Socialization of Clustering Algorithm Usage to Support Decision-Making in Prioritizing Recipients of Social Assistance Funds in the Tengah Village, Pelayangan District
DOI:
https://doi.org/10.57152/batik.v2i3.1724Keywords:
clustering algorithm, social fund assistance, socializationAbstract
Poverty is one of the problems that must be eradicated. The government's policies for poverty alleviation is the provision of social assistance funds. But the government must consider the eligibility of the recipients of the social assistance. Since 2021-2023, the poverty rate in Jambi Province has been recorded at over 250 thousand people each year. Of the several districts/cities in Jambi Province, Jambi City contributes the largest number of poor people. Tengah Village is one of the villages in Pelayangan District, Jambi City. As an effort to support the provision of targeted social assistance, a clustering algorithm is used group objects based on certain characteristics. One of the analysis in the clustering is the K-Means algorithm. This community service activity is the socialization of the use of clustering algorithms to help group data on social assistance recipients. The activity was attended by staff and RT heads in Tengah Village. Based on the evaluation, as many as 72,7% of the participants who attended stated that they were very satisfied, in addition they stated that this activity was very useful in supporting the decision to select priority recipients of social fund assistance in the Tengah Village.
References
G. Regulation, “Peraturan Pemerintah Republik Indonesia Nomor 82 Tahun 2001,” Jakarta Peratur. [1] World Bank Institute. 2004. Dasar-dasar Analisi Kemiskinan. Edisi Terjemahan. Badan Pusat Statistik, Jakarta.
https://jambi.bps.go.id/. Diakses 1 Maret 2023.
BPS Provinsi Jambi. (2023). Provinsi Jambi Dalam Angka 2022 Jambi Province In Figures 2023. Jambi: BPS Provinsi Jambi.
http://www.bpkp.go.id. Diakses 20 Maret 2023.
Widayat. (2018). Statistika Multivariat. Malang: UMM Press.
Lasheng, C., & Yuqiang, L. (2017). Improved Initial Clustering Center Selection Algorithm for K-Means. Signal Processing: Algorithms, Architectures, Arrangements, and Application (SPA) (pp. 275-279). Poznan: IEEE.
Dean, J. (2014). Big Data, Data Mining, and Machine Learning. New Jersey: John Wiley and Sons.
Ruhiman, B., Ramdan, A., & Juliane, C. (2022). Algorithm K-Means Clustering Algorithm to Classify the Level of Legal Information Service Objectives in West Java Province: K-Means Clustering Algorithm to Classify the Level of Legal Information Service Objectives in West Java Province. Jurnal Komputer Terapan, 8(1), 178–185. https://doi.org/10.35143/jkt.v8i1.5209.
Muzakir, A. (2014). Analisa dan Pemanfaatan Algoritma K-Means Clustering pada Data Nilai Siswa Sebagi Penentuan Penerima Beasiswa. Prosiding Seminar Nasional Aplikasi Sains & Teknologi (SNAST) (pp. A-196). Yogyakarta: Binadarma.
Wu, Ruobing. (2024). Behavioral analysis of electricity consumption characteristics for customer groups using the k-means algorithm. Systems and Soft Computing, Vol. 6, 01-08. https://doi.org/10.1016/j.sasc.2024.200143.
Beiranvand, Behrang., Rajaee, Taher., Komasi, Mehdi. (2024). Comparison of K-means and FCM algorithms to optimize spatiotemporal pore pressure prediction of earth dams. Results in Engineering.. Vol.24, 1-11. https://doi.org/10.1016/j.rineng.2024.103377.
Abdullahi, Muhammad Rabiu., Lu, Qing-Chang., Hussain, Adil., Tripura, Sajib., Xu, Peng-Cheng., Wang, ShiXin. (2024). Location optimization of EV charging stations: A custom K-means cluster algorithm approach. Energy Reports. Vol.12, 5367-5382. https://doi.org/10.1016/j.egyr.2024.09.075.
Ediyanto, Mara, M. N., & Satyahadewi, N. (2013). Pengklasifikasian Karakteristik dengan Metode K-Means Cluster Analysis. Buletin Ilmiah Mat. Stat. dan Terapannya (Bimaster), Vol. 2, No. 2.
Suraya, S., Sholeh, M., & Lestari, U. (2023). Evaluation of Data Clustering Accuracy using K-Means Algorithm. International Journal of Multidisciplinary Approach Research and Science, 2(01), 385–396. https://doi.org/10.59653/ijmars.v2i01.504
Chen, Yafeng., Tan, Pingan., Li, Mu., Yin, Han., & Tang, Rui. (2024). K-means clustering method based on nearest-neighbor density matrix for customer electricity behavior analysis. International Journal of Electrical Power & Energy Systems.Vol.161, 1-19. https://doi.org/10.1016/j.ijepes.2024.110165.
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