<![CDATA[Purchasing a house is an important decision, yet selecting a property that matches individual preferences can be challenging because buyers must consider multiple criteria, including price, location, accessibility, facilities, building area, and financial capacity. Differences among consumer segments, such as laborers, civil servants, and entrepreneurs, also lead to varying housing priorities. This study develops a housing purchase recommendation system integrating the Analytical Hierarchy Process (AHP) and Profile Matching methods. AHP is used to objectively determine criterion weights, while Profile Matching ranks housing alternatives based on their similarity to user preferences. The system uses 1,220 housing listings collected through web scraping from the OLX marketplace, with 1,186 usable data points after data cleaning, covering three provinces on Java Island. Three consumer segments were analyzed, each producing different AHP weights with a Consistency Ratio (CR) of 0, indicating consistent assessments. Black-box testing across four scenarios achieved 100% conformity with expected results. The highest recommendation scores were 4.56 for laborers and 4.72 for entrepreneurs, reflecting differences in segment priorities. Manual calculation and system output also showed 100% agreement at every calculation stage. The proposed system can support more objective and efficient housing decisions.]]>
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