Aisyah Alhumairo, . (2026) RANCANG BANGUN WEBSITE OPTIMASI BTS 4G BERDASARKAN 5 ALGORITMA MACHINE LEARNING. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
The surge in data traffic within the Jabodetabek area demands a stable and reliable 4G LTE network infrastructure. However, manual handling of network faults is often slow and inefficient. This study aims to design an AI-based recommendation system to support Self-Healing automation within the Self-Organizing Network (SON) framework. The system is developed as an interactive website using the Streamlit framework, integrating Coverage Monitoring (CovMo) data, Incident Logs, and BTS Metadata. This research compares the performance of five Machine Learning algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, and XGBoost, utilizing resampling techniques to address data imbalance. The results demonstrate that XGBoost is the champion model, achieving a composite score of 0.853, an AUC-ROC of 0.976, and a Recall of 0.864. Out of 2,668 observations, the system successfully identified 597 sites (22.4%) requiring optimization and generated 1,819 technical action recommendations. The majority of recommendations focused on coverage optimization (40.7%), with 68% of total actions being executable automatically or remotely. The implementation of this system has proven effective in prioritizing critical fault resolution and enhancing network operational efficiency.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Additional Information: | [No.Panggil: 2110314035] [Pembimbing 1: Silvia Anggraeni] [Pembimbing 2: Muhamad Alif Razi] [Penguji 1: Ayu Mika Sherila] [Penguji 2: Subekti Ari Santoso] |
| Uncontrolled Keywords: | 4G LTE, Machine Learning, Self-Healing, Self-Organizing Network (SON), Streamlit, XGBoost. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Teknik > Program Studi Teknik Elektro (S1) |
| Depositing User: | AISYAH ALHUMAIRO |
| Date Deposited: | 29 Apr 2026 04:18 |
| Last Modified: | 29 Apr 2026 04:18 |
| URI: | http://repository.upnvj.ac.id/id/eprint/49939 |
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