Implementasi Machine Learning untuk Deteksi Kejujuran Wakif pada Sistem Wakaf Digital Berbasis Blockchain

Digital waqf requires a validation mechanism that not only protects data integrity after storage but also evaluates transaction credibility before the data becomes immutable on a blockchain. This study develops an intelligent pre-validation mechanism through a hybrid architecture integrating a web application, machine learning, a backend API, smart contracts, and blockchain. The dataset combined the Ethereum Fraud Detection Dataset (9,841 transactions and 51 attributes) with transaction and behavioral data from the digital waqf system. Data processing included cleaning, missing-value handling, categorical encoding, StandardScaler normalization, feature engineering, and Isolation Forest anomaly detection. The resulting anomaly score was normalized into a 0–100 trust score and passed to the smart contract to process high-trust transactions, route medium-risk transactions to nazhir review, and block high-risk transactions before on-chain recording. Built using Python, Scikit-learn, PHP, MySQL, Solidity, Ganache, and MetaMask, the prototype demonstrated an end-to-end pre-validation flow from waqif input to on-chain decision-making. Representative tests yielded trust scores of 92 (normal), 68 (review), and 27 (fraud), showing that separating off-chain inference from on-chain validation provides an effective control layer against garbage in, garbage out without imposing machine learning computation on the smart contract.