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All Journal Nuansa Informatika Bulletin of Intelligent Machines and Algorithms
Tiara Permata Hati
Universitas Informatika dan Bisnis Indonesia
Computer Science & IT

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A Data-Driven Approach to Comparative Evaluation of Regression Models for Accurate House Price Prediction: Pendekatan Berbasis Data untuk Evaluasi Komparatif Model Regresi untuk Prediksi Harga Rumah yang Akurat Tiara Permata Hati; Budiman Budiman; Imannudin Akbar; Nur Alamsyah
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.411

Abstract

This study aims to develop and evaluate a property price prediction model in Bandung by applying machine learning (ML) algorithms. The need for more accurate property price predictions is increasing due to fluctuations in the property market. This study analyzes property characteristics, including the number of bedrooms, bathrooms, land area, building area, and location, as well as their impact on house prices. The study evaluates four regression algorithms, including linear regression, K-Nearest Neighbors (KNN), Random Forest, and XGBoost. Finally, this study proposes price_per_m2 and building_land_ratio as new features recommended for improvement in accuracy. The bottleneck method is derived from the data collection area of the Rumah123.com website, encompassing data preprocessing and data exploration. The following metrics will be used to evaluate each model: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). Based on our study, we conclude that both Random Forest Regression and XGBoost Regression achieve the highest accuracy, with R² values of 0.9941 and 0.9955, respectively, after adjustment. Conversely, Linear Regression and KNN Regression have the lowest accuracy, with KNN Regression being the least accurate. The primary contribution of this study is the development of a more accurate house price prediction model that can be applied in cities with similar market characteristics. These findings provide practical insights for property developers and buyers when making investment decisions.
Stacking Ensemble Revenue Predictions in Digital Marketing: A SHAP-Based Analysis of Price and Quantity as Key Predictors Hana Maulid; Tiara Permata Hati; Fikri Muhamad Fahmi
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 1 (2025): BIMA November 2025 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i1.1

Abstract

This study focuses on developing a digital marketing conversion prediction model using an ensemble stacking approach combined with explainable artificial intelligence (XAI) methods to improve model transparency. The primary objective of this study is to investigate the impact of price and product quantity on revenue predictions, as well as to gain a clearer understanding of the factors that influence customer purchasing behaviour in the context of digital sales. The methodology used includes data collection from a Kaggle dataset containing 3,000 records and 15 features related to customer demographics, product information, and marketing channels. The preprocessing stage ensures data quality, followed by feature engineering and model development using an ensemble stacking model consisting of Logistic Regression, Gaussian Naïve Bayes, and Support Vector Classification. Model evaluation was conducted using precision, recall, F1-score, and ROC-AUC metrics, with performance improvements achieved through cross-validation and probabilistic calibration. The study results showed that model accuracy reached 0.97, with significant contributions from price and product quantity features, as seen in the SHAP analysis. The ensemble stacking model provided stable and reliable predictions. These findings underscore the importance of effective pricing strategies and product volume optimisation in driving revenue growth. The use of SHAP enhances interpretability, enabling businesses to make more informed decisions. This research contributes to the development of transparent and practical machine learning applications in digital marketing, providing valuable implications for business strategy optimization.
Co-Authors Akbar, Imannudin Budiman Fikri Muhamad Fahmi Hana Maulid Nur Alamsyah