Pengelompokan PMKS menggunakan Self Organizing Maps dengan perbaikan missing value Naïve Bayes Imputation

Noor Hidayah; Muliadi -; Irwan Budiman; Dodon Turianto Nugrahadi; Rudy Herteno

doi:10.14710/jtsiskom.2022.14424

DOI: https://doi.org/10.14710/jtsiskom.2022.14424

Pengelompokan PMKS menggunakan Self Organizing Maps dengan perbaikan missing value Naïve Bayes Imputation

Noor Hidayah , Muliadi -

, Irwan Budiman, Dodon Turianto Nugrahadi

, Rudy Herteno

Universitas Lambung Mangkurat, Indonesia

Received: 7 Mar 2022; Published: 24 Sep 2024.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:

Abstract

Penyandang Masalah Kesejahteraan Sosial (PMKS) merupakan permasalahan pada kelompok masyarakat yang memiliki kesulitan dalam menjalankan fungsi sosial. Penelitian dilakukan untuk mengetahui karakteristik permasalahan di wilayah Kalimantan Selatan dengan menggunakan klasterisasi. Metode klasterisasi yang digunakan adalah SOM dan pengisian data kosong menggunakan NBI yang dibandingkan dengan Metode Statistik (Mean, Median, dan Modus). Proses dimulai dari mengisian data kosong dengan NBI dan Metode Statistik, dilanjutkan dengan klaster SOM dan hasil klaster dievaluasi menggunakan DBI. Hasil yang didapatkan adalah perbaikan NBI menempati hasil klasterisasi terbaik dengan nilai 0,032 pada pembagian 2 klaster. Klaster pertama berjumlah 8 wilayah yaitu Tanah Laut, Kota Baru, Tapin, Hulu Sungai Selatan, Hulu Sungai Tengah, Hulu Sungai Utara, Tabalong, dan Tanah Bumbu. Klaster kedua berjumlah 5 wilayah yaitu Banjar, Barito Kuala, Balangan, Banjarmasin, dan Banjarbaru. Tingkat prioritas yang diperoleh dari rata-rata klaster didapatkan bahwa klaster kedua sebagai prioritas pertama.

Note: This article has supplementary file(s).

Fulltext | Research Instrument

Clustering Self Organizing Maps (SOM) dengan perbaikan missing value menggunakan Naïve Bayes Imputation pada pengelompokan wilayah Penyandang Masalah Kesejahteraan Sosial (PMKS)

Subject
Type	Research Instrument
	Download (1MB) Indexing metadata

Email colleagues

Keywords: Penyandang Masalah Kesejahteraan Sosial (PMKS); Self Organizing Maps (SOM); Naïve Bayes Imputation (NBI); Metode Statistik

Article Metrics:

Article Info

Section: Original Research Articles

Language : ID

In [IN PRESS] Volume 10, Issue 4, Year 2022 (October 2022)

Most viewed articles

Expert System for Diagnosis of Plant Pest and Disease Horticulture with Forward and Backward Chaining Inference Pengembangan Sistem Pakar Untuk Diagnosis Penyakit Hepatitis Berbasis Web Menggunakan Metode Certainty Factor Teka-teki Unsur Kimia sebagai Media Pembelajaran Kimia Interaktif bagi siswa SMA Kelas X Berbasis Android K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes Perancangan Perangkat Lunak untuk Analisis Tingkat Fokus pada Citra Mikroskop Digital Menggunakan Proses Ekstraksi Ciri More articles

Most cited articles

Web Application of Lecturer Performance Assesment on Teaching Process: A Case Study at Internal Quality Assurance Agency of Institut Teknologi Padang Genetic Algorithm for Optimizing Food Composition for Hypertension Patients Perancangan Sistem Manajemen Restoran dengan Aplikasi Pemesanan Restoran Berbasis Mobile dalam Jaringan Lokal Decision Support System in Recommending Best Unit in PDAM Tirta Lihou Using Promethee Method Optimal Frequency Control System On Wind-Diesel Hybrid Generator With Imperialist Competitive Algorithm More cited articles

C. I. Basuki, A. Arwan, E. Muhammad, and A. Jonemaro, “Pengembangan sistem monitoring penyandang masalah kesejahteraan sosial berbasis web ( Studi kasus : Dinas Sosial Kota Malang ),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 3, pp. 709–716, 2020
I. Hidayatin, S. Adinugroho, and C. Dewi, “Pengelompokan wilayah berdasarkan Penyandang Masalah Kesejahteraan Sosial (PMKS) dengan Optimasi Algoritme K-Means menggunakan Self Organizing Map (SOM),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 8, pp. 7524–7531, 2019, [Online]. Available: http://j-ptiik.ub.ac.id/index.php/jptiik/article/download/5881/2852
D. N. Firmansyah, S. Adinugroho, and B. Rahayudi, “Kesejahteraan sosial menggunakan Algoritme Self-Organizing Maps dengan perbaikan Missing Value K-Nearest Neighbors,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 7, pp. 7205–7213, 2019
F. N. S. Damanik, “Implementasi Algoritma Self Organizing Mapping dalam pencarian jodoh,” Jurnal Stiondo Profesional, vol. VI, no. September, pp. 140–156, 2020
A. F. Solikin, K. Kusrini, and F. W. Wibowo, “Analisis cluster data interkomparasi anak timbangan dengan Algoritma Self Organizing Maps,” Jurnal Teknologi Informasi dan Sistem Informasi, vol. 7, no. 2, pp. 435–448, 2021,doi: 10.28932/jutisi.v7i2.3698
Halim, N. Nurul, and E. Widodo, “Clustering dampak gempa bumi di indonesia menggunakan kohonen self organizing maps,” Prosiding. SI MaNIS (Seminar Nasional Integral Matematika dan Nilai Islam., vol. 1, no. 1, pp. 188–194, 2017, [Online]. Available: http://conferences.uinmalang.ac.id/index.php/SIMANIS/article/view/62
N. Y. Kusrahman, I. Purnamasari, and L. M. Komputasi, “Optimasi Self-Organizing Map menggunakan Particle Swarm Optimization untuk mengelompokkan desa/ kelurahan tertinggal di Kabupaten Kutai Kartanegara Provinsi Kalimantan Timur ( Studi kasus : Data Potensi Desa Tahun 2018 ),” Jurnal Eksponensial, vol. 11, no. 2, pp. 139–144, 2020
R. Umar, A. Fadlil, and R. R. Az-Zahra, “Self Organizing Maps (SOM) untuk pengelompokkan jurusan di SMK,” Khazanah Informatika Jurnal Ilmu Komputer dan Informatika, vol. 4, no. 2, p. 131, 2018, doi: 10.23917/khif.v4i2.7044
I. Irwan, A. Y. Hashari, H. Ihsan, and A. Zaki, “Penggunaan Self Organizing Map dalam pengelompokan tingkat kesejahteraan masyarakat,” Jambura Jurnal Probability and Statistics, vol. 1, no. 2, pp. 57–68, 2020, doi: 10.34312/jjps.v1i2.7266
M. D. Kartikasari, “Self-Organizing Map menggunakan Davies-Bouldin Index dalam pengelompokan wilayah Indonesia berdasarkan konsumsi pangan,” Jambura Journal Mathematics., vol. 3, no. 2, pp. 187–196, 2021, doi: 10.34312/jjom.v3i2.10942
A. Sindar and M. Sitorus, “Machine learning prediksi karakter menggunakan hastag bahasa generasi millenial di sosial media,” IJAI (Indonesian Journal Applied. Informatics), vol. 7, no. 1, pp. 37–72, 2015, [Online]. Available: https://www.researchgate.net/publication/269107473_What_is_governance/link/548173090cf22525dcb61443/download%0Ahttp://www.econ.upf.edu/~reynal/Civilwars_12December2010.pdf%0Ahttps://thinkasia.org/handle/11540/8282%0Ahttps://www.jstor.org/stable/41857625
A. Badruttamam, S. Sudarno, and D. A. I. Maruddani, “Penerapan Analisis Klaster K-Modes dengan validasi Davies Bouldin Index dalam menentukan karakteristik kanal youtube di Indonesia (Studi kasus: 250 kanal youtube Indonesia teratas menurut Socialblade),” Journal Gaussian, vol. 9, no. 3, pp. 263–272, 2020, doi: 10.14710/j.gauss.v9i3.28907
M. Lutfi and M. Hasyim, “Penanganan data missing value pada kualitas produksi jagung dengan menggunakan metode K-NN Imputation pada algoritma C4.5,” Jurnal Resistor, vol. 2, no. 2, 2019
M. Gopal Krishna, N. Durgaprasad, S. Deepa Kanmani, G. Sravan Reddy, and D. Revanth Reddy, “Comparative analysis of different imputation techniques for handling missing dataset,” International Journal Innovative Technology and Exploring Engineering, vol. 8, no. 7, pp. 347–351, 2019
P. Madley-Dowd, R. Hughes, K. Tilling, and J. Heron, “The proportion of missing data should not be used to guide decisions on multiple imputation,” Journal of Clinical Epidemiology, vol. 110, pp. 63–73, 2019, doi: 10.1016/j.jclinepi.2019.02.016
A. Ilham, “Hybrid metode boostrap Dan teknik imputasi pada metode C4-5 untuk prediksi penyakit ginjal kronis,” Jurnal Statistika, vol. 8, no. 1, pp. 43–51, 2020, [Online]. Available: https://jurnal.unimus.ac.id/index.php/statistik/article/view/5765
S. I. Khan, A. Sayed, and L. Hoque, “SICE : an improved missing data imputation technique,” Journal of Big Data, vol. 7, pp. 7–37, 2020, doi: 10.1186/s40537-020-00313-w
A. R. Alfarisi, H. Tjandrasa, and I. Arieshanti, “Perbandingan performa antara imputasi metode konvensional dan imputasi dengan algoritma Mutual Nearest Neighbor,” Jurnal Teknik Pomits., vol. 2, no. 1, pp. 1–4, 2013
B. K. Khotimah and H. Suprajitno, “Modeling naïve bayes imputation classification for missing data,” IOP Conf. Series: Earth Environmental. Science, 2019, doi: 10.1088/1755-1315/243/1/012111
A. S. Arifianto, D. R. Hartadi, and A. N. N. ES, “Prediksi missing imputation untuk data penyebaran menggunakan Naïve Bayes,” Jurnal Teknologi Informasi dan Terapan, vol. 3, no. 1, pp. 300–306, 2016

Last update:

No citation recorded.

Last update: 2024-09-26 23:53:25

No citation recorded.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.

The author(s) guarantee that:

their article is original, written by the mentioned author(s),
has never been published before,
does not contain statements that violate the law, and
does not violate the rights of others, is subject to copyright held exclusively by the author(s), is free from the rights of third parties, and the necessary written permission to quote from other sources has been obtained by the author(s).

The author(s) retain all rights to the published work, such as (but not limited to) the following rights:

Copyright and other proprietary rights related to the article, such as patents,
The right to use the substance of the article in its own future works, including lectures and books,
The right to reproduce the article for its own purposes,
The right to archive all versions of the article in any repository, and
The right to enter into separate additional contractual arrangements for the non-exclusive distribution of published versions of the article (for example, posting them to institutional repositories or publishing them in a book), acknowledging its initial publication in this journal (Jurnal Teknologi dan Sistem Komputer).

Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.

Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.

Pengelompokan PMKS menggunakan Self Organizing Maps dengan perbaikan missing value Naïve Bayes Imputation

EDITORIAL OFFICE OF JURNAL TEKNOLOGI DAN SISTEM KOMPUTER