Analisis Waktu Pemprosesan Layanan Enterprise PT. Telkomsat Menggunakan Metode Regresi Linear Berganda
DOI:
https://doi.org/10.55382/jurnalpustakadata.v6i3.1912Kata Kunci:
regresi linear berganda, waktu pemprosesan, cuaca, Jarak lokasi, TelkomsatAbstrak
This study aims to analyze the influence of technical and non-technical factors on the company's service processing duration. The research data were obtained from the company's historical service records with independent technical variables consisting of Carrier-to-Noise Ratio (CNR), Signal Quality Factor (SQF), Customer Priority Index (CPI), and Latency, as well as non-technical variables including weather conditions and location distance. The analysis method applied in this study is multiple linear regression with a quantitative approach. The simultaneous test results (F-test) indicated that all independent variables collectively exert a significant effect on service processing duration. However, the partial test results (t-test) specifically proved that only weather and location distance variables have a significant effect p < 0,05, while the technical factors do not contribute a significant influence independently. The regression model developed yields an Adjusted R² value of 0.868, indicating that 86.8% of the variation in service processing duration can be explained by the studied independent variables. Furthermore, the model evaluation demonstrates a low prediction error rate, with a Mean Absolute Percentage Error (MAPE) value of 10.6%. These findings conclude that non-technical or external factors hold a more dominant role in determining the efficiency of service processing time compared to the company's internal technical factors
Unduhan
Referensi
A. Maulida and A. W. Arsyad, “Strategi Personal Selling dalam Mengembangkan Bisnis B2B (Business To Business) di PT Telkom Indonesia Witel Kaltimtara,” MARTABE J. Pengabdi. Masy., vol. 8, no. 6, pp. 2473–2478, 2025.
M. Tohir, A. Primadi, and N. A. H. Subroto, “Pengaruhi Kualitas Pelayanan, Fasilitas Dan Ketepatan Waktu Terhadap Kepuasan Pelanggan,” J. Manaj. Kreat. dan Inov., vol. 1, no. 4, pp. 35–59, 2023.
A. S. Nugroho, “Evaluasi Infrastruktur Jaringan Komputer untuk Mendukung Efisiensi Komunikasi Data pada Sistem Point of Sales Industri Ritel Modern,” vol. 5, no. 1, pp. 502–507, 2026.
S. Nazhif Putransyah, “Pengaruh Peningkatan Kualitas Layanan Terhadap Service Level Agreement (Sla) Pada Bank Bri Kc. Kendal,” Knowl. J. Inov. Has. Penelit. dan Pengemb., vol. 4, no. 1, pp. 16–28, 2024.
A. Najiha, M. A. Fathan, A. Alavi, F. Chaikal, and G. C. Basompe, “Analisis Regresi Linear Berganda pada Pengaruh Pemahaman Etika Profesional dan Penguasaan Konsep Keahlian terhadap Kesiapan Dunia Kerja,” J. Penelit. Inov., vol. 5, no. 3, pp. 2365–2376, 2025.
N. Saraswati and M. P. Ariasih, “Aplikasi m-Banking Terfavorit di Indonesia Tahun 2023 - 2024,” J. Manaj., vol. 11, no. 1, pp. 343–353, 2025.
I. Anwari and I. N. Yulita, “Penggunaan Machine Learning Untuk,” vol. 4, no. 3, pp. 6001–6005, 2023.
M. Gun, “Pentingnya Uji Asumsi Klasik pada Analisis Regresi Linier Berganda (Studi Kasus Penyusunan Persamaan Allometrik Kenari Muda [Canarium Indicum L.]),” BAREKENG J. Ilmu Mat. dan Terap., vol. 14, no. 3, p. 335, 2020.
D. Marcelina, A. Kurnia, and T. Terttiaavini, “Analisis Klaster Kinerja Usaha Kecil dan Menengah Menggunakan Algoritma K-Means Clustering,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 2, pp. 293–301, 2023.
J. T. Moreira-Filho, D. Ranganath, M. Conway, C. Schmitt, N. Kleinstreuer, and K. Mansouri, “Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow,” J. Cheminform., vol. 16, no. 1, pp. 1–27, 2024.
R. W. B. D. A. N. J. Atty Tri.Sarman, “Penerapan Sistem Penjadwalan Dengan Metode SPT (Short Processing Time) Dan EDD (Earliest Due Date) Dalam Mengefisiekan Biaya Operasional Dan Sumber Daya Pada Perusahaan PT. Mathar Telekomunikasi Indonesia,” J. Ecodemica J. Ekon. Manaj. dan Bisnis, vol. 8, no. 2, 2024.
I. Meiliana et al., “Pengaruh Penggunaan Gadget , Minat Belajar Dan Kehadiran Terhadap Prestasi Belajar Siswa Di SMKN 1 Maros Dengan Regresi Linier Berganda,” vol. 18, no. 2, pp. 66–75, 2026.
A. Kurniawan and Fairus, “Analisis Regresi Linear Berganda Untuk Melihat Faktor,” J. Math. UNP, vol. 7, no. 2, pp. 62–68, 2025.
U. J. Mwaipungu, K. Malekela, and R. Monko, “Regression and Validation Modelling for Predicting Constraining Factors in Design-Bid-Build Project Delivery,” vol. 14, no. 1, pp. 24–39, 2025.
E. N. R. D. F. T. P. P. E. & Widoso, “Penerapan metode regresi linier berganda untuk memperkirakan curah hujan (studi kasus: Stasiun Geofisika Sleman),” J. Ilm. Mat., vol. 9, no. 1, pp. 8–18, 2022.
Vibhu Verma, “A Comprehensive Framework for Residual Analysis in Regression and Machine Learning,” J. Inf. Syst. Eng. Manag., vol. 10, no. 31s, pp. 34–46, 2025.
A. Ihsan Fairuzsyifa and Y. Sulistyo Nugroho, “Analisis Regresi Linier Berganda Pengaruh Minat Calon Mahasiswa di Universitas Muhammadiyah Surakarta Menggunakan Python,” J. Inform. Polinema, vol. 10, no. 2, pp. 265–272, 2024.
##submission.downloads##
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Andi Taufik, Putra Muslimin

Artikel ini berlisensi Creative Commons Attribution 4.0 International License.






