Segmentasi Tunggakan Pelanggan Menggunakan Algoritma K-Means Cluster pada Perusahaan Air Minum Daerah

Syifa Chairunnissa Deliva Akbar; Sarjon Defit; Billy Hendrik

doi:10.55382/jurnalpustakaai.v5i2.1215

Penulis

Syifa Chairunnissa Deliva Akbar upi yptk padang http://orcid.org/0009-0003-5254-478X
Sarjon Defit Universitas Putra Indonesia "YPTK"
Billy Hendrik Universitas Putra Indonesia "YPTK"

DOI:

https://doi.org/10.55382/jurnalpustakaai.v5i2.1215

Kata Kunci:

data mining, k-means clustering, tunggakan, pelanggan, perusahaan umum daerah, air minum, clustering

Abstrak

Perusahaan Air Minum Daerah (Perumdam) Tirta Anai is a Regional Elected Business Entity providing clean water services to customers, but based on the BPKP performance report, this company is categorized as an unhealthy BUMD. One of the factors causing this is due to the high arrears of customers which have an impact on the company's revenue, while efforts in the form of late fines have not been able to provide a deterrent effect to customers. Based on this, this research was carried out with the aim of segmenting customer arrears at the Tirta Anai Regional Drinking Water Company. Segmentation is carried out using the K-Means Clustering algorithm. K-Means Clustering is a data mining algorithm used in grouping data based on its similarity in characteristics. The data in this study is sourced from the database of customers who are in arrears at the Tirta Anai Regional Drinking Water Company as of May 2025 which focuses on the Household group, with as many as 20,646 customer arrears data. From this population, samples were taken using the Slovin formula with an error rate of 5% so that 392 data were analyzed. The parameters used in analyzing this study are the number of months of customer arrears and total customer arrears. Based on the K-Means Clustering method, it is proven to be able to group customers based on their payment patterns. The results are divided into C0 (Low) containing 327 data, C1 (High) containing 6 data, and C2 (Medium) containing 59 data. The contribution of this research has an impact on companies in taking strategies for handling customer service in managing existing connections.

Unduhan

Data unduhan belum tersedia.

Referensi

A. F. Anwar, M. R., Nisa, F. L., & Utami, “Analisis Pengaruh Kesadaran Pelanggan Dan Kualitas Layanan Terhadap Tunggakan Pembayaran Air Di PDAM Surabaya,” J. Ilm. Wahana Pendidik., vol. 24, no. 7, pp. 28–42, 2024.

T. Hidayat and M. A. Putra, “Pelayanan Umum di Kantor Camat Koto Parik Gadang Diateh Kabupaten Solok Selatan,” J. Pengabdi. Masy. Bangsa, vol. 1, no. 12, pp. 3729–3735, 2024, doi: 10.59837/jpmba.v1i12.728.

S. S. Mutiara, “Analisis Penyebab Tunggakan Pembayaran Rekening Air Pelanggan pada Perusahaan Daerah Air Minum (PDAM) Tirta Sari Binjai,” Ekon. Bisnis Manaj. dan Akunt., vol. 183, no. 2, pp. 153–164, 2023.

A. Awaluddin, S. Mardjuni, and L. Setiawan, “Peran Kualitas Layanan, Kepuasan Pelanggan Dan Perilaku Pelanggan Terhadap Tunggakan Tagihan Listrik,” Indones. J. Bus. Manag., vol. 6, no. 1, pp. 31–39, 2023, doi: 10.35965/jbm.v6i1.3866.

D. A. Widodo and N. Nurbayti, “Pengurangan Ketidakpastian Desk Collection dalam Penagihan Tunggakan Nasabah PayLater,” WACANA J. Ilm. Ilmu Komun., vol. 23, no. 2, pp. 415–427, 2024, doi: 10.32509/wacana.v23i2.4558.

N. Nurhayati, S. Pohan, and M. Nursidin, “Analisis Pengendalian Piutang Tak Tertagih Terhadap Tunggakan Pembayaran Rekening Air Pelanggan Perusahaan Daerah Air Minum (PDAM) Tirtanadi Cabang Berastagi,” Worksh. J. Akunt., vol. 3, no. 1, pp. 52–62, 2023, doi: 10.46576/wjs.v3i1.4026.

Amyra Zhahra Lubis, Muthia Rizti, and Annio Indah Lestari Nasution, “Kinerja Bisnis PTPLN (Persero) ULP Medan Timur?: Mengatasi Tunggakan Melalui Pendekatan Terpadu,” J. Penelit. Ekon. Manaj. dan Bisnis, vol. 3, no. 1, pp. 185–190, 2023, doi: 10.55606/jekombis.v3i1.3077.

M. Robani and A. Widodo, “Algoritma K-Means Clustering Untuk Pengelompokan Ayat Al Quran Pada Terjemahan Bahasa Indonesia,” J. Sist. Inf. Bisnis, vol. 6, no. 2, p. 164, 2016, doi: 10.21456/vol6iss2pp164-176.

A. Yudistira and R. Andika, “Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering,” J. Artif. Intell. Technol. Inf., vol. 1, no. 1, pp. 20–28, 2023, doi: 10.58602/jaiti.v1i1.22.

A. Nur Khormarudin et al., “Teknik Data Mining: Algoritma K-Means Clustering,” J. Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 1, no. 2, pp. 116–123, 2022, [Online]. Available: https://djournals.com/klik%0Ahttps://ilmukomputer.org/category/datamining/

A. Pratama, M. D. Irawan, and S. D. Andriana, “Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS,” J. Comput. Networks, Archit. High Perform. Comput., vol. 6, no. 2, pp. 771–782, 2024, doi: 10.47709/cnahpc.v6i2.3771.

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.

Selfi Melisa, S. Defit, and R. Sovia, “Penerapan Algortima K-Means Clustering untuk Optimalisasi Persediaan Liquid Vape Berdasarkan Data Penjualan,” J. KomtekInfo, vol. 12, pp. 38–45, 2025, doi: 10.35134/komtekinfo.v12i1.620.

G. S. Maruli Andi, “Visualisasi Serta Efektivitas Colour Clustering dengan K-Means dan Bee Algorithm,” J. Comput. Inf. Syst. (J-CIS, vol. 8, no. 1, pp. 13–30, 2025, [Online]. Available: https://doi.org/10.31605/jcis.v8i1

R. A. Indraputra and R. Fitriana, “K-Means Clustering Data COVID-19,” J. Tek. Ind., vol. 10, no. 3, pp. 275–282, 2020, [Online]. Available: https://www.trijurnal.lemlit.trisakti.ac.id/index.php/tekin/article/view/8428/6033

S. Ocktavia and W. T. Atmojo, “Analisis Segmentasi Pelanggan Pembiayaan Berdasarkan Demografi Untuk Memprediksi Tingkat Kredit Menggunakan Algoritma K-Means,” J. Inform. Teknol. dan Sains, vol. 7, no. 1, pp. 394–402, 2025, doi: 10.51401/jinteks.v7i1.5582.

J. B. Sembiring, H. Manurung, and A. Sihombing, “Pengelompokan Data Tunggakan Pembayaran Kredit Mobil Menggunakan Metode Clustering (Studi Kasus: Cv Citra Kencana Mobil),” J. Manaj. Inform. Jayakarta, vol. 3, no. July, pp. 275–291, 2023, [Online]. Available: http://journal.stmikjayakarta.ac.id/index.php/JMIJayakarta

A. Srimurdianti, Sukamto, W. Setiawan, E. Esyudha, and Pratama, “JEPIN (Jurnal Edukasi dan Penelitian Informatika) Data Mining untuk Pengelompokan Saham pada Sektor Energi dengan Metode K-Means,” vol. 9, no. 1, pp. 76–81, 2023, [Online]. Available: https://jurnal.untan.ac.id/index.php/jepin/article/viewFile/62509/75676597024

N. Afiasari, N. Suarna, and N. Rahaningsih, “Implementasi Data Mining Transaksi Penjualan Menggunakan Algoritma Clustering dengan Metode K-Means E-commerce K-Means melakukan analisis penerapan Data Mining dalam mengelompokkan jumlah,” vol. 9, pp. 100–110, 2023.

A. Agung, A. Daniswara, I. Kadek, and D. Nuryana, “Data Preprocessing Patterns in the Assessment of Teacher Education Program Students,” J. Informatics Comput. Sci., vol. 05, pp. 97–100, 2023.

U. T. Suryadi and Y. Supriatna, “Cluster 2 = 169,” vol. 22, no. April, p. 111, 2019.

Y. Istianto and S. ’Uyun, “Klasifikasi Kebutuhan Jumlah Produk Makanan Customer Menggunakan K-Means Clustering dengan Optimasi Pusat Awal Cluster Algoritma Genetika,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 5, p. 861, 2021, doi: 10.25126/jtiik.2021842990.

A. Aditya Nugroho and P. Paduloh, “Analisis Clustering Kasus Covid – 19 di Indonesia Menggunakan Algoritma K-Means,” J. Eng. Environtmental Energy Sci., vol. 2, no. 2, pp. 111–118, 2024, doi: 10.31599/eppng886.

M. A. K-means, I. Prabujaya, F. Ibrahim, A. A. Muin, and S. Wahyuni, “Sistem Klasterisasi Data Rekam Medis pada Puskesmas Kampili,” vol. 1, no. 2, pp. 106–120, 2025.