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Tri Kurnia Sandi
Teknik Informatika, UIN Sunan Gunung Djati Bandung
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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.
Viewer Movie Predictions based on Genres, Actors, and Directors based on Data Mining Using the Eclat Algorithm Deden Muhamad Furqon; Riki Ahmad Maulana; Ahmad Fauzi; Nurul Dwi Cahya; Muhammad Nur Sidiq; 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 (483.386 KB)

Abstract

Movies are a place for everyone to find pleasure and entertainment. The film industry is an essential part of the economy in this world. On average, 79% of people in the world enjoy watching movies for their entertainment. Therefore, the film industry has become a huge industry, but it is difficult to predict because the audience's desires are very diverse. Therefore, we created a prediction system for user choices based on the recommendations that will be presented, most likely to be enjoyed by audiences based on genre, actor, and their favorite director, which will function for producers to analyze the market. The method used is data mining using the Eclat algorithm, which has five processes in it. The result is that we get and sort 5043 data by 28 columns and omit some with a support threshold of 0.003 and evaluate the results after evaluating the film data obtained from 1169 lines with a value above 0.003 supporting data to predict user choice recommendations.
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 Ahmad Fauzi Angelyna Angelyna Arham Aulia Nugraha Deden Muhamad Furqon Karima Marwazia Shaliha Muhammad Humam Wahisyam Muhammad Nur Sidiq Nurul Dwi Cahya Riki Ahmad Maulana