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Abstract

Meningkatnya kompleksitas jaringan 4G LTE menuntut manajemen Quality of Service (QoS) yang proaktif, di mana kemampuan prediksi downlink throughput menjadi krusial bagi operator untuk menjamin pengalaman pengguna. Penelitian ini bertujuan membangun dan mengoptimalkan model prediksi downlink throughput menggunakan metode Random Forest Regression berdasarkan data primer hasil drive test. Data yang digunakan mencakup parameter kualitas sinyal (Reference Signal Received Power, Reference Signal Received Quality, Signal-to-Interference-plus-Noise Ratio) dan data lokasi sebagai fitur input, yang diolah melalui perbandingan metode pra-pemrosesan, optimisasi hiperparameter, dan analisis rasio pembagian data untuk menemukan konfigurasi model paling akurat. Kinerja model dievaluasi menggunakan metrik Root Mean Square Error (RMSE). Hasil penelitian menunjukkan bahwa Random Forest Regression berhasil membangun model prediksi yang efektif, di mana konfigurasi terbaik dicapai dengan menggunakan seluruh fitur yang tersedia, rasio pembagian data 70:30, dan jumlah trees sebanyak 700. Model optimal ini mampu mencapai tingkat kesalahan (RMSE) sebesar 9201.575 kbps. Lebih lanjut, uji stabilitas melalui 1000 iterasi mengonfirmasi keandalan model, dengan selisih rata-rata antara nilai aktual (32222.05 kbps) dan prediksi (32253.18 kbps) yang sangat kecil, yaitu hanya 31.13 kbps. Temuan ini membuktikan bahwa model yang dikembangkan akurat, stabil, dan dapat diandalkan untuk estimasi throughput jaringan secara konsisten.

Keywords

Downlink Throughput 4G LTE Random Forest Regression Drive Test Machine Learning

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How to Cite
Adhim, R. M., Sulistyawan, V. N., & Sukamta, S. (2025). Prediksi Downlink Throughput Menggunakan Metode Random Forest Regression pada Jaringan 4G LTE Berdasarkan Data Drive Test. Jurnal Profesi Insinyur Universitas Lampung, 6(2). https://doi.org/10.23960/jpi.v6n2.190
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