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All Journal Gunung Djati Conference Series
Angelyna Angelyna
Teknik Informatika, UIN Sunan Gunung Djati Bandung
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Film Recommendation Analysis with Sequential Pattern Discovery Algorithm Using Equivalence classes (SPADE) Aisyah Amini Nur; Akbar Hidayatullah Harahap; Ihsan Muttaqin Bin Abdul Malik; Muhammad Irfan Nur Imam; Muhammad Thariq Sabiq Bilhaq; Angelyna Angelyna
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 (499.306 KB)

Abstract

Currently, the internet is the most essential thing in human life. So that there is a change in human needs, one of which is enjoying entertainment. With the internet, enjoying entertainment can be done anywhere and anytime, one of the entertainment is movies. With the internet, many service providers provide movie watching sites online. So that people become interested and use it a lot to watch movies online. With the increase in film fans, a film recommendation system is required to make a film recommendation pattern based on previous movie viewing. Therefore, this study aims to provide recommendations for viewing films using the pattern recognition method that often occurs. The technique used is sequence pattern mining using SPADE to find patterns from a group of data. SPADE's advantage is its speed in finding sequence frequencies that can be used as movie recommendation data from previous shows.
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 Aisyah Amini Nur Akbar Hidayatullah Harahap Arham Aulia Nugraha Ihsan Muttaqin Bin Abdul Malik Karima Marwazia Shaliha Muhammad Humam Wahisyam Muhammad Irfan Nur Imam Muhammad Thariq Sabiq Bilhaq Tri Kurnia Sandi