ALFAQIH, LUTHFANSA (2025) PERBANDINGAN METODE SIMPLE LINEAR REGRESSION, POLYNOMIAL REGRESSION, DAN K-NEAREST NEIGHBORS (KNN) UNTUK PREDIKSI TRANSAKSI PERBANKAN. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
In the digital era, the banking sector faces complex challenges in managing transaction data and making strategic decisions. Bank XYZ, like many other banks, collects a large amount of daily transaction data that includes volume, time, transaction type, and customer profiles. Although this data has great potential to provide valuable insights, it is often not fully utilized to support smarter and more responsive decision-making.This study examines the effectiveness of three prediction methods in banking transaction analysis: Simple Linear Regression, Polynomial Regression, and K-Nearest Neighbors (KNN). Transaction data was collected and processed through data cleaning. Subsequently, predictive models were built and tested using training data and testing data. The prediction results from the three models were compared using Root Mean Square Error (RMSE) to determine the best- performing model. Based on the testing results, Polynomial Regression showed the best performance with an RMSE of 0.1724, while Simple Linear Regression had an RMSE of 0.1844, and K-Nearest Neighbors (KNN) had an RMSE of 0.1739. The conclusion is that, based on the RMSE values of the three methods, Polynomial Regression outperformed both Simple Linear Regression and K-Nearest Neighbors (KNN). Keywords: Simple Linear Regression, Polynomial Regression, K-Nearest Neighbors (KNN), Prediction, Data Cleaning, RMSE Dalam era digital, sektor perbankan menghadapi tantangan kompleks dalam mengelola data transaksi dan pengambilan keputusan strategis. Bank XYZ, seperti banyak bank lain, mengumpulkan banyak data transaksi harian yang mencakup volume, waktu, jenis transaksi, dan profil pelanggan. Meskipun data ini memiliki potensi besar untuk memberikan wawasan berharga, seringkali belum dimanfaatkan optimal untuk mendukung pengambilan keputusan yang lebih cerdas dan responsif. Penelitian ini membahas keefektifan tiga metode prediksi dalam analisis transaksi perbankan, yaitu Simple Linear Regression, Polynomial Regression, dan K-Nearest Neighbors (KNN). Data transaksi dikumpulkan dan diproses melalui data cleaning. Setelah itu, model prediksi dibangun dan diuji menggunakan data training dan testing data. Hasil prediksi dari ketiga model dibandingkan menggunakan Root Mean Square Error (RMSE) untuk menentukan model dengan performa terbaik. Berdasarkan hasil pengujian ketiga metode menunujkan bahwa metode Polynomial Regression memiliki hasil yang lebih baik untuk nilai RMSE 0,1724, sedangkan untuk metode Simple Linear Regression mendapatkan nilai RMSE 0,1844, dan metode K-Nearest Neighbors (KNN) mendapatkan nilai RMSE 0,1739. Kesimpulan nya adalah berdasarkan nilai RMSE 3 metode diatas, Polynomial Regression lebih unggul dibandingkan metode Simple Linear Regression dan K-Nearest Neighbors (KNN). Kata Kunci: Simple Linear Regression, Polynomial Regression, K-Nearest Neighbors (KNN), Prediksi, Data Cleaning, RMSE
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