Mengelompokkan Garis Kemiskinan Menurut Provinsi Menggunakan Algoritma K-Medoids

Wahyuli, Disty; Handrizal, Handrizal; Parlina, Iin; Windarto, Agus Perdana; Suhendro, Dedi; Wanto, Anjar

doi:10.30645/senaris.v1i0.51

Cited by 8 publications

(11 citation statements)

References 4 publications

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“…In Indonesia, the high poverty rate consists of 23 provinces and the low poverty rate consists of 11 provinces. It is hoped that this research can provide input to the government in increasing employment so that it can improve the economy of the people in Indonesia (Wahyuli et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

K-Means Algorithm For Clustering Poverty Data in Bangka Belitung Island Province

Sugianto¹,

Bokings²

2021

CNAHPC

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The Central Bureau of Statistics is a non-ministerial government institution that reports directly to the President. Based on data from The Central Bureau of Statistics in September 2019, the wealth rate in Indonesia was 9.22% and the number of indigent people in Indonesia reached 24.79 million. The poverty rate in the Bangka Belitung Islands Province was low compared to the national level. This is evidenced by 4.62% of people in Bangka Belitung Island Province were indigent people, which is lower than the national average of 9.22%. The data mining techniques using the K-Means Clustering method are used for this study. The research data was taken from the website of the BPS from 2014-2019 which consisted of 7 districts and/or cities with 3 variables. The variables used are the number of indigent people (in thousands), the average length of school education (years), and adjusted per capita expenditure (thousand rupiahs/year). All data is processed by Rapidminer and 3 clusters are carried out, namely: medium cluster level 0, high cluster level 1, and low cluster level 2. Cluster 0 contains districts/cities whose people have the longest average school time, high per capita expenditure, and a large number of indigent people. Cluster 1 contains districts/cities whose people have a short average school time, low per capita expenditure, a moderate number of indigent people. Cluster 2 contains districts/cities whose people have an average school time, moderate per capita expenditure, a small number of indigent people. Based on the result, the government can prioritize Kabupaten Bangka, Kabupaten Bangka Barat, Kabupaten Bangka Selatan in assisting, especially in the cost of education scholarships and social funds as well as other infrastructure improvements for the welfare of the inhabitants of Bangka Belitung Islands Province.

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Section: Literature Reviewmentioning

confidence: 99%

K-Means Algorithm For Clustering Poverty Data in Bangka Belitung Island Province

Sugianto¹,

Bokings²

2021

CNAHPC

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“…Hasil akhir yang di tampilkan merupakan pengelompokan garis kemiskinan menurut provinsi. Dimana terdapat 2 cluster, yang pertama cluster dengan nilai kemiskinan terendah dimana di dalamya terdapat 11 provinsi dan yang ke 2 cluster dengan jumlah kemiskinan tertinggi sebanyak 23 provinsi [8]. Selanjutnya dilakukan penelitian Pengklasteran Dokumen Tentang Dispensasi Nikah.…”

Section: Pendahuluanunclassified

Klasterisasi Tingkat Masa Studi Tepat Waktu Mahasiswa Menggunakan Algoritma K-Medoids

Firzada

Yuhandri

2021

JSisfotek

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The period of study on time is one of the parameters of a student's success in completing college to obtain a bachelor's degree. A student is said to have completed his studies on time if he is able to complete his studies less than or equal to the predetermined time. Academic Provides facilities to find out the estimated time of student graduation. By providing information on which students are included in the cluster, they can complete their studies on time and which students do not complete their studies on time. In this study, the data processed were data from students who had graduated in the previous year. Then the data is processed using rapidminer software. This study applies the K-Medoids algorithm in clustering. The result of testing this method is to determine the student clusters who can complete the study period on time and the student clusters who cannot complete the study period on time. This research is expected to contribute to the campus in evaluating the tendency of students to complete their studies on time or not. The results of the evaluation of performance can produce information for study programs, lecturers and students in making policies

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“…Kemiskinan adalah masalah global yang sering dihadapi dan menjadi perhatian dunia. Kemudian, arti lain dari kemiskinan juga dapat dikatakan seperti masyarakat yang menganggur dan kurangnya lahan produktif sebagai asset pendapatan untuk masyarakat miskin atau masyarakat yang kurang mampu untuk memperoleh suatu kebutuhan paling dasar yaitu makan, air dan tempat tinggal [1].…”

Section: Pendahuluanunclassified

“…Berbagai permasalahan yang muncul akan menyebabkan terjadinya pengangguran, kriminalitas, sulit untuk mendapatkan tempat tinggal yang baik, dan kemacetan. Dengan demikian, hal tersebut disebabkan karena jumlah penduduk yang semakin meningkat disuatu kabupaten/kota [1]. Provinsi Jawa Barat merupakan salah satu wilayah yang memiliki permasalahan kemiskinan yang meningkat, hal tersebut dapat dilihat berdasarkan grafik pada Gambar 1 [2].…”

Section: Pendahuluanunclassified

“…Berdasarkan faktor-faktor mengenai kemiskinan bahwa pada tahun 2017, yang meliputi rata-rata lama sekolah, presentase rumah tangga yang bekerja di pertanian, tingkat pengangguran terbuka, dan pengeluaran rumah tangga per kapita menggunakan metode average linkage hierarchical clustering menunjukan bahwa diperoleh dua kelompok kabupaten/kota di Pulau Jawa dengan tingkat kemiskinan rendah dan tinggi. Adapun kelompok tingkat kemiskinan rendah terdiri dari kota besar di Indonesia, sedangkan kelompok dengan tingkat kemiskinan tinggi meliputi wilayah pedesaan pada kabupaten/kota [1].…”

Section: Penelitian Terdahuluunclassified

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Klasterisasi Faktor-Faktor Kemiskinan Di Provinsi Jawa Barat Menggunakan K-Medoids Clustering

Safitri

Aristawidya

Faradilla

2021

JaMES

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Difficulty in meeting basic food and non-food need measured on the expenditure side is referred to as poverty. Thus, the purpose of this study is to find out the poverty group by district/city in West Java Province. This method used is the K-Medoids Clustering algorithm because K-Medoids can handle data that is very sensitive to outliers. The results of the study showed that three groups were obtained, with group one having as many as eight regions, group two having members as many as 15 regions, and group three having members as many as four regions. Abstrak Kesulitan untuk memenuhi kebutuhan dasar makanan dan selain makanan diukur berdasarkan sisi pengeluaran disebut sebagai kemiskinan. Sehingga, tujuan penelitian ini yaitu mengetahui kelompok kemiskinan menurut kabupaten/kota di Provinsi Jawa Barat. Adapun metode yang digunakan yaitu algoritma K-Medoids Clustering, dikarenakan K-Medoids mampu mengatasi data yang sangat sensitif terhadap outlier. Hasil dari penelitian menunjukan bahwa diperoleh tiga kelompok, dengan kelompok satu memiliki anggota sebanyak delapan wilayah, kelompok dua memiliki anggota sebanyak yaitu 15 wilayah, dan kelompok tiga memiliki anggota sebanyak empat wilayah.

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Mengelompokkan Garis Kemiskinan Menurut Provinsi Menggunakan Algoritma K-Medoids

Cited by 8 publications

References 4 publications

K-Means Algorithm For Clustering Poverty Data in Bangka Belitung Island Province

K-Means Algorithm For Clustering Poverty Data in Bangka Belitung Island Province

Klasterisasi Tingkat Masa Studi Tepat Waktu Mahasiswa Menggunakan Algoritma K-Medoids

Klasterisasi Faktor-Faktor Kemiskinan Di Provinsi Jawa Barat Menggunakan K-Medoids Clustering

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