Analisis Algoritma Partitioning Around Medoid untuk Penentuan Klasterisasi

Orisa, Mira; Faisol, Ahmad

doi:10.25047/jtit.v8i2.258

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…Dimana clustering sendiri artinya segmentasi dataset yang menjadi inputan tanpa diketahui target classnya. Dataset yang dikelompokan di kumpulkan berdasarkan kemiripan antara instance yang ada [8]…”

Section: Clusteringunclassified

Analisis Segmentasi Puskesmas Di Kabupaten Cirebon Menggunakan Algoritma Partitioning Around Medoid Berdasarkan Indikator Penyebab Stunting

Komalasari,

Faqih,

Dikananda

et al. 2024

jati

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Stunting merupakan keadaan dimana seseorang mengalami kekurangan gizi. Namun, hingga saat ini, belum terdapat segmentasi Puskesmas terkait stunting berdasarkan indikatornya menggunakan data mining di Kabupaten Cirebon. Pada penelitin ini bertujuan untuk menganalisis segmentasi puskesmas terkait stunting dengan indikatornya guna memberikan rekomendasi pencegahan dan penyuluhan terkait stunting menggunakan Algoritma Partitioning Around Medoid (PAM). Data bersumber dari website opendata.jabarprov.go.id dan opendata.cirebonkab.go.id dan observasi langsung ke dinas Kesehatan kab. Cirebon. Variabel yang digunakan Jumlah Ibu Hamil yang mengalami KEK (Kekurangan Energi Kronik), Jumlah stunting, Jumlah Ahli Gizi dan Jumlah Puskesmas aktif yang ada di kabupaten Cirebon pada tahun 2022. Proses pengolahan data menggunakan metode KDD (Knowledge Discovery in Database). Implementasi k-medoids dilakukan dengan inisiasi nilai centroid secara acak dalam iterasi. Hasil menunjukkan medoid sementara sebesar 7122,045634, dan medoid awal 6348,966306, dengan selisih 773,0793279 hasil yang signifikan mengakibatkan penghentian iterasi pada tahap pertama. Evaluasi manual dengan rumus SSW (Sum of Square Within Cluster) dan SSB (Sum of Square Between) menghasilkan rasio cluster 0,8470604, mendekati 0, menunjukkan hasil yang baik dan Evaluasi cluster menggunakan Davies-Bouldin Index (DBI) menggunakan RapidMiner menghasilkan nilai 0,569. Cluster 1 (19 puskesmas) memiliki kasus stunting tertinggi, sedangkan Cluster 2 (41 puskesmas) memiliki kasus stunting rendah, memberikan dasar rekomendasi pencegahan serta penyuluhan yang lebih terarah.

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Section: Clusteringunclassified

Analisis Segmentasi Puskesmas Di Kabupaten Cirebon Menggunakan Algoritma Partitioning Around Medoid Berdasarkan Indikator Penyebab Stunting

Komalasari,

Faqih,

Dikananda

et al. 2024

jati

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“…Silhouette coefficient memiliki rentang nilai, yaitu −1 ≤ SC ≤ 1 [22]. Jika nilai silhouette coefficient menuju 1, maka objek-objek memiliki jarak yang sangat padat pada satu cluster dan memiliki jarak yang jauh dari cluster lainnya [23]. Validasi cluster menggunakan metode silhouette coefficient merujuk pada [24] yang selanjutnya disajikan pada Tabel 1.…”

Section: Silhouette Coefficientunclassified

Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

Putri,

Satyahadewi,

Aprizkiyandari

2024

Jambura J. Math

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Indonesia's export activities are dominated by non-oil and gas exports consisting of four sectors, namely the processing industry, agriculture, mining, and others. The government must pay attention to non-oil and gas exports for each province because exports can play an essential role in a country's economic growth. This study was conducted to cluster provinces in Indonesia using time series clustering in the non-oil and gas export sector based on data patterns concerning Dynamic Time Warping (DTW) distance. The sectors used in this study are the manufacturing industry sector and the agricultural sector in 34 Indonesian provinces in the period 2017 - 2021. Time series clustering analysis uses the average linkage method with DTW distance and the selection of the optimum number of clusters using the silhouette coefficient method. The results of the analysis in the processing industry sector resulted in 3 optimum clusters, namely cluster 1 consisting of 1 province that has high processing industry exports, cluster 2 consisting of 8 provinces that have medium processing industry exports, and cluster 3 consisting of 25 provinces that have low processing industry exports. As for the agricultural sector, it produces 2 optimum clusters, namely cluster 1 consisting of 5 provinces that have high agricultural industry exports, and cluster 2 consisting of 29 provinces that have low agricultural industry exports. The clustering results in the processing industry sector and the agricultural sectors have a silhouette coefficient value of 0.778 and 0.798, so it is said to have a strong cluster structure.

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“…The K-medoids algorithm updates the centroid with the actual object as a cluster representation instead of using the average as in the K-Means algorithm. So the K-Medoids algorithm minimizes the differences between each p object and the nearest representation object, using the absolute number of errors (Orisa & Faisol, 2021) The stages of the k-medoids algorithm is as follows (Bimantoro & Wardhani, 2020): 1. Determine the desired k (number of clusters).…”

Section: Literature Reviewmentioning

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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Analisis Algoritma Partitioning Around Medoid untuk Penentuan Klasterisasi

Cited by 3 publications

References 1 publication

Analisis Segmentasi Puskesmas Di Kabupaten Cirebon Menggunakan Algoritma Partitioning Around Medoid Berdasarkan Indikator Penyebab Stunting

Analisis Segmentasi Puskesmas Di Kabupaten Cirebon Menggunakan Algoritma Partitioning Around Medoid Berdasarkan Indikator Penyebab Stunting

Pengelompokan Provinsi di Indonesia Menggunakan Time Series Clustering pada Sektor Ekspor Nonmigas

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

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