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All Journal Gunung Djati Conference Series
Nina Nadia Syafitri Husein
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.
Decision Tree Algorithm for Determining Gender based on Sound Recording Nina Nadia Syafitri Husein; Kamal Zaki Abdurrafi; Ryan Reliovani; Cecep Rafqi Al Husni; Muhammad Azka Khowarizmi; Deden Muhamad Furqon
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 (239.788 KB)

Abstract

Humans are born with their own uniqueness and character, even though having a uniqueness that classifies humans by sex is something that is not difficult to do. In humans, the way to differentiate between men and women is to look at physical differences and listen to different voices between men and women. On the computer, gender differences can also be identified by classifying male and female voices using a tree decision algorithm with a previously appeared dataset in the form of a sample of 3168 male and female voice recordings, the voice recording sample is processed by acoustic analysis in R using Seewave and tuneR packages with frequency interval 0 hz - 280 hz. Meanfun is used as a predictor for the root sound dataset, with a threshold <= 0.142, with an optimal depth value of 6 using the cross validation method, the results achieved are the accuracy training set of 99.18809% and the accuracy test set reaches 95.89905%.
Mean Shift Algorithm to Determine Customer Segmentation in Online Store Sales Ryan Reliovani; Nina Nadia Syafitri Husein; Kamal Zaki Abdurrafi; Cecep Rafqi Al Husni; Muhammad Azka Khowarizmi
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 (313.511 KB)

Abstract

Market segmentation is one of the most important things for a business or business, with market segmentation a shop or company can see the purchasing power, needs and customers of customers. The purpose of this study was to determine the value of customer segmentation in an online shop based in the UK where the main sales are unique gifts for various events where the shop's customers are wholesalers from various countries. Data mining with clustering techniques is used in this study. The algorithm used to build clusters is the Mean Shift algorithm, with an estimated bandwidth value of  1.55, the quantile value = 4, epsilon = 4% and n_samples = 5000, there are 3 clusters visualized using a scatter plot model.
Co-Authors Cecep Rafqi Al Husni Deden Muhamad Furqon Kamal Zaki Abdurrafi Karima Marwazia Shaliha Muhammad Azka Khowarizmi Ryan Reliovani