SKRIPSI Sistem Informasi

SEGMENTASI PENDUDUK MISKIN DI INDONESIA MENGGUNAKAN ALGORITMA K-MEANS

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SEGMENTASI PENDUDUK MISKIN DI INDONESIA MENGGUNAKAN
ALGORITMA K-MEANS
Agita Vidiasti Rivallinata1), Tubagus Mohammad Akhriza2), Dwi Safiroh Utsalina3)
Sistem Informasi, STMIK PPKIA Pradnya Paramita
agitavidiastirivallinata@gmail.com, akhriza@stimata.ac.id, utsalina@stimata.ac.id
Abstract
The Covid-19 pandemic has led to an increase in the number of poor people in Indonesia due to
government policies aimed at curbing the virus's spread. Grouping poverty levels in Indonesia is
crucial for policy-making. This study aims to classify poverty data in Indonesia based on attributes
such as GKM, GKNM, IkdK, and IKpK, utilizing the K-Means algorithm for data mining. The
results reveal three poverty clusters: low, medium, and high. Clustering the data before, during,
and after the pandemic without employing binning techniques did not lead to cluster shifts.
However, using binning resulted in cluster shifts in certain provinces in 2020 and 2022. Clustering
the data during the pandemic peak, towards normalcy, without binning, showed a shift from high
to low poverty levels in Maluku and East Nusa Tenggara provinces. On the other hand, applying
binning led to a shift from high to low poverty in East Nusa Tenggara province and from medium
to low poverty in Bengkulu province. The Silhouette Coefficient, used as an evaluation metric,
ranged from 0.54 to 0.59, indicating that the formed clusters have a good interpretation and are
close to 1.

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Ketersediaan

19510013

001.42 S Agt Skr SistemInformasi

Perpus STMIK

Tersedia

Informasi Detail

Judul Seri

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No. Panggil

001.42 S Agt Skr SistemInformasi

Penerbit

: .,

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

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Info Detail Spesifik

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Pernyataan Tanggungjawab

Agita Vidiasti Rivallinata

Versi lain/terkait

Lampiran Berkas

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