PENENTUAN TINGKAT KINERJA PENDAMPING SOSIAL PROGRAM KELUARGA HARAPAN MENGGUNAKAN METODE K-MEAN CLUSTERING
Abstract
This research aims to optimize the evaluation process of social assistants in the Family Hope Program (PKH) managed by the Social Service of Padang Lawas Regency. Currently, the performance evaluation of PKH social assistants is conducted conventionally, lacking structured data analysis. This study employs the K-Means Clustering method to analyze 2016 performance data of 28 PKH social assistants, identifying patterns and grouping them based on performance. The research framework includes problem identification, solution analysis, literature review, data analysis, K-Means implementation, and clustering validation. Initial random centroid assignment followed by Euclidean Distance calculations iteratively refines the clustering. Results reveal three performance clusters: high-performing (8 assistants), low-performing (2 assistants), and average-performing (18 assistants). These clusters assist in making objective contract renewal recommendations. The study demonstrates K-Means Clustering's efficacy in social performance evaluation, offering insights for strategic decision-making in social services.
References
[1] S. P. Tamba, M. D. Batubara, W. Purba, M. Sihombing, V. M. Mulia Siregar, and J. Banjarnahor, “Book data grouping in libraries using the k-means clustering method,” J. Phys. Conf. Ser., vol. 1230, no. 1, p. 012074, Jul. 2019, doi: 10.1088/1742-6596/1230/1/012074.
[2] P. D. P. Adi et al., “A Study of Programmable System on Chip (PSoC) Technology for Engineering Education,” J. Phys. Conf. Ser., vol. 1899, no. 1, p. 012163, May 2021, doi: 10.1088/1742-6596/1899/1/012163.
[3] V. M. Mulia Siregar and H. Sugara, “Implementation of artificial neural network to assesment the lecturer‘s performance,” IOP Conf. Ser. Mater. Sci. Eng., vol. 420, p. 012112, Oct. 2018, doi: 10.1088/1757-899X/420/1/012112.
[4] V. M. M. Siregar and E. D. Siringo-Ringo, “Decision Support System to Determine Scholarship Recipients using Analytical Hierarchy Process Method,” COSTA J. (Computer Sci. Technol. Appl. Journal), vol. 1, no. 1, pp. 39–49, 2023, doi: 10.35335/idss.v4i2.67.
[5] V. M. M. Siregar and E. D. Siringo-Ringo, “Decision Support System to Determine Scholarship Recipients Using the Electre Method,” COSTA J. (Computer Sci. Technol. Appl. Journal), vol. 1, no. 2, pp. 39–49, 2023.
[6] V. M. M. Siregar, K. Sinaga, E. Sirait, A. S. Manalu, and M. Yunus, “Classification of Customer Satisfaction Through Machine Learning : An Artificial Neural Network Approach,” IOTA, vol. 3, no. 3, pp. 273–282, 2023, doi: 10.31763/iota.v3i3.643.
[7] V. M. M. Siregar and M. A. Hanafiah, “Perancangan Aplikasi Augmented Reality Untuk Edukasi Penerapan Konsep Green Economy Dalam Pengembangan Desa Wisata Sebagai Upaya Mewujudkan Pembangunan Berwawasan Lingkungan,” J. TEKINKOM, vol. 6, no. 2, pp. 339–348, 2023, doi: 10.37600/tekinkom.v6i2.950.
[8] Kisno, V. M. M. Siregar, H. Sugara, A. T. Purba, and S. Purba, “Jurnal abdi insani,” J. Abdi Insa., vol. 9, no. 2, pp. 570–579, 2022.
[9] N. A. Sinaga et al., “Decision support system with MOORA method in selection of the best teachers,” AIP Conf. Proc., vol. 2453, no. July, 2022, doi: 10.1063/5.0094437.
[10] V. M. M. Siregar et al., “Decision support system for selection of food aid recipients using SAW method,” AIP Conf. Proc., vol. 2453, no. July, 2022, doi: 10.1063/5.0094385.
[11] E. Damanik and I. M. Siregar, “PENGEMBANGAN SISTEM CUSTOMER RELATIONSHIP MANAGEMENT BERBASIS WEB PADA PT. TERUS MEGA TARA JAKARTA,” J. Tek. Inf. dan Komput., vol. 4, no. 1, pp. 60–69, 2021, doi: 10.37600/tekinkom.v4i1.278.
[12] H. Sugara, V. M. M. Siregar, K. Sinaga, M. A. Hanafiah, and H. D. Pardede, “SAW and Electre Methods Implementation for Scholarship Awardee Decision,” IOTA, vol. 01, no. 4, pp. 209–220, 2021, doi: 10.31763/iota.v1i4.496.
[13] P. D. P. Adi et al., “A Performance Evaluation of ZigBee Mesh Communication on the Internet of Things (IoT),” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 7–13, 2021, doi: 10.1109/EIConCIT50028.2021.9431875.
[14] H. Priyatman, F. Sajid, and D. Haldivany, “Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa,” J. Edukasi dan Penelit. Inform., vol. 5, no. 1, p. 62, Apr. 2019, doi: 10.26418/jp.v5i1.29611.
[15] S. Hadija, E. Irawan, and I. S. Damanik, “Penerapan Data Mining Pada Pola Penjualan Barang di Minimarket Menggunakan Algoritma Apriori Application of Data Mining on Patterns of Sales of Goods in Minimarkets Using the Apriori Algorithm,” JOMLAI (Journal Mach. Learn. Artif. Intell., vol. 1, no. 4, 2022, doi: 10.55123/jomlai.v1i4.1668.
[16] J. Dongga, A. ` Sarungallo, N. Koru, and G. Lante, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang (Studi Kasus: Toko Swapen Jaya Manokwari),” G-Tech J. Teknol. Terap., vol. 7, no. 1, pp. 119–126, 2023, doi: 10.33379/gtech.v7i1.1938.
[17] S. Wahyuni Nengsih, I. Alfian, D. Aji, and S. Anwar, “ANALISIS PENGELOMPOKAN PENENTUAN JURUSAN SISWA SMA MENGGUNAKAN METODE K-MEANS CLUSTERING,” Saeful Anwar J. Ilm. Betrik, vol. 12, no. 03, pp. 242–248, 2021.
[18] K. S. H. Kusuma Al Atros, A. R. Padri, O. Nurdiawan, A. Faqih, and S. Anwar, “Model Klasifikasi Analisis Kepuasan Pengguna Perpustakaan Online Menggunakan K-Means dan Decission Tree,” JURIKOM (Jurnal Ris. Komputer), vol. 8, no. 6, p. 323, 2021, doi: 10.30865/jurikom.v8i6.3680.
[19] Y. D. Darmi and A. Setiawan, “Penerapan Metode Clustering K-Means Dalam Pengelompokan Penjualan Produk,” J. Media Infotama, vol. 12, no. 2, pp. 148–157, 2017, doi: 10.37676/jmi.v12i2.418.
[20] J. O. Ong, “Implementasi Algotritma K-means clustering untuk menentukan strategi marketing president university,” J. Ilm. Tek. Ind., vol. vol.12, no, no. juni, pp. 10–20, 2013.
[2] P. D. P. Adi et al., “A Study of Programmable System on Chip (PSoC) Technology for Engineering Education,” J. Phys. Conf. Ser., vol. 1899, no. 1, p. 012163, May 2021, doi: 10.1088/1742-6596/1899/1/012163.
[3] V. M. Mulia Siregar and H. Sugara, “Implementation of artificial neural network to assesment the lecturer‘s performance,” IOP Conf. Ser. Mater. Sci. Eng., vol. 420, p. 012112, Oct. 2018, doi: 10.1088/1757-899X/420/1/012112.
[4] V. M. M. Siregar and E. D. Siringo-Ringo, “Decision Support System to Determine Scholarship Recipients using Analytical Hierarchy Process Method,” COSTA J. (Computer Sci. Technol. Appl. Journal), vol. 1, no. 1, pp. 39–49, 2023, doi: 10.35335/idss.v4i2.67.
[5] V. M. M. Siregar and E. D. Siringo-Ringo, “Decision Support System to Determine Scholarship Recipients Using the Electre Method,” COSTA J. (Computer Sci. Technol. Appl. Journal), vol. 1, no. 2, pp. 39–49, 2023.
[6] V. M. M. Siregar, K. Sinaga, E. Sirait, A. S. Manalu, and M. Yunus, “Classification of Customer Satisfaction Through Machine Learning : An Artificial Neural Network Approach,” IOTA, vol. 3, no. 3, pp. 273–282, 2023, doi: 10.31763/iota.v3i3.643.
[7] V. M. M. Siregar and M. A. Hanafiah, “Perancangan Aplikasi Augmented Reality Untuk Edukasi Penerapan Konsep Green Economy Dalam Pengembangan Desa Wisata Sebagai Upaya Mewujudkan Pembangunan Berwawasan Lingkungan,” J. TEKINKOM, vol. 6, no. 2, pp. 339–348, 2023, doi: 10.37600/tekinkom.v6i2.950.
[8] Kisno, V. M. M. Siregar, H. Sugara, A. T. Purba, and S. Purba, “Jurnal abdi insani,” J. Abdi Insa., vol. 9, no. 2, pp. 570–579, 2022.
[9] N. A. Sinaga et al., “Decision support system with MOORA method in selection of the best teachers,” AIP Conf. Proc., vol. 2453, no. July, 2022, doi: 10.1063/5.0094437.
[10] V. M. M. Siregar et al., “Decision support system for selection of food aid recipients using SAW method,” AIP Conf. Proc., vol. 2453, no. July, 2022, doi: 10.1063/5.0094385.
[11] E. Damanik and I. M. Siregar, “PENGEMBANGAN SISTEM CUSTOMER RELATIONSHIP MANAGEMENT BERBASIS WEB PADA PT. TERUS MEGA TARA JAKARTA,” J. Tek. Inf. dan Komput., vol. 4, no. 1, pp. 60–69, 2021, doi: 10.37600/tekinkom.v4i1.278.
[12] H. Sugara, V. M. M. Siregar, K. Sinaga, M. A. Hanafiah, and H. D. Pardede, “SAW and Electre Methods Implementation for Scholarship Awardee Decision,” IOTA, vol. 01, no. 4, pp. 209–220, 2021, doi: 10.31763/iota.v1i4.496.
[13] P. D. P. Adi et al., “A Performance Evaluation of ZigBee Mesh Communication on the Internet of Things (IoT),” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 7–13, 2021, doi: 10.1109/EIConCIT50028.2021.9431875.
[14] H. Priyatman, F. Sajid, and D. Haldivany, “Klasterisasi Menggunakan Algoritma K-Means Clustering untuk Memprediksi Waktu Kelulusan Mahasiswa,” J. Edukasi dan Penelit. Inform., vol. 5, no. 1, p. 62, Apr. 2019, doi: 10.26418/jp.v5i1.29611.
[15] S. Hadija, E. Irawan, and I. S. Damanik, “Penerapan Data Mining Pada Pola Penjualan Barang di Minimarket Menggunakan Algoritma Apriori Application of Data Mining on Patterns of Sales of Goods in Minimarkets Using the Apriori Algorithm,” JOMLAI (Journal Mach. Learn. Artif. Intell., vol. 1, no. 4, 2022, doi: 10.55123/jomlai.v1i4.1668.
[16] J. Dongga, A. ` Sarungallo, N. Koru, and G. Lante, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang (Studi Kasus: Toko Swapen Jaya Manokwari),” G-Tech J. Teknol. Terap., vol. 7, no. 1, pp. 119–126, 2023, doi: 10.33379/gtech.v7i1.1938.
[17] S. Wahyuni Nengsih, I. Alfian, D. Aji, and S. Anwar, “ANALISIS PENGELOMPOKAN PENENTUAN JURUSAN SISWA SMA MENGGUNAKAN METODE K-MEANS CLUSTERING,” Saeful Anwar J. Ilm. Betrik, vol. 12, no. 03, pp. 242–248, 2021.
[18] K. S. H. Kusuma Al Atros, A. R. Padri, O. Nurdiawan, A. Faqih, and S. Anwar, “Model Klasifikasi Analisis Kepuasan Pengguna Perpustakaan Online Menggunakan K-Means dan Decission Tree,” JURIKOM (Jurnal Ris. Komputer), vol. 8, no. 6, p. 323, 2021, doi: 10.30865/jurikom.v8i6.3680.
[19] Y. D. Darmi and A. Setiawan, “Penerapan Metode Clustering K-Means Dalam Pengelompokan Penjualan Produk,” J. Media Infotama, vol. 12, no. 2, pp. 148–157, 2017, doi: 10.37676/jmi.v12i2.418.
[20] J. O. Ong, “Implementasi Algotritma K-means clustering untuk menentukan strategi marketing president university,” J. Ilm. Tek. Ind., vol. vol.12, no, no. juni, pp. 10–20, 2013.
Published
2024-06-30
How to Cite
ANANDA, Lidya Rizki; ADITIA, Mellya Rindhani; NADRIATI, Sri.
PENENTUAN TINGKAT KINERJA PENDAMPING SOSIAL PROGRAM KELUARGA HARAPAN MENGGUNAKAN METODE K-MEAN CLUSTERING.
Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 1, p. 457-465, june 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1334>. Date accessed: 03 dec. 2024.
doi: https://doi.org/10.37600/tekinkom.v7i1.1334.
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