Klasterisasi Menggunakan Agglomerative Hierarchical Clustering Untuk Memodelkan Wilayah Banjir

Pratikto, Ridzki Okta; Damastuti, Natalia

doi:10.31328/jointecs.v6i1.1473

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The application of AHC in segmenting barbershop customers, where in its research uses 2 distance parameters, namely Single Linkage and average linkage [12]. AHC is also carried out in detecting communities on Facebook social media [13], modeling flooded areas in East Java with an accuracy of 0.92 [14], and grouped pre-college academic history data and student graduation data [15] [16]. By looking at the ability of the AHC method in grouping data in previous studies, in this study the AHC method was used in grouping MSME data.…”

Section: Redemptionmentioning

confidence: 99%

Strategy for improving and empowering MSMEs through grouping using the AHC method

Zahrotun,

Amanatullah,

Linarti

et al. 2024

SISFOKOM

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The high number of migrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in Culinary and Handicrafts. The large amount of data collected by the Cooperative Office, which reached thousands, caused inas to have difficulties in determining what training was needed by MSMEs and also difficulties in choosing which MSMEs would receive training held by the Cooperative Office. In addition, the Yogyakarta Cooperatives and UMKM Office had difficulties in selecting which UMKM needed to receive these trainings. Grouping can be used as a strategy in selecting MSMEs and determining training according to their individual needs. The purpose of this study was to group SMEs using the Agglomerative Hierarchical Clustering Single Linkage method and its application to provide recommendations for MSME groups to the Yogyakarta Cooperative and MSME Office. The results of the recommendations for the number of groups can be used in providing implementation, design, and evaluation of the development and empowerment of MSME data in the City of Yogyakarta. This study uses the Agglomerative Hierarchical Clustering Single Linkage method. The stages in this research are Load Data, Cleaning Data, Data Selection, Transformation Data, Clustering Process with AHC single linkage, Silhouette Coefficient, and Knowledge Representation. This research resulted in 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Handicraft MSME data. The results of the silhouette score test in the Culinary Sector are included in the strong structure category with a value of 0.79 and the Crafts Sector is included in the Medium Structure category with a value of 0.615. From the number of these groups, recommendations were obtained for improving a service in increasing MSMEs, especially those with a turnover of less than 10 million, marketing purposes within the Yogyakarta area, and not having financial assistance from the government. The high number of immigrants in the city of Yogyakarta has resulted in increased opportunities for Micro, Small and Medium Enterprises (MSMEs) in the Culinary and Crafts sector. The large number of MSMEs creates increasingly higher competitiveness. Apart from that, the large amount of data collected by the Department of Cooperatives and MSMEs, which reaches thousands, causes the Department to have difficulties in efforts to improve and empower these MSMEs. Grouping is one method that can be used as a strategy in mapping MSMEs, especially in efforts to improve and empower MSMEs through training conducted by the Department. The aim of this research is to group MSMEs using the Agglomerative Hierarchical Clustering (AHC) method in an effort to achieve strategies for improving and empowering MSMEs. The focus of this research is[a1] MSMEs in the craft sector and MSMEs in the culinary sector. The results of this research provide 2 group recommendations from a total of 1336 Culinary MSME data and 3 group recommendations from a total of 145 Craft MSME data. The silhouette score test results in the Culinary Sector are in the strong structure category with a value of 0.79 and in the Crafts Sector are in the Medium Structure category with a value of 0.615. From the number of groups in the two MSMEs, strategies were obtained to improve and empower MSMEs, especially those with a turnover of less than 10 million, marketing objectives within the Yogyakarta area, and not having capital assistance from the government. [a1]the result of the revision of the Abstract

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Section: Redemptionmentioning

confidence: 99%

Strategy for improving and empowering MSMEs through grouping using the AHC method

Zahrotun,

Amanatullah,

Linarti

et al. 2024

SISFOKOM

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“…Klasterisasi data dengan metode Hierarchy Clustering Klasterisasi adalah suatu proses mengelompokkan sejumlah data menjadi beberapa kelompok sehingga object dapat dikelompokkan berdasarkan kecenderungan kesamaan dan perbedaan. Istilah lain untuk klasterisasi adalah unsupervised classification karena klastering bersifat dipelajari dengan memperhatikan object (Pratikto & Damastuti, 2021).Metode ini membangun klaster dengan mempartisi data secara rekursif baik dengan menggunaka urutan dari data atas ke bawah ataupun bawah ke atas (Fadliana & Rozi, 2015), (Nisha & Kaur, 2016). 1.…”

Section: Pra Pemrosesan Dataunclassified

Klasterisasi Data Untuk Sistem Rekomendasi Pemilihan Sekolah Dasar Di Wilayah Surakarta Degan Metode Hierarchy Clustering

Rokhmah,

Pakarti

2023

JIS

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The role of parents is very important in determining the school for their children, especially at elementary school level. There are many factors that parents consider when choosing an elementary school, including school quality, distance to school, school fees, content of religious education, school performance and school facilities. The aim of this research is to develop a data clustering concept using Hierarchy Clustering method for an elementary school recommendation system in the city of Surakarta, so that it can help parents in determining the elementary school for their children. In this study, agglomerative clustering with Ward linkage was used, this is because among other linkage methods (Single linkage, ward linkage, flexible linkage and complete linkage), Ward linkage has the highest Silhouette index value, namely 0.666. Cluster optimization in this research was determined by looking at the Silhouette index graph by comparing the Silhouette cluster values 1 to 10 for each linkage method, and obtained the cluster optimization value was 4, so the number of groupings used was 4 clusters. The accuracy of the recommendation system is tested using the Mean Average Error (MAE) statistical calculation, with an MAE value of 0.2, which means the recommendation system is suitable for use.

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“…Beberapa penelitian yang dilakukan mengenai penggelompokan wilayah banjir seperti yang di lakukan oleh Pratikto R.O dengan penelitan yang berjudul Klasterisasi Menggunakan Agglomerative Hierarchical Clustering Untuk Memodelkan Wilayah Banjir [4]. Dan juga penelitian yang dilakukan oleh Khomsiyah J.Dkk dengan penelitian yang berjudul Penerapan Algoritma K-Means Clustering untuk pengelompokan wilayah rawan banjir [5].…”

Section: Pendahuluanunclassified

Clustering Daerah Banjir Di Jawa Timur Dengan Algoritma Fuzzy C-Means

Ifadah¹,

Ratnasari²,

Novitasari³

2022

JDI

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Bencana alam seperti banjir merupakan peristiwa yang dapat terjadi setiap waktu untuk dapat mengurai resiko perlu diadakan mitigasi bencana. Namun pada tiap daerah memiliki karakteristik bencana banjir yang berbeda. Penelitian ini dimaksud untuk melakukan clustering kejadian banjir yang terjadi di Jawa Timur. Proses pengelompokan dilakukan dengan menggunakan metode Fuzzy C Means. Variabel yang digunakan pada penelitia ini yaitu jumlah kejadian, korban, dan kerusakan. Proses pengklasteran dan kejadian bencana dilakukan terhadap wilayah kabupaten/kota yang ada di Jawa Timur selama 4 tahun terakhir. Untuk pengklasteran ditentukan parameter dari c=3, w=2, MaxIter=100, dan 𝜺=0,0001. Diperoleh hasil algoritma Fuzzy C-means cluster 1: Pasuruan, cluster 2: Bojonegoro,

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Klasterisasi Menggunakan Agglomerative Hierarchical Clustering Untuk Memodelkan Wilayah Banjir

Cited by 3 publications

References 7 publications

Strategy for improving and empowering MSMEs through grouping using the AHC method

Strategy for improving and empowering MSMEs through grouping using the AHC method

Klasterisasi Data Untuk Sistem Rekomendasi Pemilihan Sekolah Dasar Di Wilayah Surakarta Degan Metode Hierarchy Clustering

Clustering Daerah Banjir Di Jawa Timur Dengan Algoritma Fuzzy C-Means

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