APPLICATION OF THE APRIORI ALGORITHM TO DETERMINE THE COMBINATION OF POVERTY INDICATORS

  • Sri Siswanti STMIK Sinar Nusantara https://orcid.org/0000-0001-8146-7611
  • Retno Tri Vulandari STMIK Sinar Nusantara
  • Andriani Kusumaningrum STMIK Sinar Nusantara
  • Setiyowati Setiyowati STMIK Sinar Nusantara
Keywords: Apriorics Rule, Association Rule, Proverty

Abstract

Poverty is a society that has not been solved until now. The decline in poverty in Laweyan District from 2000 to 2013 was 5.71%, among the five lowest in the reduction in the percentage of poverty in Central Java Province. The problem of poverty is very complex, and the differences in regional characteristics, as well as the techniques used, also influence the indicators of the causes of poverty and the formulation of policies for poverty alleviation. This study uses Principal Component Analysis as part of data preprocessing, followed by applying association rules with the Apriori Algorithm to explore the relationship pattern of poverty indicators. Based on the research that has been conducted on the poverty dataset, which consists of 46 attributes, it is found that the attributes that have passed the preprocessing data are six attributes, namely the Poor Population, ADHB in the Communication Sector, ADHB in the Mining and Excavation Sector, ADHB in the Agriculture and Food Crops Sector, ADHB in the Plantation Sector. and unemployment. These six attributes are transformed into Ascending, Fixed, and Descending categorical data. The fuzzification process for the increase and decrease categories uses the shoulder-type triangle membership function. Applying the Apriori Algorithm to the poverty dataset with a minimum support of 0.4 and a minimum confidence of 0.8 produces 38 rules that show the relationship between indicators and poverty and 134 rules that show the relationship pattern between indicators.

Downloads

Download data is not yet available.

Author Biography

Sri Siswanti, STMIK Sinar Nusantara

Lecturer at STMIK Sinar Nusantara Surakarta

References

Andhykha, R., Handayani, H. R., & Woyanti, N. (2018). Analisis Pengaruh PDRB, Tingkat Pengangguran, dan IPM Terhadap Tingkat Kemiskinan di Provinsi Jawa Tengah. Media Ekonomi Dan Manajemen, 33(2), 113–123. https://doi.org/10.24856/mem.v33i2.671

Arafah, A. A., & Mukhlash, I. (2015). The Application of Fuzzy Association Rule on Co-movement Analyze of Indonesian Stock Price. Procedia Computer Science, 59(Iccsci), 235–243. https://doi.org/10.1016/j.procs.2015.07.541

Badan Pusat Statistik Jawa Tengah. (2022). Provinsi Jawa Tengah Dalam Angka 2022 Jawa Tengah Province in Figures 2022. Badan Pusat Statistik Provinsi Jawa Tengah.

Edastama, P., Bist, A. S., & Prambudi, A. (2021). Implementation Of Data Mining On Glasses Sales Using The Apriori Algorithm. International Journal of Cyber and IT Service Management, 1(2), 159–172. https://doi.org/10.34306/ijcitsm.v1i2.46

Farida, N., Chulkamdi, M. T., & Wulansari, Z. (2022). Application of Data Mining By Using a Priori Algorithm To Improve Customer Purchasing Decisions At Mikamart Blitar Store. International Journal of Multidisciplinary Research and Literature, 1(5), 526–534. https://doi.org/10.53067/ijomral.v1i5.58

Fitrina, N., Kustanto, K., & Vulandari, R. T. (2018). Penerapan Algoritma Apriori Pada Sistem Rekomendasi Barang Di Minimarket Batox. Jurnal Teknologi Informasi Dan Komunikasi (TIKomSiN), 6(2), 21–27. https://doi.org/10.30646/tikomsin.v6i2.376

Hakim, L., & Fauzy, A. (2015). Menggunakan Metode Association Rules. University Research Colloquium, 73–81.

Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques (3rd ed.). Amsterdam: Elsevier. https://doi.org/10.1016/C2009-0-61819-5

Hendro, G., Adji, T. B., & Setiawan, N. A. (2012). Penggunaan Metodologi Analisa Komponen Utama ( PCA ) untuk Mereduksi Faktor-Faktor yang Mempengaruhi Penyakit Jantung Koroner. Seminar Nasional ScrETec, 1–5.

Mei Alfianto, D. B., Istiyani, N., & Priyono, T. H. (2019). Faktor-Faktor Yang Mempengaruhi Tingkat Kemiskinan di Provinsi Jawa Timur. E-Journal Ekonomi Bisnis Dan Akuntansi, 6(1), 85. https://doi.org/10.19184/ejeba.v6i1.11108

Nurwati, N. (2008). Kemiskinan : Model Pengukuran , Permasalahan dan Alternatif Kebijakan. Jurnal Kependudukan Padjadjaran, 10(1), 1–11.

Permana, R. (2016). Faktor-Faktor yang Mempengaruhi Tingkat Kemiskinan di Provinsi Kalimantan Timur. Journal FEB UNMUL: Forum Ekonomi: Jurnal Ekonomi, Manajemen Dan Akuntansi, 18(2), 111–129.

Silva, M. B. (2016). Percepção da população assistida sobre a inserção de estudantes de medicina na Unidade Básica de Saúde. Trabalho de Conclusão de Curso, 1(9), 1–10. https://doi.org/10.1017/CBO9781107415324.004

Sukaesih Sitanggang, I. (2013). Spatial Multidimensional Association Rules Mining in Forest Fire Data. Journal of Data Analysis and Information Processing, 01(04), 90–96. https://doi.org/10.4236/jdaip.2013.14010

Wanto, A. (2018). Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Jumlah Kemiskinan. Klik - Kumpulan Jurnal Ilmu Komputer, 5(1), 61–74.

Published
2023-03-10
How to Cite
Siswanti, S., Vulandari, R., Kusumaningrum, A., & Setiyowati, S. (2023). APPLICATION OF THE APRIORI ALGORITHM TO DETERMINE THE COMBINATION OF POVERTY INDICATORS. Jurnal Pilar Nusa Mandiri, 19(1), 45-52. https://doi.org/10.33480/pilar.v19i1.4161