PERBANDINGAN MODEL ENSEMBLE LEARNING DALAM MEMPREDIKSI HARGA SEWA INDEKOS DI JAKARTA

Syawaludin; Antonius Bagas Sunu W.A

doi:10.23960/jitet.v13i3S1.7737

Authors

Syawaludin Politeknik Statistika STIS
Antonius Bagas Sunu W.A. Politeknik Statistika STIS; Jl. Otto Iskandardinata No. 64C, Jakarta Timur, Indonesia 13330

DOI:

https://doi.org/10.23960/jitet.v13i3S1.7737

Abstract Views: 35 File Views: 29

Keywords:

XGBoost, feature importance, harga sewa kos, prediksi harga

Abstract

Ketersediaan kamar kos di Jakarta semakin dibutuhkan seiring dengan tingginya laju urbanisasi. Namun, lonjakan harga sewa menciptakan kebutuhan untuk memahami faktor-faktor yang mempengaruhinya secara akurat. Penelitian ini membandingkan empat model ensemble learning, yaitu XGBoost, CatBoost, Random Forest, dan LightGBM dalam memprediksi harga sewa kos berdasarkan data dari situs Mamikos. Data dikumpulkan melalui web scraping dan dilakukan pra-pemrosesan untuk menghapus nilai hilang, pencilan, dan mengubah variabel kategorikal menjadi numerik. Evaluasi model menggunakan metrik MAE, MSE, dan R², dengan hyperparameter tuning melalui Optuna. Hasil menunjukkan bahwa XGBoost memiliki performa terbaik dengan R² sebesar 0,6333. Analisis feature importance menunjukkan bahwa fasilitas seperti AC, kloset duduk, dan kamar mandi dalam memiliki pengaruh tertinggi terhadap harga sewa kos, lebih besar dibandingkan lokasi. Temuan ini mengindikasikan bahwa fasilitas kamar menjadi faktor utama dalam penentuan harga. Untuk peningkatan model di masa depan, disarankan penambahan fitur relevan dan penerapan feature engineering lanjutan.

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Author Biography

Antonius Bagas Sunu W.A., Politeknik Statistika STIS; Jl. Otto Iskandardinata No. 64C, Jakarta Timur, Indonesia 13330

Mahasiswa Politeknik Statistika STIS

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