Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara

Fajriana, Fajriana

doi:10.26418/jp.v7i2.47795

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…Pada penelitian ini penulis menerapkan algoritma K-Medoids dalam sistem klasterisasi populasi peternakan di Jawa Barat. K-Medoids adalah suatu algoritma yang dapat diimplementasikan dalam proses clustering yang memakai objek menjadi representasi (medoid) untuk pusat cluster pada setiap kelompok [7]. Pada penelitian ini algoritma K-Medoids dioptimasi menggunakan Davies Bouldin Index (DBI) untuk menentukan jumlah cluster yang optimal.…”

Section: Pendahuluanunclassified

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Penerapan Algoritma K-Medoids Dalam Menentukan Cluster Kabupaten Dan Kota Berdasarkan Populasi Peternakan Di Provinsi Jawa Barat

Khoirunisa,

Astuti,

Suprapti

2024

jati

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Peternakan di Indonesia merupakan salah satu sektor yang memberikan kontribusi cukup besar bagi perekonomian Indonesia. Peternakan berperan sebagai sektor utama dalam upaya pemenuhan kebutuhan dan menjaga ketersediaan pangan hewani untuk masyarakat di Indonesia. Upaya yang dapat dilakukan untuk meningkatkan dan mempertahankan produktivitas sektor peternakan antara lain dengan mengelompokkan wilayah berdasarkan karakteristiknya. Tujuan dari pengelompokan ini adalah agar informasi jenis ternak yang tersebar di setiap wilayah Jawa Barat menjadi lebih efisien dan spesifik. Salah satu cara untuk mengelompokkannya dapat ditemukan dengan menggunakan analisis cluster. Analisis cluster adalah teknik analisis statistik multivariat yang mengklasifikasikan objek ke dalam kelompok berdasarkan propertinya. Tujuan penulisan laporan ini adalah untuk mengkaji hasil cluster yang dibentuk dengan menggunakan algoritma K-Medoids. Algoritma ini merupakan metode yang kuat dalam mengelompokan wilayah-wilayah berdasarkan karakteristik peternakan yang berbeda untuk cluster kabupaten/kota di Provinsi Jawa Barat Tahun 2022. Dari hasil analisis cluster menggunakan K-Medoids pada tahun 2022 diperoleh 24 kabupaten/kota pada cluster 1, 2 kabupaten pada cluster 2 dan 1 kabupaten pada cluster 3. Hasil dari analisis cluster ini dapat memberikan wawasan yang berharga bagi pengambil keputusan dalam perencanaan dan pengembangan sektor peternakan. Dengan demikian, upaya-upaya yang dibutuhkan untuk meningkatkan produktivitas peternakan di Provinsi Jawa Barat dapat lebih terarah dan efektif.

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Section: Pendahuluanunclassified

“…Paper [7] Penelitian ini menggunakan algoritma K-Medoids untuk mengelompokkan data produksi perikanan tangkap di Kabupaten Aceh Utara. Hasilnya menunjukkan bahwa data produksi perikanan tangkap berhasil dikelompokkan menjadi tiga klaster: rendah, sedang, dan tinggi.…”

Section: Tinjauan Pustakaunclassified

Penerapan Algoritma K-Medoids Dalam Menentukan Cluster Kabupaten Dan Kota Berdasarkan Populasi Peternakan Di Provinsi Jawa Barat

Khoirunisa,

Astuti,

Suprapti

2024

jati

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“…The clusterization method is often used in grouping separate data into several groups with relatively similar resemblance (Fajriana, 2021). The relationship between the clusterization method and the context of capture fisheries management is that it can be used to identify and analyze the characteristics of fish species, fish populations, or fishing grounds that are similar to one another (Picaulima et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatial Clusterization of Fishing Area in Bulu Waters, Tuban Regency

Sari,

Kusuma,

Harlyan

et al. 2023

JEEST

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The various characteristics of catches and fishing gear provide distinct advantages for fishermen, but these characteristics can cause other problems, namely pressure on stock availability caused by an increase in the number of catches and the use of various types of fishing gear and the efficiency provided. There needs to be good management to handle this. The purpose of this research is to find out the clustering of fishing areas based on the catch and fishing gear type. This study can be regarded as a reference in sustainable fisheries management. The research utilized descriptive methods and case studies at PPP Bulu. The analytical method used is the Shannon-Wienner index of diversity (S-W,H'), Margalef richness (R') and clusterization. Clustering analysis was performed by using Hierachical Cluster Analysis (HCA) method and Euclidean distance. The results showed that the value of diversity has a range of 0-2 and the value of wealth has a range of 1-10. The results of the clustering show that there were 3 clusters of fishing areas formed based on the catch, while no clusters of fishing areas are formed based on the type of fishing gear operated.

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“…Pangestu and Ridwan (2022) apply the K-Means algorithm to group customers based on the volume of water sold[3] Simanjuntak (2022). uses the clustering method to analyze customer satisfaction with PDAM services[4] Fajriana (2021). analyzed the K-Medoids algorithm in the capture fisheries production clustering system[5].Lestari et al (2023) used the K-Means clustering algorithm for data mining on clean water sales at SPAM Akidah [6][5].…”

mentioning

confidence: 99%

“…uses the clustering method to analyze customer satisfaction with PDAM services[4] Fajriana (2021). analyzed the K-Medoids algorithm in the capture fisheries production clustering system[5].Lestari et al (2023) used the K-Means clustering algorithm for data mining on clean water sales at SPAM Akidah [6][5]. Ula et al (2023) applied KNN to determine new PDAM customers and K-Means clustering based on region [7].…”

mentioning

confidence: 99%

Application of the K-Medoids Algorithm in Clustering PAM Customers Based on Provinces in Indonesia

Almazar,

Yel

2024

ijsecs

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Clean water is a basic need that is very important for human life. In Indonesia, many clean water companies supply water for community needs. Indonesia's government-owned clean water company has many customers spread across various provinces. This research aims to apply the k-medoids algorithm in clustering PAM customers based on provinces in Indonesia to determine which provinces are included in the low, medium, and high-level clusters of PAM customers. The data used in this research is on the number of PAM customers, the amount of water distributed, and the value of the water distributed from 1998 to 2021 to 34 provinces in Indonesia. This research obtained 3 clusters, namely low, medium, and high-level clusters determined by the number of PAM customers, the amount of water distributed, and the value of the water distributed in each province. Cluster 1 (C1) is a low-level cluster that obtained results from 19 provinces, Cluster 2 (C2) is a medium-level cluster that obtained results from 11 provinces, and Cluster 3 (C3) is a high-level cluster that obtained results from 4 provinces. And get a k-medoids cluster validation value of 0.014846718. In this way, it is hoped that the government can identify areas in each province that require improvement or improvement in making appropriate decisions regarding clean water resources to provide better services to customers

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Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap Kabupaten Aceh Utara

Cited by 3 publications

References 10 publications

Penerapan Algoritma K-Medoids Dalam Menentukan Cluster Kabupaten Dan Kota Berdasarkan Populasi Peternakan Di Provinsi Jawa Barat

Penerapan Algoritma K-Medoids Dalam Menentukan Cluster Kabupaten Dan Kota Berdasarkan Populasi Peternakan Di Provinsi Jawa Barat

Spatial Clusterization of Fishing Area in Bulu Waters, Tuban Regency

Application of the K-Medoids Algorithm in Clustering PAM Customers Based on Provinces in Indonesia

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