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All Journal Gunung Djati Conference Series
Acep Razif Andriyan
Teknik Informatika, UIN Sunan Gunung Djati Bandung
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Determination of Film Recommendations using the Generalized Sequence Pattern (GSP) Association Method Acep Razif Andriyan; Dinda Meysya Rochma; Melani Nur Mudyawati; Miftahul Jannah; Siti Lufia Dwi Agustini; Arham Aulia Nugraha
Gunung Djati Conference Series Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (207.618 KB)

Abstract

Along with the development of the times, films are used as a medium of communication and as a medium of entertainment that can be watched by fans everywhere. The high level of film enthusiasts makes viewing patterns that can be used as recommendation data. Besides, this system can provide benefits to the company concerned. This study used the Generalized Sequential Pattern (GSP) Algorithm with the Association Mining method. The primary function is to find the sequence or sequential of a set of attributes that often appear together to see the results to find out the recommendation data information after the previous film runs out.
The Frequent Pattern Growth Algorithm in the Film Recommendation System Angelyna Angelyna; Arham Aulia Nugraha; Karima Marwazia Shaliha; Muhammad Humam Wahisyam; Tri Kurnia Sandi; Acep Razif Andriyan
Gunung Djati Conference Series Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (329.417 KB)

Abstract

In order to decrease the covid-19 rate, people choose to stay at home. Watching movies with family can be an alternative to fill activities during a pandemic. But sometimes it’s hard to determine the film to be watched. To overcome this a recommendation system is needed. This research is shown to build a system recommendation for film recommendations next will be witnessed. This system created using the Frequent Pattern Growth Algorithm which will do filtering later against several films based on the user’s viewing history. The results of testing the recommendation system using the FP-Growth algorithm work well and can show a minimum support value of 0.973 and a confidence value of 0.291, where the size of this value affects the resulting pattern output.
Fuzzy C-Means Algorithm for Clusterization of Credit Card Usage Miftahul Jannah; Melani Nur Mudyawati; Acep Razif Andriyan; Dinda Meysya Rochma; Siti Lufia Dwi Agustini
Gunung Djati Conference Series Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.965 KB)

Abstract

Credit cards are now familiar to the public. A credit card is a means of payment in lieu of cash in the form of a card issued by the bank to facilitate transactions for customers. Currently, there are various kinds of credit card issuing financial service companies in the world, including Indonesia. With various benefits so that credit is loved by all groups, so that everyone competes to use credit by choosing the desired bank. Data mining methods can provide solutions to extract knowledge from data by looking for certain patterns or rules from large amounts of data. One of the data mining methods is clustering, in which clustering is used to group data by grouping the data into several clusters. By using the credit card data set is divided into 3 clusters using the Fuzzy C-Means algorithm. Of the 3 clusters, the ones that are prioritized with the largest value are the clusters that are widely used by many people, which have a medium value, while the cluster with the smallest value is the cluster with the least interest.
Co-Authors Angelyna Angelyna Arham Aulia Nugraha Dinda Meysya Rochma Karima Marwazia Shaliha Melani Nur Mudyawati Miftahul Jannah Muhammad Humam Wahisyam Siti Lufia Dwi Agustini Tri Kurnia Sandi