Penerapan Metode Apriori Pada Data Penduduk Berdasarkan Tingkat Kesejahteraan (Studi Kasus : Kantor Camat Sirapit)
DOI:
https://doi.org/10.62951/repeater.v2i4.260Keywords:
Apriori Algorithm, Data Mining, Public WelfareAbstract
The Indonesian government has implemented various programs to improve public welfare; however, social assistance often misses its target, primarily due to a lack of accurate data. Sirapit Subdistrict, as a government institution, has access to important population data for policy development, particularly in the distribution of aid based on community welfare levels. Factors such as education, age, number of dependents, and income play a significant role in determining an individual's welfare. To address this issue, this study proposes the use of the Apriori method to analyze the factors affecting population welfare. The Apriori method is a data mining algorithm useful for discovering association patterns within a dataset. The study results show that with a support value of 3% and a confidence level of 100%, a pattern was found where residents with a high school education, 1-2 dependents, aged 35-45 years, earning Rp 500,000 - Rp 999,999, and with a low welfare level tend to work as laborers. These findings are expected to serve as a foundation for formulating more targeted policies to improve community welfare in Sirapit Subdistrict.
Downloads
References
Agita Dinda, H., & Ramadani, S. (2022). Korelasi penjualan produk pada toko kosmetik menggunakan metode Apriori. Agustus, 6(3).
Arief, N., Sudahri Damanik, I., Irawan, E., Tunas Bangsa, S., & Utara, S. (2021). Penerapan algoritma K-Medoids dalam mengelompokkan tingkat kasus kejahatan di setiap provinsi. KLIK: Kajian Ilmiah Informatika dan Komputer, 2(3), 111–116. https://djournals.com/klik
Aziz Muslim, M., Prasetiyo, B., Laily Harum Mawarni, E., Juli Herowati, A., Mirqotussa’adah, Hardiyanti Rukmana, S., & Nurzahputra, A. (2019). Data mining algoritma C4.5. In Nucl. Phys. (Vol. 13, Issue 1).
Budi Sutedjo, S. M., & Michael AN, S. (2018). Algoritma & teknik pemrograman. Yogyakarta: ANDI.
Kartono, K. (2007). Psikologi anak (Psikologi perkembangan). Bandung: CV. Manjur Jaya.
Kristiawan, A., Relita, B., & Maulita, Y. (2018). Korelasi faktor penyebab tindak kekerasan dalam rumah tangga menggunakan data mining algoritma Apriori. Jurnal Media Infotama, 14(1), 21–30.
Pahlevi, M. R., & Seprina, I. (2022). Analisa pola kejahatan pencurian motor (studi kasus Polrestabes Palembang) dengan metode association rule menggunakan algoritma FP-Growth. Bina Darma Conference on Computer Science, 1(3), 328–336.
Relita Buaton, Zarlis, M., Efendi, S., & Yasin, V. (2019). Data mining time series (1st ed., Vol. 1). Wade Group.
Risdianti, Khoirunnisa Nasution, A., Oktaviandi, R., & Bu’ulolo, E. (2021). Penerapan algoritma Apriori untuk mengetahui pola jenis kejahatan yang sering terjadi (studi kasus: Polsek Percut Sei Tuan). Seminar Nasional Sains dan Teknologi Informasi (SENSASI), 1(1), 117–120. http://prosiding.seminar-id.com/index.php/sensasi/issue/archivePage|117
Rosita, I. B. B. (2019). Prediksi putusan hukuman tindakan kriminalitas dengan menggunakan algoritma nearest neighbor (studi kasus: Pengadilan Negeri Lubuk Pakam). Majalah Ilmiah INTI, 6(2), 241–245.
Sanjaya, W. (2017). Algoritma & teknik pemrograman. Jakarta: Kencana.
Sidik, B., & Ir. (2019). Pemrograman web dengan PHP. INFORMATIKA, Bandung.
Winarti, D., Revita, E., & Yandani, E. (2021). Penerapan data mining untuk analisa tingkat kriminalitas dengan algoritma association rule metode FP-Growth. Jurnal SIMTIKA, 4(3).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Repeater : Publikasi Teknik Informatika dan Jaringan
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
