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Siti Lufia Dwi Agustini
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (207.618 KB)

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.
Classification of Heart Disease Diagnosis using the Random Forest Algorithm Akbar Hidayatullah Harahap; Ihsan Muttaqin Bin Abdul Malik; Muhammad Irfan Nur Imam; Muhammad Thariq Sabiq Bilhaq; Aisyah Amini Nur; 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 (603.088 KB)

Abstract

The heart is an important part of the human body organs. But it does not rule out this heart problem and causes several symptoms, causing deadly heart disease. Therefore, many studies are used to obtain fast, precise and accurate heart disease diagnosis data. One of them is the classification of heart disease diagnoses using the Random Forest algorithm. This random forest algorithm method uses several unified decision trees. So that accurate results can be obtained regarding the diagnosis of this heart disease. The accuracy obtained from the experimental results using the Python programming language is 85.3%.
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 Aisyah Amini Nur Akbar Hidayatullah Harahap Arham Aulia Nugraha Dinda Meysya Rochma Ihsan Muttaqin Bin Abdul Malik Melani Nur Mudyawati Miftahul Jannah Muhammad Irfan Nur Imam Muhammad Thariq Sabiq Bilhaq