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All Journal Jurnal Teknik Informatika (JUTIF)
Rika Rosnelly
Computer Science, Universitas Potensi Utama, Indonesia
Computer Science & IT

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Optimized KNN Performance with PCA and K-Fold Cross-Validation for Colorectal Cancer Survival Prediction Yuke Manza; Rika Rosnelly; Mhd Furqan; Bob Subhan Riza
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5422

Abstract

Colorectal cancer remains a leading cause of global mortality, necessitating effective predictive tools for patient survival. While Machine Learning algorithms like K-Nearest Neighbors (KNN) utilize patient data for prediction, standard KNN implementations often suffer from the curse of dimensionality and overfitting, leading to unreliable performance on complex medical datasets. This study aims to evaluate and optimize the performance of the KNN algorithm by integrating Principal Component Analysis (PCA) for dimensionality reduction and K-Fold Cross-Validation (KFCV) to enhance model stability. The research utilized a quantitative approach on a global colorectal cancer dataset, processing demographic and clinical features through a rigorous pipeline of imputation, encoding, and normalization. Three model configurations were systematically compared: Standard KNN, KNN combined with PCA, and an optimized KNN model utilizing both PCA and KFCV across various neighbor values. The results demonstrate a distinct trade-off between predictive sensitivity and model stability. While the Standard KNN and PCA-enhanced models achieved higher recall, indicating a strong ability to identify survivors in a single data split, the fully optimized KNN+PCA+KFCV model provided the most stable and generalized accuracy with minimal deviation. These findings indicate that while PCA effectively reduces computational complexity without information loss, the integration of cross-validation is crucial for obtaining an honest assessment of model performance. This research contributes to clinical informatics by highlighting the necessity of prioritization between high sensitivity and generalization stability when developing survival prediction models for complex, inseparable medical data.
Long Short Term Memory and Gradient Boosting Model for One Day Ahead Forecasting of ANTAM Gold Bar Prices Annisa Ashari; Zakarias Situmorang; Rika Rosnelly
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5630

Abstract

This study develops and optimizes a hybrid LSTM-XGBoost forecasting model for daily ANTAM gold bar prices. The model utilizes historical time-series data of ANTAM gold prices, enriched with macroeconomic variables including the USD/IDR exchange rate and Brent oil prices, as well as derived features such as returns, lags, rolling statistics, and calendar effects. The LSTM component captures medium-term sequential patterns from the price series and macroeconomic variables, while the XGBoost component exploits a rich set of tabular features to model nonlinear relationships and volatility dynamics. Both models are trained and tuned separately, then combined through a weighted ensemble scheme in which the optimal weight is selected by minimizing Mean Absolute Percentage Error (MAPE) on the validation set. Experimental results on the test set show that the proposed hybrid model achieves Mean Squared Error (MSE) of 26,891,172.36, Root Mean Squared Error (RMSE) of 16,398.53, MAPE of 0.0058 (approximately 99.42% accuracy), and coefficient of determination \mathbit{R}^\mathbf{2} of 0.9971, outperforming a naïve baseline that assumes “tomorrow’s price equals today’s price”. The optimized LSTM-XGBoost hybrid model proves highly effective for short-term ANTAM gold price forecasting, providing reliable decision support for Indonesian gold market stakeholders.
Co-Authors Annisa Ashari Bob Subhan Riza Mhd Furqan Yuke Manza Zakarias Situmorang