Implementasi Metode K-Means  Dalam Pengklasteran Daerah Pungutan Liar Di Kabupaten Sukabumi   (Studi Kasus : Dinas Kependudukan Dan Pencatatan Sipil)

Sembiring, Falentino; Octaviana, Octaviana; Saepudin, Sudin

doi:10.36787/jti.v14i1.165

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…Centroid is the data center point for calculating the vector mean as a centroid. In applying the K-means algorithm, the midpoint or centroid value is generated from the data obtained from each cluster [11]. Clustering steps contained in the K-Means algorithm [12].…”

Section: Image 2 Decription Of Data Usedmentioning

confidence: 99%

Implementation of Data Mining by Using K-Means to Classify Marriage Age

Handoko

Nasution

2023

JURTEKSI

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Marriage is a husband and wife relationship between a man and a woman to form a family. There are several conditions in marriage that must be fulfilled both religiously and legally in force in Indonesia. To carry out the marriage, the prospective bride and groom must register at the nearest Religious Affairs Office (KUA), KUA is an institution established by the government to handle marriage matters. At marriages, various age groups are often found registering at the KUA. This research was conducted using the Data Mining technique through the K-Means Clustering Model to determine the age grouping of marriage which aims to make it easier for the Office of Religious Affairs in educating the prospective bride and groom from a future perspective and an economic perspective in terms of having a child. The research dataset is data on prospective wedding brides at KUA Rawang Lama, Panca Arga in 2022 with a total of 102 samples, by forming 3 clusters, namely: the Ideal cluster of 76 prospective wedding brides (age 19-30 based on husband's age and age 18-25 based on age wife), a good cluster of 20 prospective marriage brides (age 28-44 based on husband's age and age 24-37 based on wife's age), and a risky cluster of 6 prospective marriage brides (age 49-72 based on husband's age and age 39-58 based on wife's age), and produces a Silhouette Score of 0.57. Keywords: clustering; data mining; k-means; marriage Abstrak: Pernikahan merupakan hubungan sebagai suami dan istri antara seorang laki-laki dan perempuan untuk membentuk sebuah keluarga. Terdapat beberapa syarat dalam pernikahan yang wajib dipenuhi baik secara agama maupun secara hukum yang berlaku di Indonesia. Untuk melakukan pernikahan, calon kedua mempelai harus mendaftarkan diri pada Kantor Urusan Agama (KUA) terdekat, KUA merupakan lembaga yang dibentuk oleh pemerintah untuk menangani masalah pernikahan. Pada pernikahan sering ditemukan berbagai kalangan umur yang mendaftarkan diri di KUA. Penelitian ini dilakukan dengan menggunakan teknik Data Mining melalui Model K-Means Clustering untuk menentukan pengelompokkan umur pernikahan yang bertujuan untuk mempermudah pihak KUA dalam mengedukasi calon mempelai pernikahan dalam sudut pandang masa depan dan sudut pandang ekonomi dalam hal memiliki seorang anak. Dataset penelitian ini adalah data calon mempelai pernikahan pada KUA Rawang Lama, Panca Arga pada tahun 2022 sebanyak 102 sampel, dengan membentuk 3 klaster yaitu : cluster Ideal sebanyak 76 calon mempelai pernikahan (usia 19-30 berdasarkan umur suami dan usia 18-25 berdasarkan umur istri), cluster baik sebanyak 20 calon mempelai pernikahan (usia 28-44 berdasarkan umur suami dan usia 24-37 berdasarkan umur istri), dan cluster beresiko sebanyak 6 calon mempelai pernikahan (usia 49-72 berdasarkan umur suami dan usia 39-58 berdasarkan umur istri), dan menghasilkan Silhouette Score 0.57. Kata kunci: clustering; data mining; k-means; pernikahan

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Section: Image 2 Decription Of Data Usedmentioning

confidence: 99%

Implementation of Data Mining by Using K-Means to Classify Marriage Age

Handoko

Nasution

2023

JURTEKSI

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“…45,47,54,64,76,81,91,110,122,129,135,147,158,166,172,191,198. Cluster 2 2,6,8,9,12,18,19,24,27,28,30,31,33,34,35,36,38,42,44,50,55,58,59,60,61,62,63,66,67,72,73,74,75,77,82,85,87,88,89,90,93,94,96,97,103,105,106...…”

Section: Iterasi Ke Duamentioning

confidence: 99%

Penerapan Data Mining untuk Mengcluster Data Penduduk Miskin Menggunakan Algoritma K-Means di Dusun Bagik Endep Sukamulia Timur

Suhartini¹,

Yuliani²

2021

jit

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The occurrence of poverty in the community is caused by a condition of the economic inability of the head of the family to meet the primary / basic needs of his family, namely the need for clothing, food, shelter and education. The poor community itself can be found in almost every country, city and region, for example in one of the Bagik Endep hamlets of East Sukamulia Village. Based on these conditions, it is necessary to carry out clustering to assist the village government in grouping poor families, so that assistance can be distributed appropriately. By observing the above problems, Data Mining is needed to classify aid recipients using the K-Means method in clustering the poor. Where the K-Means Clustering Algorithm method aims to classify population data in the East Sukamulia region who are said to be classified as poor. The data used is data on the population of East Sukamulia in 2019, amounting to 200 data with 9 attributes, namely the name of the population, occupation, income / month, the number of children attending elementary school, the number of children attending junior high school, the number of children attending high school, the number of children attending college , the number of children who are not in school and the number of family members. Based on the results of tests carried out by applying the K-Means algorithm, the results obtained are Cluster 1 totaling 18 residents with the criteria of high economic population, Cluster 2 totaling 72 residents with moderate economic population criteria, and Cluster 3 totaling 110 residents with low economic population criteria. The K-Means method is expected to be able to assist the government of Sukamulia Timur Village in making decisions and finding the information needed to solve problems in recording the poor population accurately

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“…Adanya oknum apparat, calo dan pungli (pungutan liar) sebagai pihak yang menjajikan kepengurusan dokumen administrasi kependudukan lebih cepat, masih banyak dijumpai di sebagian besar kecamatan di Indonesia (Haykal et al, 2020;Mafutra & Effiyaldi, 2021;Mujianto et al, 2021;Ratnawaty, 2021;Sembiring et al, 2020). Proses penerbitan yang lambat tidak sesuai dengan target pelayanan juga ditemukan diberbagai wilayah (Masruroh & Rahmaningtyas, 2020;Sopian & Israhadi, 2021).…”

Section: Pendahuluanunclassified

Pengaruh Kinerja Aparatur Terhadap Kualitas Pelayanan Pembuatan Kartu Keluarga di Kecamatan Tambun Utara Kabupaten Bekasi

Rachman¹,

Kurniansyah²,

Ramdani³

et al. 2021

JMSDA

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This study aims to determine the effect of apparatus performance on the service quality of making family cards in Tambun Utara district, Bekasi district. This research is a quantitative research through survey method with correlation regression data analysis technique. Using the Performance questionnaire instrument to measure the performance of the apparatus includes 4 aspects, namely: quality, quantity, use of time, cooperation and the service quality questionnaire covers 5 aspects, namely: physical form (responsiable), reliability (reability), responsiveness (responsiviness), assurance (assurance. ) and empathy (emphaty). The sample consisted of 122 family heads who took care of making family cards in North Tambun District in 2019-2010. The results showed that there is a significant influence between the performance of the apparatus on the quality of service for making family cards in Tambun Utara District, Bekasi Regency, which is equal to 52.8%, the remaining 47.2% is influenced by other factors outside the performance of the apparatus. Based on the results of the above research, it is concluded that there is a strong influence between the performance of the apparatus on the quality of service for making family cards in Tambun Utara District, Bekasi Regency.

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Implementasi Metode K-Means Dalam Pengklasteran Daerah Pungutan Liar Di Kabupaten Sukabumi (Studi Kasus : Dinas Kependudukan Dan Pencatatan Sipil)

Cited by 6 publications

References 1 publication

Implementation of Data Mining by Using K-Means to Classify Marriage Age

Implementation of Data Mining by Using K-Means to Classify Marriage Age

Penerapan Data Mining untuk Mengcluster Data Penduduk Miskin Menggunakan Algoritma K-Means di Dusun Bagik Endep Sukamulia Timur

Pengaruh Kinerja Aparatur Terhadap Kualitas Pelayanan Pembuatan Kartu Keluarga di Kecamatan Tambun Utara Kabupaten Bekasi

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