CLUSTERING THE HAPPINESS LEVEL OF PROVINCES IN INDONESIA USING K-MEANS

Heti Mulyani; Ricak Agus Setiawan

doi:10.56873/jitu.7.2.5854

Authors

Heti Mulyani Politeknik Enjinering Indorama
Ricak Agus Setiawan Politeknik Enjinering Indorama, Purwakarta, Indonesia

DOI:

https://doi.org/10.56873/jitu.7.2.5854

Keywords:

Happiness Level, Cluster, Province, KMeans

Abstract

Community welfare is a government goal related to the fulfillment of basic needs, education and employment, which can be measured through the happiness index. The purpose of this research is to cluster provinces in Indonesia based on their resident’s happiness level. The data obtained from the Indonesian Central Bureau of Statistics website. The method used in this research is K-means clustering. There are 2 dimensions used, namely the personal dimension which includes education, employment, household income, health, housing conditions and, assets. The social dimension includes social relations, environmental conditions, security conditions, family harmony, and availability of free time. Based on the results of the study, 2 provincial groups were obtained based on the level of happiness. Testing is done using the Davies Bouldin Index (DBI). The optimal K is obtained, namely K = 2 with a DBI value of = 0.776. The first group is the happiest group including the provinces of North Maluku, Maluku, North Sulawesi, North Kalimantan, Gorontalo, Central Sulawesi, West Papua, Riau Islands, East Kalimantan. The other provinces are in the second group. The unhappiest groups are Banten, Bengkulu and Papua.

References

[1] K. Hamidah and A. Voutama, “Analisis Faktor Tingkat Kebahagiaan Negara Menggunakan Data World Happiness Report dengan Metode Regresi Linier,” Explor. IT J. Keilmuan dan Apl. Tek. Inform., vol. 15, no. 1, pp. 1–7, 2023, doi: 10.35891/explorit.v15i1.3874.

[2] F. O. Dayera and M. B. Palungan, “G-Tech : Jurnal Teknologi Terapan,” G-Tech J. Teknol. Terap., vol. 8, no. 1, pp. 186–195, 2024, [Online]. Available: https://ejournal.uniramalang.ac.id/index.php/g-tech/article/view/1823/1229

[3] A. F. D. Rositawati and I. N. Budiantara, “Pemodelan Indeks Kebahagiaan Provinsi di Indonesia Menggunakan Regresi Nonparametrik Spline Truncated,” J. Sains dan Seni ITS, vol. 8, no. 2, 2020, doi: 10.12962/j23373520.v8i2.45160.

[4] C. F. Palembang, M. Y. Matdoan, and S. P. Palembang, “Perbandingan Algoritma K-Means dan K-Medoids dalam Pengelompokkan Tingkat Kebahagiaan Provinsi di Indonesia,” J. Multidisiplin Ilmu, vol. 01, no. 5, pp. 830–839, 2022, [Online]. Available: https://journal.mediapublikasi.id/index.php/bullet/article/download/1135/550

[5] N. Wayan, R. Damayanthi, N. Luh, P. Suciptawati, K. Jayanegara, and E. N. Kencana, “Pengelompokan Provinsi di Indonesia Menurut Indikator Indeks Kebahagiaan Menggunakan Metode Average Linkage,” Innov. J. Soc. Sci. Res., vol. 3, pp. 8859–8872, 2023.

[6] S. N. Mayasari and J. Nugraha, “Implementasi K-Means Cluster Analysis untuk Mengelompokkan Kabupaten/Kota Berdasarkan Data Kemiskinan di Provinsi Jawa Tengah Tahun 2022,” KONSTELASI Konvergensi Teknol. dan Sist. Inf., vol. 3, no. 2, pp. 317–329, 2023, doi: 10.24002/konstelasi.v3i2.7200.

[7] F. U. Fitra Ramdhani, “Pengelompokan Provinsi di Indonesia Berdasarkan Karakteristik Kesejahteraan Rakyat Menggunakan Metode K-Means Cluster,” GAUSSIAN, vol. 4, no. 2015, pp. 875–884, 2015.

[8] U. N. U. Y. Yudiana, “Prediksi Customer Churn Menggunakan Metode CRISP-DM pada Industri Telekomunikasi sebagai Implementasi Mempertahankan Pelanggan,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 10, pp. 3571–3579, 2020.

[9] L. Y. Hutabarat, I. Gunawan, I. Purnamasari, M. Safii, and W. Saputra, “Penerapan Algoritma K-Means Dalam Pengelompokan Jumlah Penduduk Berdasarkan Kelurahan Di Kota Pematangsiantar”, IKOMTI (Jurnal Ilmu Komputer dan Teknologi), vol. 2, no. 2, pp. 20–26, 2021.

[10] F. Sandova, R. Kurniawan, and T. Supratati, “Penerapan Data Mining Menggunakan Metode K-Means Clustering pada Penjualan Tas di Asia Toserba Cirebon,” JATI (Jurnal Mhs. Tek. Inform.), vol. 8, no. 1, pp. 245–251, 2024, doi: 10.36040/jati.v8i1.8330.

[11] Y. Sopyan, A. D. Lesmana, and C. Juliane, “Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1464–1470, 2022, doi: 10.47065/bits.v4i3.2697.

[12] N. A. Maori and E. Evanita, “Metode Elbow dalam Optimasi Jumlah Cluster pada K-Means Clustering,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 14, no. 2, pp. 277–288, 2023, doi: 10.24176/simet.v14i2.9630.

[13] V. A. Ekasetya and A. Jananto, “Klusterisasi Optimal Dengan Elbow Method untuk Pengelompokan Data Kecelakaan Lalu Lintas di Kota Semarang,” J. Din. Inform., vol. 12, no. 1, pp. 20–28, 2020, doi: 10.35315/informatika.v12i1.8159.

[14] M. R. Nahjan, N. Heryana, and A. Voutama, “Implementasi Rapidminer dengan Metode Clustering K-Means untuk Analisa Penjualan pada Toko Oj Cell,” JATI (Jurnal Mhs. Tek. Inform.), vol. 7, no. 1, pp. 101–104, 2023, doi: 10.36040/jati.v7i1.6094.

[15] I. Azhami and R. Fauziah, “Penerapan Rapidminer pada Data Mining Klastering (Kasus: Distribusi Persentase Rumah Tangga Menurut Kabupaten/Kota dan Bahan Bakar untuk Memasak),” KESATRIA J. Penerapan Sist. Inf. (Komputer Manajemen), vol. 1, no. 2, pp. 52–58, 2020, doi: 10.30645/kesatria.v1i2.20.