonesia. Related to this, the problem that has occurred so far is that the direct assistance program is not right on target, because many deserving families should not receive the assistance. The data obtained from the village government is not accurate, it is found that data is considered invalid. This study aimed to determine the distribution of sewing equipment recipients in the best cluster, for sewing equipment recipients in Greged Village, Cirebon Regency. As one way to improve data accuracy, a computational method or model is needed in the form of a data mining algorithm using the k-means clustering method to generate priority groups among hundreds of citizens or the poor. The stages start from data collection, training data, and testing data that consider several criteria from household information, economic conditions, housing conditions, and the number of household members in Greged Village, Cirebon Regency. The results of the tests carried out using 155 data with the best level of accuracy were in the K3 cluster with the Davies Bouldin Index’s value o: - 0.584. With the K-Means method, it is very appropriate to determine the recipient of the sewing equipment program in Greged Village.
Analysis of Beverage Sales Data Using the FP-Growth Algorithm at Sini Aja Cafe Widisa Adi Kumara; Rini Astuti; Willy Prihartono; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.772

Abstract

The growth of information technology and data mining techniques has greatly helped analyze consumer purchasing behavior, particularly in marketing and inventory management. This study aims to uncover association patterns between products frequently bought by customers at Sini Aja Cafe and to measure these patterns' support and confidence values. The research uses Knowledge Discovery in Databases (KDD), including stages like data selection, preprocessing, transformation, applying the FP-Growth algorithm, and interpreting results. Data from 1,083 beverage sales transactions at Sini Aja Cafe from August 1 to October 31, 2024. The findings reveal five significant association rules when applying a minimum support of 0.1 (10%) and confidence of 0.3 (30%). Notably, if customers buy Red Velvet Oreo, there is a 56% chance they will also buy Thai Tea. Thai Tea sales dominate with a support value 0.557 (55.7%). The support values of the association rules range from 0.141, categorized as medium, and the confidence values range from 0.235, categorized as low. These findings offer valuable insights for the cafe owner to optimize operations, enhance customer satisfaction, and increase profits.
Co-Authors Herman Nana Suarna Nining Rahaningsih Tati Suprapti Widisa Adi Kumara Willy Prihartono Yudhistira Arie Wijaya