Tinjauan Kesejahteraan di Daerah Perbatasan Republik Indonesia Tahun 2021: Penerapan Analisis Klaster K-Means dan Hierarki

Afifah Az-Zahra, Arie Wahyu Wijayanto

Abstract


Kesejahteraan menjadi salah satu tujuan utama pemerintahan yang perlu ditinjau secara multidimensi. Di Indonesia, program-program pembangunan banyak menyasar Daerah Terdepan, Terluar, dan Tertinggal (3T). Daerah terdepan dan terluar merupakan daerah yang berada di garis perbatasan negara dengan banyak ancaman terhadap kesejahteraan. Oleh karena itu, diperlukan analisis klaster sebagai gambaran kesejahteraan di daerah perbatasan, yang diharapkan dapat membantu proses monitoring dan evaluasi program pembangunan. Indikator-indikator kesejahteraan yang digunakan bersumber dari publikasi Statistik Kesejahteraan Rakyat 2021, tabel Badan Pusat Statistik, Buku Saku Hasil Suvei Status Gizi Indonesia 2021, dan tabel FSVA Nasional 2021 di 204 kabupaten/kota di 13 provinsi perbatasan. Penelitian ini membandingkan dua metode analisis klaster, yaitu partitioning dengan K-Means dan hierarki dengan Ward’s Method berdasarkan kriteria validitas internal dan stabilitas klaster. Hasilnya diperoleh bahwa ukuran sampel 2 memberikan klaster paling yang optimal dan metode K-Means menghasilkan kinerja yang lebih baik. Secara umum, kabupaten/kota yang tergabung ke dalam klaster kedua memiliki indikator kesejahteraan yang lebih tinggi dibandingkan klaster pertama. 


Keywords


Analisis Klaster; Kesejahteraan; Daerah Perbatasan; K-Means; Hierarchical Clustering

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References


A. Rinaldi, “Model Persamaan Struktural untuk Menganalisis Indikator Kesejahteraan Rumah Tangga,” Desimal: Jurnal Matematika, vol. 2, no. 3, pp. 281–288, Sep. 2019, doi: 10.24042/djm.v2i3.4692.

Kemendikbud, “Analisis Sebaran Guru Dikdasmen di Wilayah 3 T,” Jakarta, 2016.

Peraturan Presiden Republik Indonesia, Peraturan Presiden Republik Indonesia Nomor 63 Tahun 2020 tentang Penetapan Daerah Tertinggal Tahun 2020-2024. Indonesia , 2020.

S. Purwanda, R. Monika, N. Betaubun, and R. H. Rado, “Klasifikasi Penetapan Daerah Terdepan, Terluar dan Tertinggal (Daerah 3T) Dalam Regulasi Indonesia,” Jurnal Komunikasi Hukum (JKH), 2023, [Online]. Available: https://ejournal.undiksha.ac.id/index.php/jkh

A. I. Kostyaev, “Rural areas of Russia’s north-west borderland: Problems and development paths,” Baltic Region, vol. 11, no. 4, pp. 93–113, 2019, doi: 10.5922/2079-8555-2019-4-6.

V. Kolosov and A. Sebentsov, “Russian borderlands: Contemporary problems and challenges,” Regional Science Policy & Practice, vol. 12, no. 4, pp. 671–687, Aug. 2020, doi: 10.1111/rsp3.12285.

L. S, “Partisipasi Masyarakat di Daerah Perbatasan NKRI untuk Mencegah Anak Sebagai Objek Human Trafficking,” Jurnal Wawasan Yuridika, vol. 2, no. 1, p. 24, Mar. 2018, doi: 10.25072/jwy.v2i1.162.

Badan Pusat Statistik, “Statistik Kesejahteraan Rakyat 2022,” 2022. Accessed: Jun. 18, 2023. [Online]. Available: https://www.bps.go.id/publication/2022/11/17/76d9e38c1a9fe738a2dcde75/statistik-kesejahteraan-rakyat-2022.html

J. C. J. M. van den Bergh and W. J. W. Botzen, “Global impact of a climate treaty if the Human Development Index replaces GDP as a welfare proxy,” Climate Policy, vol. 18, no. 1, pp. 76–85, Jan. 2018, doi: 10.1080/14693062.2016.1227954.

N. Dwitiyanti, N. Selvia, and F. R. Andrari, “Penerapan Fuzzy C-Means Cluster dalam Pengelompokkan Provinsi Indonesia Menurut Indikator Kesejahteraan Rakyat,” Faktor Exacta, vol. 12, no. 3, p. 201, Nov. 2019, doi: 10.30998/faktorexacta.v12i3.4526.

N. Thamrin and A. W. Wijayanto, “Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island: Analisis cluster dengan menggunakan hard clustering dan soft clustering untuk pengelompokkan tingkat kesejahteraan kabupaten/kota di pulau Jawa,” Indonesian Journal of Statistics and Its Applications, vol. 5, no. 1, pp. 141–160, 2021.

K. P. Sinaga and M.-S. Yang, “Unsupervised K-Means Clustering Algorithm,” IEEE Access, vol. 8, pp. 80716–80727, 2020, doi: 10.1109/ACCESS.2020.2988796.

N. Afira and A. W. Wijayanto, “Analisis Cluster dengan Metode Partitioning dan Hierarki pada Data Informasi Kemiskinan Provinsi di Indonesia Tahun 2019,” Komputika : Jurnal Sistem Komputer, vol. 10, no. 2, pp. 101–109, Sep. 2021, doi: 10.34010/komputika.v10i2.4317.

A. Saxena et al., “A review of clustering techniques and developments,” Neurocomputing, vol. 267, pp. 664–681, Dec. 2017, doi: 10.1016/j.neucom.2017.06.053.

A. Firnanda and A. W. Wijayanto, “Grouping of Regencies/Municipalities in Eastern Indonesia in 2021 Based on Socio-Economic Indicators,” Sistemasi: Jurnal Sistem Informasi, vol. 12, no. 2, pp. 390–403, 2023.

S. Wajrock, N. Antille, A. Rytz, N. Pineau, and C. Hager, “Partitioning methods outperform hierarchical methods for clustering consumers in preference mapping,” Food Qual Prefer, vol. 19, no. 7, pp. 662–669, Oct. 2008, doi: 10.1016/j.foodqual.2008.06.002.

Z. Zhu and N. Liu, “Early Warning of Financial Risk Based on K-Means Clustering Algorithm,” Complexity, vol. 2021, pp. 1–12, Mar. 2021, doi: 10.1155/2021/5571683.

A. Ashabi, S. Bin Sahibuddin, and M. Salkhordeh Haghighi, “The Systematic Review of K-Means Clustering Algorithm,” in 2020 The 9th International Conference on Networks, Communication and Computing, New York, NY, USA: ACM, Dec. 2020, pp. 13–18. doi: 10.1145/3447654.3447657.

A. T. R. Dani, S. Wahyuningsih, and N. A. Rizki, “Penerapan Hierarchical Clustering Metode Agglomerative pada Data Runtun Waktu,” Jambura Journal of Mathematics, vol. 1, no. 2, pp. 64–78, 2019.

R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, 5th ed. New Jersey: Pearson Prentice Inc, 2002.

P. Mishra, A. Biancolillo, J. M. Roger, F. Marini, and D. N. Rutledge, “New data preprocessing trends based on ensemble of multiple preprocessing techniques,” TrAC Trends in Analytical Chemistry, vol. 132, pp. 1–12, Nov. 2020, doi: 10.1016/j.trac.2020.116045.

S. Gupta and A. Gupta, “Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review,” Procedia Comput Sci, vol. 161, pp. 466–474, 2019, doi: 10.1016/j.procs.2019.11.146.

T. Emmanuel, T. Maupong, D. Mpoeleng, T. Semong, B. Mphago, and O. Tabona, “A survey on missing data in machine learning,” J Big Data, vol. 8, no. 1, p. 140, Oct. 2021, doi: 10.1186/s40537-021-00516-9.

A. Jadhav, D. Pramod, and K. Ramanathan, “Comparison of Performance of Data Imputation Methods for Numeric Dataset,” Applied Artificial Intelligence, vol. 33, no. 10, pp. 913–933, Aug. 2019, doi: 10.1080/08839514.2019.1637138.

I. Bin Mohamad and D. Usman, “Standardization and its effects on K-means clustering algorithm,” Research Journal of Applied Sciences, Engineering and Technology, vol. 6, no. 17, pp. 3299–3303, 2013.

A. K. Sangaiah, A. E. Fakhry, M. Abdel-Basset, and I. El-henawy, “Arabic text clustering using improved clustering algorithms with dimensionality reduction,” Cluster Comput, vol. 22, no. 2, pp. 4535–4549, 2019, doi: 10.1007/s10586-018-2084-4.

S. V. Budaev, “Using Principal Components and Factor Analysis in Animal Behaviour Research: Caveats and Guidelines,” Ethology, vol. 116, no. 5, pp. 472–480, May 2010, doi: 10.1111/j.1439-0310.2010.01758.x.

N. Salem and S. Hussein, “Data dimensional reduction and principal components analysis,” Procedia Comput Sci, vol. 163, pp. 292–299, 2019, doi: 10.1016/j.procs.2019.12.111.

E. M. Al-Balhan, H. Khabbache, A. Watfa, T. S. Re, R. Zerbetto, and N. L. Bragazzi, “Psychometric evaluation of the Arabic version of the nomophobia questionnaire: confirmatory and exploratory factor analysis – implications from a pilot study in Kuwait among university students,” Psychol Res Behav Manag, vol. Volume 11, pp. 471–482, Oct. 2018, doi: 10.2147/PRBM.S169918.

D. Layton and T. Walton, “Patient-evaluated dentistry: development and validation of a patient satisfaction questionnaire for fixed prosthodontic treatment,” International Journal of Prosthodontics, vol. 24, no. 4, p. 332, 2011.

S. Mishra, A. Kumar, S. Yadav, and M. K. Singhal, “Assessment of heavy metal contamination in water of Kali River using principle component and cluster analysis, India,” Sustain Water Resour Manag, vol. 4, no. 3, pp. 573–581, Sep. 2018, doi: 10.1007/s40899-017-0141-4.

Y. Liu, Z. Li, H. Xiong, X. Gao, and J. Wu, “Understanding of internal clustering validation measures,” in Proceedings - IEEE International Conference on Data Mining, ICDM, 2010, pp. 911–916. doi: 10.1109/ICDM.2010.35.

G. Brock, V. Pihur, S. Datta, and S. Datta, “clValid: An R Package for Cluster Validation,” 2008. [Online]. Available: http://www.jstatsoft.org/

X. Wang and Y. Xu, “An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index,” IOP Conf Ser Mater Sci Eng, vol. 569, no. 5, p. 052024, Jul. 2019, doi: 10.1088/1757-899X/569/5/052024.

M. Brun et al., “Model-based evaluation of clustering validation measures,” Pattern Recognit, vol. 40, no. 3, pp. 807–824, Mar. 2007, doi: 10.1016/j.patcog.2006.06.026.

Badan Pusat Statistik, “Statistik Kriminal 2021,” 2021.

Badan Pusat Statistik, “Statistik Kriminal Papua Barat 2020,” 2020.

A. Mutiarasari, “Peran Entrepreneur Meningkatkan Pertumbuhan Ekonomi dan Mengurangi Tingkat Pengangguran Peran Entrepreneur Meningkatkan Pertumbuhan Ekonomi dan Mengurangi Tingkat Pengangguran,” Dinar : Jurnal Prodi Ekonomi Syari’ah , vol. 1, no. 2, Apr. 2018.




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