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Gunung Djati Conference Series
Published by UIN Sunan Gunung Djati Bandung
ISSN : 27746585     EISSN : -     DOI : -
Core Subject : Religion, Economy, Science, Education, Agriculture, Social, Engineering,
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Gunung Djati Conference Series merupakan penyedia layanan publikasi dan konferensi berbagai bidang keilmuan yang diterbitkan oleh UIN Sunan Gunung Djati Bandung dan sebagai sarana publikasi hasil penelitian. Penerbit ini memuat artikel yang belum pernah dipublikasikan sebelumnya yang berupa artikel hasil penelitian ataupun penelitian terapan. Informasi mengenai pedoman penulisan artikel dan prosedur pengiriman artikel terdapat pada setiap penerbitan. Semua artikel yang masuk akan melalui ‘peer-review process’ setelah memenuhi persyaratan sesuai pedoman penulisan artikel, kemudian penerbit menyerahkan semua template ke pelaksana konferensi keilmuan. Penerbitan artikel ini dilakukan sesuai kegiatan yang dilaksanakan oleh penyelenggara konferensi.
Arjuna Subject : Umum - Umum
Articles 1,851 Documents
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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
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

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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%.
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

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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%.
Implementation of the ID3 Algorithm for Classification of Car Quality Dimas Adi Putra Pratama; Nisvy Sya`'bana Nugraha; Risnandy Maulana; Adellia Rahmasari; Ahmad Fauzi
Gunung Djati Conference Series Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020
Publisher : UIN Sunan Gunung Djati Bandung

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Abstract

Transportation like a car is needed by most of family. Cheap, economical and comfortable family cars have been sold in the market. However, buyers must be careful in finding the car they want. To get the best car, many factors are taken into consideration, such as price, seating, and comfort. Comfort is the main thing in owning a car. There are cars that are expensive but do not have comfort, and vice versa. Therefore, we discuss how the Iterative Dichtomizer Three (ID3) Algorithm can be implemented for the classification process of any car with quality according to price, seat and passenger comfort.
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.
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

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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%.
Premier League 2020 Player Data Clustering with the DBSCAN Algorithm Muhammad Thariq Sabiq Bilhaq; Aisyah Amini Nur; Akbar Hidayatullah Harahap; Ihsan Muttaqin Bin Abdul Malik; Muhammad Irfan Nur Imam; Muhammad Nur Sidiq
Gunung Djati Conference Series Vol. 3 (2021): Mini Seminar Kelas Data Mining 2020
Publisher : UIN Sunan Gunung Djati Bandung

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Abstract

Premiere League is prestigious event that is in demand by many people. And player goals are very important in this regard. Research on the number of players based on this age can be done. One of which is by clustering. Clustering is a method for grouping objects according to their similarities. And the algorithm used DBSCAN. This algorithm can solve complex problems and has the right density so that it can help to get clustering data on this theme.
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

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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.
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

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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.
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.

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