DECISION TREE BASED INTERNET SIGNAL QUALITY ANALYSIS ON TELKOM INFRASTRUCTURE
DOI:
10.54443/morfai.v6i2.4949Published:
2026-01-08Downloads
Abstract
This study analyzes internet signal quality on Telkom infrastructure using the Decision Tree algorithm. Utilizing 5,000 data points from Kaggle, the research classifies network quality into three categories: Good, Fair, and Poor based on parameters such as download speed, ping latency, and packet loss. The evaluation results show that the Decision Tree model achieved an accuracy rate of 98%. Parameters such as download speed and ping latency were identified as the most dominant factors in determining signal quality. These findings prove that a machine learning approach is effective in generating easily interpretable decision rules for network service optimization.
Keywords:
Decision Tree Signal Quality Telkom Network Accuracy.References
Cisco Systems, Inc. (2023). Cisco annual internet report (2018–2023). Cisco.
https://www.cisco.com/site/us/en/solutions/annual-internet-report/index.html
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann. https://shop.elsevier.com/books/data-mining/han/978-0-12-811760-6
International Telecommunication Union. (2016). ITU-T Recommendation Y.1541: Network performance objectives for IP-based services. ITU-T.
https://www.itu.int/rec/T-REC-Y.1541/en
Kaggle. (2023). Internet speed prediction dataset. https://www.kaggle.com/datasets/getanmolgupta01/internet-speed
Katz, R. L., & Koutroumpis, P. (2013). Measuring digitization: A growth and welfare multiplier. Technovation, 33(10–11), 314–319.
https://www.sciencedirect.com/science/article/abs/pii/S0166497213000667
Telkom Indonesia. (2023). Annual report of PT Telekomunikasi Indonesia Tbk.
https://www.telkom.co.id/sites/hubungan-investor/id_ID/page/laporan-1025
Lohiya, P. B., & Bamnote, G. R. (2025). Internet Traffic Classification through Supervised Learning: Exploring Machine Learning Techniques. *Intelligent Methods in Engineering Sciences*, 4(1), 8–14. Retrieved from https://doi.org/10.58190/imiens.2025.119
Salau, A. O., & Beyene, M. M. (2024). Software defined networking based network traffic classification using machine learning techniques. *Scientific Reports*, 14, 20060. https://doi.org/10.1038/s41598-024-70983-6
Nafis, Z. M. J., Nazilla, R., Nugraha, R., & ’Uyun, S. (2024). Comparison Algorithm Decision Tree and K-Nearest Neighbor for Classification Attack Network IoT. *Computics: Journal System Computer, 13*(2), 245–252. https://doi.or g/10.34010/computika.v13i2.12609
Rosyidin, Z. U., Muladi, M., & Handayani, A. N. (2025). Determining Quality of Service (QoS) of End-User Internet Networks with Data Sniffing and Classification Algorithms. *International Journal of Artificial Intelligence Research, 9*(1). https://doi.org/10.29099/ijair.v9i1.1444
Muhajriyah, S. J., Ruslan, & Firdaus. (2024). Analysis and Classification Of Wireless LAN Network Service Quality Using The Naive Bayes Algorithm. *Journal of Computers, Information and Technology, 4*(2). https://doi.org/10.53697/jkomitek.v4i2.1841
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015).
Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095
Chen, M., Challita, U., Saad, W., Yin, C., & Debbah, M. (2020). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039–3071. https://doi.org/10.1109/COMST.2019.2926625
Sitanggang, N. S. M., Personal, O., & Hidayat, H. (2024). Implementation of the decision tree method for detecting attacks in networks. Journal of Artificial Intelligence and Engineering Applications, 4(1), 45–52. https://journal.ioinformatic.org/index.php/JAIEA/article/view/1272
Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(1), 20–28. https://doi.org/10.38094/jastt20165
License
Copyright (c) 2026 Yeni Fitri Afidah, Sri Mujiono
This work is licensed under a Creative Commons Attribution 4.0 International License.
Downloads
How to Cite
