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All Journal Gunung Djati Conference Series
Karima Marwazia Shaliha
Teknik Informatika, UIN Sunan Gunung Djati Bandung
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Implementation of the Apriori Algorithm for Film Recommendations based on Director and Movie Duration Kamal Zaki Abdurrafi; Ryan Reliovani; Nina Nadia Syafitri Husein; Cecep Rafqi Al Husni; Muhammad Azka Khowarizmi; Karima Marwazia Shaliha
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 (570.766 KB)

Abstract

The Film Industry is an industry that never dies, in Indonesia itself for the past three years the number of film viewers has continued to increase. Reporting from the indonesia.go.id page in 2018 the number of film productions produced is almost 200 titles, from the large number of films produced, of course film lovers have different tastes for films, one way that can be used to increase attractiveness in films is the existence of film recommendation system based on film trends based on the director and how long the ideal film duration for prospective viewers. The algorithm chosen in this research is to find and determine the pattern of director selection and film duration available in 1001 data on film data, the data will be divided into lists consisting of 30 items. The results of this study are film recommendations based on a priori algorithm with the director and film duration as a reference for association rules. The results obtained from this study are that the apriori algorithm can be implemented in film recommendations based on the director and film duration.
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 Arham Aulia Nugraha Cecep Rafqi Al Husni Kamal Zaki Abdurrafi Muhammad Azka Khowarizmi Muhammad Humam Wahisyam Nina Nadia Syafitri Husein Ryan Reliovani Tri Kurnia Sandi