Clustering of Social Conditions in Batam, Indonesia Using K-Means Algorithm and Geographic Information System

Kurniawan, Dwi Ely; Fatulloh, Agus

doi:10.31227/osf.io/qc342

Cited by 6 publications

(6 citation statements)

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“…Penerapan algoritma Apriori [2] didorong oleh kebutuhan akan pendekatan yang lebih sistematis dan akurat dalam menganalisis data transaksi dan mengidentifikasi pola [3][4][5][6] preferensi pelanggan. Dengan memanfaatkan kekuatan data mining [7][8], penelitian ini berusaha untuk meningkatkan proses pengambilan keputusan bagi toko suku cadang [2], memungkinkan mereka untuk proaktif memenuhi kebutuhan pelanggan dan mengoptimalkan strategi manajemen inventaris mereka.…”

Section: Pendahuluanunclassified

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Alhillah,

Priatna,

Fitriyani

2023

JAIC

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The aim of this research is to determine recommended product packages for spare parts from an automotive parts supplier. Shop owners have faced challenges in meeting customer demands over the past few months, experiencing frequent stockouts of spare parts due to a manual transaction recording system and a manual checking system for spare parts storage. This inefficiency and lack of accuracy in managing in-demand spare parts prompted the application of the apriori algorithm, a data mining method. Data was collected from the total sales over the past three months, subsequently cleaned and transformed for manual and Python-based apriori calculations. The results, obtained through both manual and Python implementations of apriori, indicate that the two frequently occurring item sets are oil filters with a confidence value of 68% and air filters with a confidence value of 63%. Based on these findings, the study recommends spare parts stores to maintain higher stock levels of oil filters and air filters compared to other spare parts.

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Section: Pendahuluanunclassified

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Alhillah,

Priatna,

Fitriyani

2023

JAIC

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“…Diharapkan data tersebut dapat digunakan sebagai evaluasi Dinas Kesehatan Kabupaten Karawang. Pengelompokan ini di ambil berdasarkan kecamatan [3] di Kabupaten Karawang untuk mengetahui jumlah tenaga kesehatannya, jumlah tenaga kesehatan berdampak pada kualitas pelayanan kepada masyarakat, semakin lengkap tenaga kesehatan maka pelayanan atau kepuasan masyarakat akan lebih baik. Clustering dipilih untuk bisa mengelompokan jumlah tenaga kesehatan di kecamatan mana yang sudah lengkap, sedang, dan kurang lengkap, dalam penelitian ini menggunakan algoritma K-Means [4].…”

Section: Pendahuluan Pembangunan Kesehatan Sebagai Bagian Integral Da...unclassified

Clustering Tenaga Kesehatan Berdasarkan Kecamatan di Kabupaten Karawang Menggunakan Algoritma K-Means

Sitinjak¹,

Pangestu²,

Sari³

2022

JAIC

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Pembangunan kesehatan merupakan bagian dari Pembangunan Nasional yang pada hakekatnya adalah penyelenggaraan upaya kesehatan untuk mencapai kemampuan hidup sehat bagi setiap penduduk agar dapat mewujudkan derajat kesehatan yang optimal, masalah kesehatan yang ada pada masyarakat di Indonesia yaitu masih minimnya tenaga kesehatan pada setiap wilayah. Salah satunya di Kabupaten Karawang, Tenaga kesehatan yang tidak tercukupi di beberapa kecamatan yang ada di Karawang akan membuat masyarakat di kecamatan tersebut kesulitan untuk hidup sehat dan mengobati penyakitnya. Penelitian ini bertujuan untuk melakukan pengelompokan terhadap Kecamatan yang memiliki tenaga kesehatan yang masih kurang sehingga data tersebut dapat digunakan untuk peningkatan kualitas kesehatan. Penelitian ini mengunakan metode K-Means Clustering. Hasil pengolahan dataset tenaga kesehatan yang ada di Kabupaten Karawang menghasilkan 3 cluster, yaitu cluster 1 dengan tenaga kesehatan sedikit sebanyak 24 kecamatan, cluster 2 dengan tenaga kesehatan sedang sebanyak 4 kecamatan dan cluster 3 dengan tenaga kesehatan terbanyak yaitu 2 kecamatan.

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“…Cluster analysis is useful in some pattern-analytical exploration, clustering, and decision making (Hafiludien & Istiawan, 2018). Several studies related to cluster analysis include research conducted by Kurniawan and Fatulloh (2017) with the title "Clustering of Social Conditions in Batam, Indonesia Using K-Means Algorithm and Geographic Information System" which groups sub-districts in Batam City based on social indicators such as data poverty, divorce data, percentage of population with the highest high school education, population distribution by age, and morbidity. Based on these indicators, 4 groups were formed with different characteristics in each group.…”

Section: Literature Reviewmentioning

confidence: 99%

K-Means Clustering for Grouping Indonesia Underdeveloped Regions in 2020 Based on Poverty Indicators

Wahyuni

2021

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Poverty is still a problem in Indonesia, especially in underdeveloped areas. Underdeveloped areas are areas where the region and its people are less developed than other regions on a national scale. The classification of disadvantaged areas is determined by the president in the Presidential Regulation of the Republic of Indonesia Number 63 of 2020 concerning the Determination of Underdeveloped Regions of 2020-2024. Various policies need to be set by the government to overcome poverty in underdeveloped areas. Program planning strategies may be different for each region. Therefore, in order to achieve an optimal implementation of poverty alleviation programs, it is necessary to group the districts covered in underdeveloped areas in Indonesia based on poverty indicators. The data used is macro data from the characteristics of each region in disadvantaged areas obtained from regional publications in the figures for each district. From the results of the analysis of k means clustering formed three groups with different characteristics in each cluster. In cluster one, the focus of government policies is on employment and sanitation aspects, cluster two is on health, education, and employment aspects, cluster three is on all aspects because cluster three is the area with the highest percentage of poor people compared to the other two clusters. The high percentage of poor people is also followed by other poor aspects.

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Clustering of Social Conditions in Batam, Indonesia Using K-Means Algorithm and Geographic Information System

Cited by 6 publications

References 0 publications

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Implementation of Apriori Algorithm for Determining Spare Parts Product Recommendation Packages

Clustering Tenaga Kesehatan Berdasarkan Kecamatan di Kabupaten Karawang Menggunakan Algoritma K-Means

K-Means Clustering for Grouping Indonesia Underdeveloped Regions in 2020 Based on Poverty Indicators

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