Comparative Analysis of Machine Learning Algorithms on Family Wellness Classification

Retno Budiarti, Febri Hemarani, Mohammad Reza, Rindi Melati Mulyasari

Abstract


Family welfare is a state in which a family can experience happiness, have a decent quality of life, and be sufficient in meeting primary and secondary needs in family life. One factor that influences family welfare is the amount of per capita expenditure. This study aims to compare the performance of three machine learning algorithms, namely KNN (K-Nearest Neighbors), random forest, and naive Bayes, in classifying the status of families per province in Indonesia as prosperous or not prosperous. The data used in this study is demographic and social statistics data from the years 2017-2021, obtained from the bps.go.id website. The first statistical analysis conducted is principal component analysis (PCA) with 9 predictor variables. PCA produces four principal components which are then used in the KNN, random forest, and naive Bayes methods. The analysis results from the KNN, random forest, and naive Bayes methods each yield an F1-score of 65.46%, 68%, and 69.44%, respectively.


Keywords


consumption expenditure; family welfare; KNN; naive bayes; random forest

Full Text:

PDF

References


[1] E. Fani, “MAKNA KESEJAHTERAAN BAGI MASYARAKAT PEMULUNG (Studi Pada TPA Bakung Kecamatan Teluk Betung Barat Kota Bandar Lampung),” UNIVERSITAS ISLAM NEGERI RADEN INTAN, Lampung, 2018.

[2] Y. Zebua, P. K. Wildani, A. Lasefa, and R. Rahmad, “FAKTOR PENYEBAB RENDAHNYA TINGKAT KESEJAHTERAAN NELAYAN PESISIR PANTAI SRI MERSING DESA KUALA LAMA KABUPATEN SERDANG BEDAGAI SUMATERA UTARA,” JURNAL GEOGRAFI, vol. 9, no. 1, pp. 88–98, Feb. 2017, doi: 10.24114/jg.v9i1.6923.

[3] R. Astika and L. Harudu, “FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT KESEJAHTERAAN KELUARGA,” JURNAL PENELITIAN PENDIDIKAN GEOGRAFI, vol. 8, no. 4, pp. 2502–2776, 2023, doi: 10.36709/jppg.v8i4.94.

[4] D. P. Sari, W. Astuti, and N. Dzulfikry, “Indikator dan Tingkat Keluarga Sejahtera menurut Dinas P3AP2KB Kabupaten Sambas,” Ekodestinasi, vol. 1, no. 1, pp. 47–54, Mar. 2023, doi: 10.59996/ekodestinasi.v1i1.38.

[5] Y. A. Setianto, K. Kusrini, and H. Henderi, “Penerapan Algoritma K-Nearest Neighbour Dalam Menentukan Pembinaan Koperasi Kabupaten Kotawaringin Timur,” Creative Information Technology Journal, vol. 5, no. 3, pp. 232–241, Sep. 2018, doi: 10.24076/citec.2018v5i3.179.

[6] K. Taunk, S. De, S. Verma, and A. Swetapadma, “A Brief Review of Nearest Neighbor Algorithm for Learning and Classification,” in 2019 International Conference on Intelligent Computing and Control Systems (ICCS), IEEE, May 2019, pp. 1255–1260. doi: 10.1109/ICCS45141.2019.9065747.

[7] N. Bhatia and V. Vandana, “Survey of Nearest Neighbor Techniques,” 2010. doi: 10.48550/arXiv.1007.0085.

[8] R. Supriyadi, W. Gata, N. Maulidah, and A. Fauzi, “Penerapan Algoritma Random Forest Untuk Menentukan Kualitas Anggur Merah,” E-Bisnis : Jurnal Ilmiah Ekonomi dan Bisnis, vol. 13, no. 2, pp. 67–75, Nov. 2020, doi: 10.51903/e-bisnis.v13i2.247.

[9] A. Syarifah and A. Muslim, “PEMANFAATAN NAÏVE BAYES UNTUK MERESPON EMOSI DARI KALIMAT BERBAHASA INDONESIA,” UJM, vol. 4, no. 2, pp. 147–156, 2015, [Online]. Available: http://journal.unnes.ac.id/sju/index.php/ujm

[10] M. Mariana, “ANALISIS KOMPONEN UTAMA,” JURNAL MATEMATIKA DAN PEMBELAJARANNYA, vol. 2, no. 2, pp. 99–114, 2013, doi: 10.33477/mp.v1i2.304.

[11] BPS, “Profil Kemiskinan di Indonesia Maret 2023,” Jakarta, Jul. 2023.

[12] A. J. T, D. Yanosma, and K. Anggriani, “IMPLEMENTASI METODE K-NEAREST NEIGHBOR (KNN) DAN SIMPLE ADDITIVE WEIGHTING (SAW) DALAM PENGAMBILAN KEPUTUSAN SELEKSI PENERIMAAN ANGGOTA PASKIBRAKA,” Pseudocode, vol. 3, no. 2, pp. 98–112, Jan. 2016, doi: 10.33369/pseudocode.3.2.98-112.

[13] J. Jonathan, “Implementasi Algoritma Random Forest untuk Klasifikasi Kategori Berita,” Universitas Multimedia Nusantara, Tangerang, 2021.

[14] M. Gusnina, “Penerapan Metode Random Forest pada Klasifikasi Student Academics Performance di Universitas Sebelas Maret ,” Universitas Sebelas Maret, Surakarta, 2022.

[15] L. Fadilah, “KLASIFIKASI RANDOM FOREST PADA DATA IMBALANCED,” Universitas Islam Negeri Syarif Hidayatullah, Jakarta, 2018.

[16] A. Kiding, “ANALISIS SENTIMEN PUBLIK TERHADAP TES CPNS MELALUI MEDIA TWITER MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER,” Universitas Atma Jaya, Yogyakarta, 2021.

[17] N. K. K. Ardana et al., “Perbandingan Metode KNN, Naive Bayes, dan Regresi Logistik Binomial dalam Pengklasifikasian Status Ekonomi Negara,” Jambura Journal of Mathematics, vol. 5, no. 2, pp. 404–428, Aug. 2023, doi: 10.34312/jjom.




DOI: https://doi.org/10.18860/ca.v9i2.28259

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Febri Hemarani, Retno Budiarti, Mohammad Reza, Rindi Melati Mulyasari

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

Editorial Office
Mathematics Department,
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Gajayana Street 50 Malang, East Java, Indonesia 65144
Faximile (+62) 341 558933
e-mail: cauchy@uin-malang.ac.id

Creative Commons License
CAUCHY: Jurnal Matematika Murni dan Aplikasi is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.