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All Journal Gunung Djati Conference Series
Arham Aulia Nugraha
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.
Implementation of K-Means Clustering in Online Retail based on Recency, Frequency, and Monetary Karima Marwazia Shaliha; Angelyna Angelyna; Arham Aulia Nugraha; Muhammad Humam Wahisyam; Tri Kurnia Sandi
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 (326.42 KB)

Abstract

During a pandemic like today, many changes have occurred, one of which is the increasing number of online buying and selling sites. Each Online Store offers a variety of products and services with a variety of attractive offers, competing fiercely to attract enthusiasts. With the occurrence of a pattern of change in society, it is necessary to carry out a grouping to obtain information in order to determine a better sales strategy. The grouping process uses techniques from data mining, namely Clustering with the K-Means algorithm based on the Recency Frequency Monetary (RFM) algorithm, it is hoped that by analyzing the three attributes and implementing the K-Means algorithm, it can provide an accurate output and in accordance with the objectives of this study.
Co-Authors Acep Razif Andriyan Angelyna Angelyna Dinda Meysya Rochma Karima Marwazia Shaliha Melani Nur Mudyawati Miftahul Jannah Muhammad Humam Wahisyam Siti Lufia Dwi Agustini Tri Kurnia Sandi