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All Journal Gunung Djati Conference Series
Nisa Eka Juliana
Teknik Informatika, UIN Sunan Gunung Djati Bandung
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The Film Recommendation System uses the Recursive Elimination Algorithm Aaz M Hafidz Azis; Nisa Eka Juliana; Faridah Dewi Khansa; Miftahul Jannah
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 (193.869 KB)

Abstract

The number of films has increased to become denser. Therefore, it is very difficult to find the film that users are looking for through existing technology. For this reason, users want a system that can suggest their film needs and the best technology about this is a recommendation system. However, the most recommended system is to use the collaborative filtering method to predict user needs because this method provides the most accurate predictions. Currently, many researchers are concerned with developing methods for increasing accuracy rather than using collaborative screening methods. In this paper we use the Recursive Elimination (RElim) algorithm for the film recommendation system. As a result, each itemset is annotated with its support. Itemset support is the number of times the itemset appears in the transaction database
News Category Classification using Support Vector Machine Algorithm Nisa Eka Juliana; Faridah Dewi Khansa; Aaz M Hafidz Azis; Rafli Indra Gunawan; Nurul Dwi Cahya
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 (232.313 KB)

Abstract

Nowadays many have used web-based systems to convey information and news in real time. However, in dividing news into these categories, some are still done manually, so it takes a long time. Of the several existing techniques, the technique most often used for classification of news content is the Support Vector Machine (SVM). In complex problems or problems with many parameters, this method is very good to use. The SVM algorithm performs supervised learning classifications or has inputs and outputs that have been formed into a mathematical relationship model that can classify and predict existing data. There are 2224 datasets and 5 categories with 70% of the data being trained and 30% of the data being tested. This study produces text classifications in the form of technology, business, sports, entertainment, and political categories from digital news content. The classification results obtained an accuracy value of 98.35% with an average precision of 90%, a recall of 98%, an F1-score of 98% and a Support of 668.
World Map Asia clustering using Agglomerative Clustering Algorithm Faridah Dewi Khansa; Nisa Eka Juliana; Aaz M Hafidz Azis; Rafli Indra Gunawan
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 (434.607 KB)

Abstract

Cluster analysis is one technique that can be correlated. Explain the closeness or similarity between objects and variables. The analysis is divided into 2 methods, namely hierarchical and non-hierarchical. In the hierarchical method, there are several methods, including the complete linkage method, average linkage, single linkage and ward linkage. This method is applied in the grouping of the Asian continent. The purpose of this study is to classify countries based on GDP and population in Asia. In this study, the hierarchical method used is Agglomerative Hierarchical Clustering (AHC). 177 countries with 6 columns of country data around the world and sorted into 47 country data columns and 6 country data columns in the Asian continent. The per capita state income is obtained by dividing GDP by population. Then made 4 clusters. Cluster 1 consists of the United Arab Emirates, Kuwait and Qatar. Cluster 2 consists of Brunei, Oman, Saudi Arabia and Taiwan. Cluster 3 consists of 34 countries and the last cluster is Cyprus, Israel, Kazakhstan, South Korea, Malaysia.
Co-Authors Aaz M Hafidz Azis Faridah Dewi Khansa Miftahul Jannah Nurul Dwi Cahya Rafli Indra Gunawan