A LOF K-Means Clustering on Hotspot Data

Muhima, Rani Rotul; Kurniawan, Muchamad; Pambudi, Oktavian Tegar

doi:10.25139/ijair.v2i1.2634

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…Then calculate the cluster average to determine the cluster center. This method is still carried out until the cluster members do not experience displacement [17].…”

Section: De =mentioning

confidence: 99%

“…Previous research that uses data mining to create clusters in dataset analysis, including the Fuzzy C-Means Algorithm [12]- [14], Multifactor Evaluation Process (MFEP) [15], Comparison of Single Linkage, Complete Linkage and Average Linkage Clustering Methods [16] [14], and K-Means Algorithm [14] [17]by Using Particle Swarm [18]. However, no one has discussed the paper to make groupings know the level of student satisfaction with online learning.…”

Section: Introduction Coronavirus (Covid 19mentioning

confidence: 99%

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The Level of Student Satisfaction with the Online Learning Process During a Pandemic Using the K-means Algorithm

Arifin

Natassa

Khoirunnisa

et al. 2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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The number of cases of Covid-19 in this pandemic era is increasing and getting out of control every day. This triggers the Indonesian government to set policies on schools with online learning methods. Of course, online learning cannot ensure that it runs smoothly in all circles because several factors hinder the learning process. The difficulty of the internet network, limited quotas, unfamiliarity with the use of learning media, and an unsupportive environment for conducting online learning are the obstacles to ineffective online learning. The purpose of this study was to determine the level of satisfaction with online learning during the pandemic. This study uses quantitative research methods with a descriptive approach. Quantitative research methods will be processed into data mining using the K-Means Clustering Algorithm. The clustering process is carried out to get the results of clustering the level of student satisfaction. The dataset was obtained from the results of the questionnaire by submitting statements of satisfaction and dissatisfaction. The cluster type is based on high, medium, and low class. The test results obtained a value with the final iteration, namely the level of satisfied statements is categorized as high with a value of 11.79 compared to the dissatisfied statement, which is categorized as moderate with a value of 7.46. In contrast, for the low category level, there is no value of 0.00 cluster results state that the category is satisfied with online learning with a value of 9.33.

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“…Then calculate the cluster average to determine the cluster center. This method is still carried out until the cluster members do not experience displacement [17].…”

Section: De =mentioning

confidence: 99%

Section: Introduction Coronavirus (Covid 19mentioning

confidence: 99%

The Level of Student Satisfaction with the Online Learning Process During a Pandemic Using the K-means Algorithm

Arifin

Natassa

Khoirunnisa

et al. 2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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show abstract

“…From the loop, it can be seen that the longer the center of the cluster will move towards that location is right. FCM is satisfied as equation (1)…”

Section: B Fuzzy C-meansmentioning

confidence: 99%

“…This dataset is from NASA's official website (https://earthdata.nasa.gov/earth-observationdata/near-real-time/firms/viirs-i-band-active-fire-data).This dataset was studied before. In this study [1], The purpose active fire data are used to prevent forest fires. The features used have been reduced to 2 features: longitude and latitude.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset

Kurniawan

Muhima

2020

Int.J.Artif.Intell.Robot.

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One of the causes of forest fires is the lack of speed of handling when a fire occurs. This can be anticipated by determining how many extinguishing units are in the center of the hot spot. To get hotspots, NASA has provided an active fire dataset. The clustering method is used to get the most optimal centroid point. The clustering methods we use are K-Means, Fuzzy C-Means (FCM), and Average Linkage. The reason for using K-means is a simple method and has been applied in various areas. FCM is a partition-based clustering algorithm which is a development of the K-means method. The hierarchical based clustering method is represented by the Average Linkage method. The measurement technique that uses is the sum of the internal distance of each cluster. Elbow evaluation is used to evaluate the optimal cluster. The results obtained after conducting the K-Means trial obtained the best results with a total distance of 145.35 km, and the best clusters from this method were 4 clusters. Meanwhile, the total distance values obtained from the FCM and Linkage methods were 154.13 km and 266.61 km.

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“…Literature study on previous research [5]- [8]. The proposes a method for grouping or classifying distinct sorts of eggs utilizing the connected components method of analysis applied to an object in a Fig.…”

Section: Introductionmentioning

confidence: 99%

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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A LOF K-Means Clustering on Hotspot Data

Cited by 9 publications

References 8 publications

The Level of Student Satisfaction with the Online Learning Process During a Pandemic Using the K-means Algorithm

The Level of Student Satisfaction with the Online Learning Process During a Pandemic Using the K-means Algorithm

Comparison of Clustering K-Means, Fuzzy C-Means, and Linkage for Nasa Active Fire Dataset

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

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