<![CDATA[Purpose – This study develops and revises a web-based decision support system for laptop selection by integrating web-scraped iPrice Indonesia product data, reproducible preprocessing, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The study responds to the difficulty consumers face when comparing many laptop alternatives with heterogeneous specifications, prices, and budget constraints. Methods – The prototype used a verified static CSV dataset derived from public product listings. Five complete laptop alternatives were evaluated with 12 criteria: brand, screen size, screen resolution, processor, storage type, storage capacity, graphics card, laptop weight, battery durability, new-price distance, preloved-price distance, and RAM. Categorical attributes were transformed into ordinal scores. TOPSIS was implemented in Python and Streamlit. Simple Additive Weighting (SAW), sensitivity analysis, and functional black-box testing were used as comparative and verification procedures. Findings – Under equal criterion weights of 1.5, a new-laptop budget of IDR 7,500,000, and a preloved-laptop budget of IDR 5,000,000, HP Envy x360 13-inch obtained the highest TOPSIS closeness coefficient of 0.746618238. SAW selected the same top alternative, although the complete ranking differed and produced a moderate Spearman correlation of 0.400. Research implications – The results show that transparent criterion transformation, budget-distance modeling, and interface-based preference adjustment can support practical laptop selection. The system does not replace consumer judgment because the ranking depends on the dataset, weights, scoring rules, and budget assumptions. Originality – This study contributes a reproducible DSS prototype that combines scraped price-comparison data, TOPSIS ranking, SAW benchmarking, sensitivity checking, and an Indonesian-language Streamlit interface for practical laptop recommendation.]]>
