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Journal : international journal software engineering and computer science ijsecs

A Multi-Criteria Decision Support System for Burial Plot Selection Using the TOPSIS Method: A Web-Based Approach Khoirunnisya Khoirunnisya; Muhamad Arief Yulianto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6860

Abstract

Rapid and accurate decision-making is essential in burial plot selection, particularly in urgent situations where families must simultaneously evaluate multiple criteria — price, available facilities, and land type. At TPU Al-Qobri, the absence of structured decision tools has allowed the manual selection process to persist, producing inconsistent outcomes and placing unnecessary burden on families during emotionally difficult circumstances. This study develops a web-based Decision Support System (DSS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support objective burial plot selection based on multiple weighted criteria. System development follows the Software Development Life Cycle (SDLC), covering planning, analysis, design, implementation, testing, and maintenance phases. TOPSIS is applied to rank available burial plot alternatives and generate recommendations that are traceable and consistent across evaluation sessions. Testing results confirm that the system produces accurate rankings, reduces selection time, and improves administrative service management at the cemetery level. The proposed system demonstrates that structured, criterion-based decision support can replace subjective manual processes in public cemetery administration.
Revisiting Feature Scaling in Linear Regression: An Empirical Study on Microsoft Stock Price Prediction Farhan Mahfudz; Khoirunnisya Khoirunnisya
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6873

Abstract

Stock price prediction occupies a central position in quantitative finance, bearing directly on risk management, portfolio construction, and investment decision-making. This study evaluated the effect of feature scaling on linear regression performance in predicting Microsoft (MSFT) stock prices. A quantitative experimental design was employed, drawing on historical MSFT stock data spanning 2014 to 2024. Preprocessing involved data cleaning, outlier treatment via the Interquartile Range (IQR) method, and feature standardization through Z-score normalization. Two experimental conditions were tested: linear regression without feature scaling and linear regression with feature scaling. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Both conditions produced nearly identical results — R² approaching 0.99, with negligible divergence across all error metrics. The evidence suggests that feature scaling does not meaningfully alter the predictive behavior of linear regression. For simple linear models operating without regularization, scaling appears to be an unnecessary preprocessing step, a finding that warrants more deliberate evaluation of preprocessing decisions in machine learning pipelines.