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All Journal Gunung Djati Conference Series
Melani Nur Mudyawati
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

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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.
Comparison of Classification Algorithms for Sentiment Analysis on Movie Comments Dian Sa'adillah Maylawati; Melani Nur Mudyawati; Muhammad Humam Wahisyam; Riki Ahmad Maulana
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

Abstract

The film industry is growing rapidly nowadays, various genres and storylines are nicely packaged to convey messages and entertain audiences. Sentiment analysis technology can be used for the advancement of the film industry as well as film recommendations that need to be presented next. This study aims to compare several algorithms used for sentiment analysis of movie reviews or comments. The algorithms used in this study are K-Nearest Neighbor (k-NN), Naïve Bayes Classifier (NBC), and Logistic Regression. The experimental results using 25,000 film comment datasets show that Logistic Regression has the highest accuracy rate with an accuracy of 89%, compared to Naïve Bayes' accuracy of 86%, while k-NN is 65.22%.
Fuzzy C-Means Algorithm for Clusterization of Credit Card Usage Miftahul Jannah; Melani Nur Mudyawati; Acep Razif Andriyan; Dinda Meysya Rochma; Siti Lufia Dwi Agustini
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 (628.965 KB)

Abstract

Credit cards are now familiar to the public. A credit card is a means of payment in lieu of cash in the form of a card issued by the bank to facilitate transactions for customers. Currently, there are various kinds of credit card issuing financial service companies in the world, including Indonesia. With various benefits so that credit is loved by all groups, so that everyone competes to use credit by choosing the desired bank. Data mining methods can provide solutions to extract knowledge from data by looking for certain patterns or rules from large amounts of data. One of the data mining methods is clustering, in which clustering is used to group data by grouping the data into several clusters. By using the credit card data set is divided into 3 clusters using the Fuzzy C-Means algorithm. Of the 3 clusters, the ones that are prioritized with the largest value are the clusters that are widely used by many people, which have a medium value, while the cluster with the smallest value is the cluster with the least interest.
Co-Authors Acep Razif Andriyan Arham Aulia Nugraha Dian Sa’adillah Maylawati Dinda Meysya Rochma Miftahul Jannah Muhammad Humam Wahisyam Riki Ahmad Maulana Siti Lufia Dwi Agustini