Optimization of Constraints in Valins New Installation Data Validation in Monitoring ODP Installation Progress Using the K-Means Algorithm (Case Study: PT Telkom Akses Bengkulu)

Maharanti Putri Cempaka; Andang Wijanarko; Dirgan Wisang

doi:10.70656/ijcse.v3i02.900

Authors

Maharanti Putri Cempaka Information System Study Program, Department of Engineering, University of Bengkulu
Andang Wijanarko Information System Study Program, Department of Engineering, University of Bengkulu
Dirgan Wisang PT Telkom Akses Bengkulu

DOI:

https://doi.org/10.70656/ijcse.v3i02.900

Keywords:

Data Mining, K-Means Clustering, Valins PSB, PSB, Data Validation, Monitoring

Abstract

The Valins PSB data validation process played a crucial role in monitoring the progress of ODP installation at PT Telkom Akses Bengkulu. Accurate and comprehensive validation was necessary for the monitoring system to provide reliable information. However, in practice, there were still challenges such as incomplete data, data inconsistencies, limitations in the use of QR barcodes, and disruptions to the validation bot, which resulted in some data remaining unvalidated and affected the effectiveness of monitoring. This study aimed to analyze validation challenges, identify data patterns, and provide optimization recommendations. The method used was K-Means Clustering with 1,647 Valins PSB data points that underwent selection, cleaning, and transformation before the clustering process. The results showed that the data could be grouped into several clusters representing different levels of validation success, thereby helping identify patterns of challenges based on location. This study was expected to support improvements in validation quality and monitoring effectiveness at PT Telkom Akses Bengkulu.

Downloads

Download data is not yet available.

References

[1] K. Mustaqim, G. Al Raffi, N. S. Fitri, and N. Riza, “Penerapan Metode K-Means Clustering Untuk Klasifikasi Tingkat Literasi Terhadap Penyebaran Hoaks Di Provinsi Jawa Barat,” vol. 5, no. 2, pp. 1–9, 2025.

[2] S. I. Safitri, C. Suhery, S. Bahri, J. Rekayasa, and S. Komputer, “IMPLEMENTASI ALGORITMA K – MEANS UNTUK CLUSTERING SENTIMEN PADA OPINI KUALITAS PELAYANAN,” vol. 09, no. 02, pp. 186–197, 2021.

[3] R. G. Guntara, “Visualisasi Data Laporan Penjualan Toko Online Melalui Pendekatan Data Science Menggunakan Google Colab,” vol. 2, no. 6, pp. 2091–2100, 2023.

[4] B. M. Suparwanto, “Visualisasi Data Dari Data Ketidakhadiran Mahasiswa Menggunakan Pemrograman Python,” J. Teknol. Inf., vol. 02, no. 02, pp. 103–113, 2023.

[5] M. B. Uddin and Z. Fatah, “Penerapan Data Mining Clustering K-Means Dalam Mengelompokkan Data Penduduk Penyandang Disabilitas,” Gudang J. Multidisiplin Ilmu, vol. 2, no. November, pp. 86–94, 2024.

[6] A. Fatkhudin et al., “Implementasi Algoritma Clustering K-Means Dalam Pengelompokan Mahasiswa Studi Kasus ( Prodi Manajemen Informatika ),” vol. 12, pp. 777–783, 2023.

[7] N. R. Setri Dwi Prasetiani, “Penerapan Data Mining Untuk Clustering Menu Favorit Menggunakan Algoritma K-Means (Studi Kasus Kedai Expo ),” J. Inform. dan ilmu Komput., vol. 03, pp. 278–286, 2022.

[8] A. Alif, R. Mulyana, A. R. Juwita, A. M. Siregar, and A. Fauzi, “Penerapan Algoritma K-means Clustering dan Hierarchical Clustering dalam Mengelompokkan Data Pengangguran di Karawang,” no. 2021, 2024, doi: 10.33364/algoritma/v.21- 2.2155.

[9] N. Hendrastuty, “Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa,” vol. 3, pp. 46–56, 2024.

[10] B. Ariansah et al., “Penerapan K-Means Clustering untuk Pengelompokan Data Industri Kecil Menengah di Provinsi Jambi,”

vol. 6, no. September, pp. 359–371, 2025, doi: 10.35957/jtsi.v6i2.13553.