Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau

Kamila, Insanul; Khairunnisa, Ulya; Mustakim, Mustakim

doi:10.24014/rmsi.v5i1.7381

Cited by 69 publications

(75 citation statements)

References 2 publications

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“…Data Mining merupakan proses mencari informasi menarik atau pola dalam suatu data atau dapat juga dikenaldengan istilah yang digunakan untuk menemukan pengetahuan di dalam suatudatabase dengan menggunakan teknik atau metode tertentu. Data Mining merupakan proses menggunakan kecerdasan buatan, machine learning, teknik statistik dan matematika dalam mengidentifikasi dan menemukan informasi yang bermanfaat bagi pengetahuan yang terkait dengan berbagai database berkapasitas besar [3]. K-Medoids merupakan salah satu teknik dalam clustering.…”

Section: Pendahuluanunclassified

Akurasi Pemberian Insentif Menggunakan Algoritma K-Medoids Terhadap Tingkat Kedisiplinan Pegawai

Robiansyah

Nurcahyo

2021

jidt

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Assessment of a discipline is a performance evaluation stage that is important for the continuity of company activities. Monitoring and assessment of an employee's discipline must be carried out continuously in order to improve the quality of human resources. This research was conducted to make the accuracy of providing incentives based on the level of employee discipline. The data processed in this study is a recapitulation of the attendance of AMIK and STIKOM Tunas Bangsa Pematangsiantar employees as many as 25 employees as a sample. For grouping the employee data using the K-Medoids Algorithm. K-Medoids groups a set of n objects into a number of k clusters using the partition clustering method. Furthermore, the employee data is processed using Rapid Miner software. Research using this method obtained results in the form of grouping employees into 3 groups that have good discipline levels of 12 employees, sufficient discipline levels of 8 employees, and less disciplinary levels of 5 employees. Based on the grouping results that have been produced, it can be a consideration for the leadership to determine the amount of incentives for employees.

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Section: Pendahuluanunclassified

Akurasi Pemberian Insentif Menggunakan Algoritma K-Medoids Terhadap Tingkat Kedisiplinan Pegawai

Robiansyah

Nurcahyo

2021

jidt

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“…The K-Medoids algorithm applies objects as representatives (medoid) for each cluster. The application of the K-Medoids algorithm takes longer than K-Means because it takes about 2 minutes on Rapidminer, while the K-Means method only takes about 1 second [11]. because the use of this method is done by searching epsilon and min points randomly to get a particular cluster [12].…”

Section: Clusteringmentioning

confidence: 99%

Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

Brahmana¹,

Mohammed²,

Chairuang³

2020

LKJITI

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A problem that appears in marketing activities is how to identify potential customers. Marketing activities could identify their best customer through customer segmentation by applying the concept of Data Mining and Customer Relationship Management (CRM). This paper presents the Data Mining process by combining the RFM model with K-Means, K-Medoids, and DBSCAN algorithms. This paper analyzes 334,641 transaction data and converts them to 1661 Recency, Frequency, and Monetary (RFM) data lines to identify potential customers. The K-Means, K-Medoids, and DBSCAN algorithms are very sensitive for initializing the cluster center because it is done randomly. Clustering is done by using two to six clusters. The trial process in the K-Means and K-Medoids Method is done using random centroid values ??and at DBSCAN is done using random Epsilon and Min Points, so that a cluster group is obtained that produces potential customers. Cluster validation completes using the Davies-Bouldin Index and Silhouette Index methods. The result showed that K-Means had the best level of validity than K-Medoids and DBSCAN, where the Davies-Bouldin Index yield was 0,33009058, and the Silhouette Index yield was 0,912671056. The best number of clusters produced using the Davies Bouldin Index and Silhouette Index are 2 clusters, where each K-Means, K-Medoids, and DBSCAN algorithms provide the Dormant and Golden customer classes.

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“…Data mining digunakan untuk mengolah data menjadi suatu informasi dan pengetahuan [1]. Dalam prosesnya, data mining menggunakan teknik statistik, matematika, dan kecerdasan buatan untuk identifikasi dan ekstraksi informasi dan pengetahuan.…”

Section: Pendahuluanunclassified

“…Kamila dkk. [1] melakukan perbandingan algoritme k-medoids dan k-means dengan menggunakan waktu kecepatan pengolahan dan davies-bouldin index sebagai parameternya. Penelitian tersebut menghasilkan algoritme k-medoids lebih unggul dibandingkan kmeans.…”

Section: Pendahuluanunclassified

Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster

Aditya

Sari

Padilah

2020

J. Teknol. dan Sist. Komput

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K-medoids clustering uses distance measurement to find and classify data that have similarities and inequalities. The distance measurement method selection can affect the clustering performance for a dataset. Several studies use the Euclidean and Gower distance as measurement methods in numerical data clustering. This study aims to compare the performance of the k-medoids clustering on a numerical dataset using the Euclidean and Gower distance. This study used seven numerical datasets and Silhouette, Dunn, and Connectivity indexes in the clustering evaluation. The Euclidean distance is superior in two values of Silhouette and Connectivity indexes so that Euclidean has a good data grouping structure, while the Gower is superior in Dunn index showing that the Gower has better cluster separation compared to Euclidean. This study shows that the Euclidean distance is superior to the Gower in applying the k-medoids algorithm with a numeric dataset.

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Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Data Transaksi Bongkar Muat di Provinsi Riau

Cited by 69 publications

References 2 publications

Akurasi Pemberian Insentif Menggunakan Algoritma K-Medoids Terhadap Tingkat Kedisiplinan Pegawai

Akurasi Pemberian Insentif Menggunakan Algoritma K-Medoids Terhadap Tingkat Kedisiplinan Pegawai

Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods

Comparison analysis of Euclidean and Gower distance measures on k-medoids cluster

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