<![CDATA[Among PT PLN (Persero)'s roughly 5,457 idle property locations across Indonesia, screening and prioritizing commercialization candidates has long fallen to just four staff in the utilization sub-division, since PLN, unlike Pertamina or KAI, has no dedicated property subholding. This mismatch in scale and capacity leaves the Identification of Potential Property Assets stage under Perdir 0001 and Perlak 0004 dependent on subjective, unit-by-unit judgment. This study designs and validates a Random Forest based Decision Support System producing a propensity score indicating how closely an idle asset resembles successfully commercialized assets, serving as a screening instrument at the pre-HBU stage. The model is trained on 3,876 observations comprising 3,142 idle and 734 successfully commercialized assets, using 94 features representing three theoretical pillars: physical lot characteristics from Optimal Lot Theory, locational accessibility from bid-rent theory, and regional economic conditions, with geospatial features extracted via a purpose built GeoFeature Extractor. Scores are computed through out-of-bag prediction to avoid training data contamination, then adjusted with a location viability score to correct for the land area variable's 27.01% share of feature importance, confirmed by ablation testing not to be trivial since removing it drops AUC by only 0.0079. On the test set, the model achieves an AUC-ROC of 0.9898 and 95.62% accuracy, supported by 5-fold cross-validation yielding a mean AUC of 0.9925±0.0016 and well calibrated probabilities, Brier score 0.0300. Of the 3,113 ranked assets, 445, or 14.3%, are identified as priority candidates, cutting by roughly 85.7% the volume requiring evaluation before Highest and Best Use analysis. Keywords: Decision Support System, idle assets, machine learning, propensity score, property utilization, Random Forest]]>
