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All Journal Jurnal Indonesia Sosial Teknologi
Rafi Farizki
Sekolah Tinggi Manajemen Informatika dan Komputer LIKMI, Indonesia
Computer Science & IT Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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Permissioned Blockchain and Smart Contracts for Halal Supply Chain Traceability in Indonesia Rafi Farizki; Rani Santika; Vo Hung Cuong
Jurnal Indonesia Sosial Teknologi Vol. 7 No. 3 (2026): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v7i3.9207

Abstract

Assurance of halal integrity along the supply chain carries major economic and religious weight in Indonesia, yet conventional traceability relies on centralized databases and paper certificates that are difficult to verify, easy to tamper with, and fragmented across the many actors between farm and consumer. This study designs and evaluates HalalChain, a permissioned-blockchain and smart-contract traceability framework that records each custody transfer on a shared, tamper-evident ledger and encodes halal certification rules as executable logic. Using a design science methodology, supply-chain actors participate as permissioned nodes on Hyperledger Fabric, bulky documents are stored off chain with only their cryptographic hashes on chain, and consumers verify authenticity by scanning a code; the framework was compared against a centralized-database baseline across throughput and latency, scalability, traceability completeness and time to trace, and security and trust, complemented by a structured expert assessment. The evaluation indicates that the permissioned blockchain strengthens traceability and trust, yielding a more complete, verifiable history, faster tracing, and demonstrated tamper-evidence and access control, while keeping performance within practical bounds through the on-chain/off-chain design. The framework provides a deployable reference architecture linking blockchain traceability to halal certification governance, transferable to other provenance-critical chains. A permissioned blockchain can improve halal traceability and trust at an acceptable performance cost.
Privacy-Preserving Federated Learning for Stunting Risk Prediction across Indonesian Community Health Centers Rafi Farizki; Rani Santika; Meritorious Autonomous
Jurnal Indonesia Sosial Teknologi Vol. 7 No. 4 (2026): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v7i4.9208

Abstract

Stunting remains a serious public-health challenge in Indonesia, and data-driven prediction could help health workers identify at-risk children earlier and target limited resources more effectively. However, maternal and child health data are highly sensitive and are fragmented across many community health centers, and centralizing such records for model training raises privacy, legal, and infrastructural obstacles. This study designs and evaluates FedStunt, a privacy-preserving federated learning framework that trains a shared stunting-risk prediction model across distributed community health centers without moving raw records off site. The framework combines federated averaging with a differential-privacy mechanism based on gradient clipping and calibrated noise, secure aggregation so that the server observes only aggregated updates, and a proximal term to cope with statistically heterogeneous data across sites. Following a design science methodology, the artifact was compared against a centralized model that serves as a privacy-agnostic upper bound and against local-only models trained separately at each site. Evaluation spans discrimination and sensitivity, calibration, fairness across sites, the differential-privacy budget, and communication cost. The results are intended to show whether privacy-preserving federation can approach centralized predictive quality while keeping data local, and to provide a governance-friendly and reusable blueprint for collaborative health analytics in low-resource settings where data sharing is constrained.
Co-Authors Meritorious Autonomous Rani Santika Vo Hung Cuong