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Journal : Borobudur Informatics Review

Prediction of material requirements for network construction using apriori algorithm Layli Nur'Aini; Mukhtar Hanafi; Emilya Ully Artha
Borobudur Informatics Review Vol 2 No 1 (2022): Vol 2 No 1 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/binr.5824

Abstract

PT XYZ is one of the providers of fixed broadband development services spread throughout Indonesia. What often happens is the lack of material availability and the long pre-order process, thus hampering projects that have been agreed upon for completion. The a priori algorithm is one of the data mining algorithms in the association method, which is looking for relationships between interrelated items. This research uses the help of RStudio software. This prediction is expected to help to prepare material stock in the warehouse so as to prevent material vacancies in the warehouse. The results of this study are in the form of a rule that meets the minimum support value (a measure that shows how much dominance an item/item set is from the entire transaction) of 27.84%, minimum confident (a measure that shows the relationship between 2 items conditionally) of 27.84%. 84.48% and the lift ratio (a measure to see whether or not the association rules are formed) is > 1.
Grouping community reading interests using the k-means clustering method (case study: Magelang district library and archive service) Achmat Mujafar; Mukhtar Hanafi; Maimunah Maimunah
Borobudur Informatics Review Vol 2 No 2 (2022): Vol 2 No 2 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/binr.6810

Abstract

So far, the problem of gathering information at the Library and Archives Office of Magelang Regency is relatively low. To increase reading interest, policies are needed to determine reading interest. So the data used is book transaction data. This study aimed to classify people's reading interests according to the number of borrowed books at the Magelang Regency Library and Archives Service using the K-Means Clustering method and to find out which book categories are most in demand by the public at the Magelang Regency Library and Archives Service. One way to manage this data is to use data mining using the K-Means method. The results of this study are low reading interest, evidenced by using 2 clusters and the category of books that are most in-demand Literature with a high cluster strength value, namely with a Silhouette Coefficient value of 0.7354.