Reduksi Atribut Menggunakan Information Gain untuk Optimasi Cluster Algoritma K-Means

Dinata, Rozzi Kesuma; Novriando, Haried; Hasdyna, Novia; Retno, Sujacka

doi:10.26418/jp.v6i1.37606

Cited by 10 publications

(10 citation statements)

References 5 publications

(6 reference statements)

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“…The test is carried out by determining the smallest value from the DBI, where the smallest value in the number of clusters is 0.175 after the data has been processed. Study [17], the DBI value for the conventional K-Means was 2.1972, while the DBI for the K-Means which had been reduced from 1 attribute to 5 attributes obtained an average DBI value of 2.0290, 1.8771, 1.8641, 1.8389, and 1.8117. Study [14], The method used in this study is the K-method.…”

Section: Related Researchmentioning

confidence: 84%

Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

Solikhun

Yasin²,

Nasution

2022

Int. J. Artif. Intell. Robot.

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The need for eggs that continues to increase will not increase with large egg production so that there is a shortage of egg supplies which results in high egg prices. It is necessary to group egg production in Indonesia to find out which areas fall into the high cluster and which areas fall into the low cluster. This study aims to classify the egg production of laying hens in Indonesia. The method used is the K-Means Clustering method which is a popular clustering method. To find out how optimal the number of clusters in the K-Means method is for grouping egg production in Indonesia, the researcher evaluates the DBI value of each number of existing clusters. In this study, 8 clusters were used, namely 2 clusters, 3 clusters, 4 clusters, 5 clusters, 6 clusters, 7 clusters, 8 clusters, and 9 clusters. The results of measuring the DBI value are the number of clusters 2 = 0.215, the number of clusters 3 = 0.149, the number of clusters 4 = 0.146, the number of clusters 5 = 0.157, the number of clusters 6 = 0.180, the number of clusters 7 = 0.205, the number of clusters 8 = 0.192 and the number of clusters 9 = 0.154. This study shows that the best number of clusters is the number of clusters 4 with the smallest DBI value of 0.146.

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Section: Related Researchmentioning

confidence: 84%

Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

Solikhun

Yasin²,

Nasution

2022

Int. J. Artif. Intell. Robot.

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“…K-means is one of the methods in machine learning that can group data or clustering a data into the form of one or more clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped into different groups [12]. In detail, K-means algorithm works is as follows:…”

Section: B K-meansmentioning

confidence: 99%

Machine Learning Approach to Determine the Drug-Prone Areas in Lhokseumawe City, Indonesia

2022

IJMRA

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This research aims to apply Machine Learning algorithms, such as K-means and K-nearest neighbor into a system that can determine drug-prone areas in Lhokseumawe City, Indonesia. The results of the k-means calculation in some clusters of areas that are very prone, prone and not prone to drugs based on drug user data with some criterias, namely the number of users, sub- district, education, occupation and types of drugs. Based on the research that tested by using the k-means algorithm on data on drug users in 2018-2021, the average percentage of clusters in very prone areas is 49.50%, the percentage of clusters in prone areas is 29.26% and the percentage of clusters in non-prone areas is 21.23%. Furthermore, the results of testing with the K-NN algorithm with 98 training data and 20 testing data obtained an average accuracy value of 87%.

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“…Selain itu dilakukan pula reduksi fitur pada pengelompokan dengan k-means dan FCM. Reduksi fitur menggunakan information gain untuk mengoptimalkan hasil clustering dengan k-means juga telah dilakukan [12]. Untuk meningkatkan performa FCM, pengelompokan dilakukan dengan menghitung bobot fitur individu secara otomatis dan secara bersamaan mereduksi komponen fitur yang tidak relevan [13].…”

Section: Pendahuluanunclassified

Seleksi Fitur pada Pengelompokan Posisi Pemain Basket menggunakan Fuzzy C-Means

Erga

Nataliani

2021

JOINTECS

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Basketball is a well-known sport in the world. Satya Wacana Saint is one of the basketball teams in Indonesia. This team is one of the contestants for the Indonesian Basketball League (IBL). In basketball, players are divided into three main positions according to their physical condition, which are center, forward, and guard. The goal of this research is to select the physical condition features that most influence the player's position. The data used in this paper is data on height, weight, age, and body mass index (BMI) of 23 players. Fuzzy c-means (FCM) clustering is used to group the data into three positions based on their physical condition's features. To find out which feature(s) are the most influential in determining the player's position, we use feature selection with FCM clustering algorithm. For comparison, the FCM results are compared with their actual positions. From the four features of physical condition, it can be concluded that the features of height and BMI are the two most influential features in determining the position of a player. If all features are used in FCM, the accuracy is 0.8696, while if the features of height and BMI are used, the accuracy is higher, i.e., 0.9565.

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Reduksi Atribut Menggunakan Information Gain untuk Optimasi Cluster Algoritma K-Means

Cited by 10 publications

References 5 publications

Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

Optimization of the Number of Clusters of the K-Means Method in Grouping Egg Production Data in Indonesia

Machine Learning Approach to Determine the Drug-Prone Areas in Lhokseumawe City, Indonesia

Seleksi Fitur pada Pengelompokan Posisi Pemain Basket menggunakan Fuzzy C-Means

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