Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung

Andini, Asri Dwi; Arifin, Toni

doi:10.51977/jti.v2i2.247

Cited by 11 publications

(14 citation statements)

References 5 publications

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“…To solve the problem in this research, data mining techniques with the k-medoids algorithm are used as follows: [15], [16], [17], [18], [19], [20]:…”

Section: Resultsmentioning

confidence: 99%

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

Angraini

Safii

Anggraini

2021

ijobas

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Oil palm (Elaies Guinnnsiss Jacq) is one of the important industrial crops producing cooking oil, industrial oil, and fuel. Indonesia is the largest palm oil producer in the world. The rest of the processing of oil palm fruit is called janjang. Janjang also serves to be used as compost. The data that is processed in this research is the harvest data at PT. Surya Intisariraya Mandau. Data mining is the process of looking for patterns or information in selected data using certain techniques or methods. The processing steps are grouped using the K-Medoids method and then the data will be processed using RapidMiner tools. Where this grouping is done to minimize the amount of similarity of data and appropriate so that it becomes more valid data. This study aims to simplify the grouping of harvest data based on high, medium and low clusters.

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“…To solve the problem in this research, data mining techniques with the k-medoids algorithm are used as follows: [15], [16], [17], [18], [19], [20]:…”

Section: Resultsmentioning

confidence: 99%

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

Angraini

Safii

Anggraini

2021

ijobas

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“…Penyakit merupakan salah satu keadaan tidak normal yang mana tubuh maupun pikiran mengalami ketidaknyamanan terhadap orang yang dipengaruhinya. Pola hidup yang kurang sehat dan lingkungan sangat berpengaruh terhadap penyakit yang diderita pasien [2]. Menurut Unicef ada beberapa faktor yang mempengaruhi kesehatan tubuh diantaranya yaitu faktor lingkungan, keturunan, perilaku dan faktor pelayanan kesehatan.…”

Section: Pendahuluanunclassified

“…Dari hasil referensi penelitian terdahulu maka peneliti memutuskan memakai algoritma K-Medoids dikarenakan metode K-Medoids memiliki kinerja lebih baik dibandingkan dengan algoritma K-Means untuk dataset kecil dan besar. Metode ini digunakan untuk dapat memecah dataset menjadi kelompok-kelompok dan tidak sensitive terhadap outlier [2] [14] [15] dan mempunyai tingkat akurasi tinggi serta efisien dalam memproses objek dalam jumlah besar [6]. Algoritma K-Medoids cukup efisien untuk mengolah data yang sensitif terhadap outlier dan data dalam jumlah kecil [11].…”

Section: Pendahuluanunclassified

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Implementasi Algoritma K-Medoids Dengan Evaluasi Davies-Bouldin-Index Untuk Klasterisasi Harapan Hidup Pasca Operasi Pada Pasien Penderita Kanker Paru-Paru

Septiani

Fauzan

Huda

2022

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Lung Cancer is a disease in which there are cells that grow in the lungs by a collection of carcionogens uncontrollably. Lung Cancer can be treated with surgery, chemotherapy and radiotherapy. Early treatment that needs to be done to reduce the mortality rate in patients with lung cancer after performing thoracic surgery, by collecting data from each patients regarding this information causes a new problem, including the data obtained including high-dimensional data and has many attributes so that it can produce less accurate information. So it is necessary to calculate data mining clustering. In general, the methods for performing clustering are grouped into four parts, namely partitioning, hierarchial, grid-based and model-based. This study used the k-medoids algorithm because it is able to handle data sensitive to outliers and has high accuracy and efficiency in processing large numbers objects. The results of the k-medoids calculation were evaluated using the euclidean distance Davies Bouldin Index which resulted in a DBI value of 0.93543 indicating that the k-medoids algoritm achieves good grouping because the final result of the calculation is less than 0. From the results of the evaluation using DBI it shows that the k-medoids algorithm has an average accumulation average at the time of execution is quite fast and the cluster quality is good.

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“…This type of research related to applying the K-Medoids algorithm was also carried out by (Andini & Arifin, 2020). The dataset in this study is the patient disease dataset at the Bandung City Hospital in 2019.…”

Section: Introductionmentioning

confidence: 99%

Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation

Hutagalung¹,

Syahril²,

Sobirin³

2022

CNAHPC

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IndiHome (Indonesia Digital Home) is a leading digital fibre optic service product consisting of fibre optic internet services, landline telephones, and interactive TV services. Although the coverage of Indihome products is extensive in the city of Medan, in marketing, Indihome products have not reached the planned target. Based on data from Indihome service package users that have been received, Indihome product users only numbered 6419 customers in all STOs in Medan City. At the same time, the target was planned by PT. Telkom Access Medan, namely Marketing Indihome products, must reach 5,000 customers per month in all STOs in Medan City. Indihome product marketing is an obstacle for PT. Telkom Access Medan, because the Indihome product is a new product, the people of Medan City do not fully know what Indihome is and what facilities they get from using the Indihome service package. Therefore PT. Telkom Access Medan needs to make a plan to make a marketing strategy. The first step that needs to be done is to segment the market for the Indihome service package. This study aimed to determine the application of Data Mining using the K-Medoids Clustering method in the Indihome service package market segmentation at PT. Telkom Access Medan. With this research, it is hoped that it can provide a reference for the results of the decision so that it can help related parties to make it easier to classify the market segmentation of the Indihome service package at PT. Telkom Access Medan. Because the value of S > 0, then the calculation is stopped and ends in the 3rd iteration. Indihome service package data processing uses the k-medoids clustering method in the form of potential, potential, and not potential STO (Sentral Telephone Automated) cluster members.

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Implementasi Algoritma K-Medoids Untuk Klasterisasi Data Penyakit Pasien Di Rsud Kota Bandung

Cited by 11 publications

References 5 publications

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

A Oil Palm Harvest Grouping Using K-Medoids Algorithm

Implementasi Algoritma K-Medoids Dengan Evaluasi Davies-Bouldin-Index Untuk Klasterisasi Harapan Hidup Pasca Operasi Pada Pasien Penderita Kanker Paru-Paru

Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation

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