Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal Jurnal Indonesia Sosial Teknologi Jurnal Ekonomi, Teknologi dan Bisnis
Rani Santika
Universitas Muhammadiyah prof Dr hamka, Indonesia
Astronomy Computer Science & IT Economics, Econometrics & Finance Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
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.
Explainable Lightweight CNN for On-Device Rice-Leaf Disease Detection in Indonesian Smallholder Farms Rafi Farizki; Rani Santika; Vo Hung Cuong
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 5 No. 4 (2026): Jurnal Ekonomi, Teknologi dan Bisnis
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/hmavwp42

Abstract

Background: Rice is the primary staple food crop in Indonesia, yet leaf diseases—including bacterial blight, blast, and brown spot—cause annual yield losses of 10–30%, disproportionately affecting smallholder farmers who lack timely access to plant-pathology expertise; unreliable rural connectivity further limits cloud-based diagnostic tools. High-capacity convolutional neural networks deliver strong accuracy but are too large and slow for low-cost devices, and their opaque predictions undermine farmer trust. Objective: This study designs and evaluates RiceLeaf-Edge, an explainable and lightweight convolutional neural network for on-device rice-leaf disease detection that operates fully offline. Methods: Following Design Science Research methodology, a compact depthwise-separable student network was trained with knowledge distillation from a high-capacity teacher and compressed via INT8 post-training quantization; a Grad-CAM visual explanation module was integrated and evaluated on a rice-leaf dataset comprising five classes (healthy and four disease categories, n = 3,355 images). Results: RiceLeaf-Edge achieved 97.3% accuracy and 97.0% macro-F1—within 0.8 percentage points of the heavy baseline (98.1%) at only 8.9 MB and 34 ms on-device latency versus 92.4 MB and 164 ms for the heavy baseline. Explanations were faithful (insertion score 0.87; deletion score 0.18) with 92.6% symptom agreement. Conclusion: The framework demonstrates that trustworthy, deployable agricultural diagnosis is achievable at the edge on commodity hardware, offering a transferable recipe for edge AI in low-connectivity settings.
Co-Authors Meritorious Autonomous Rafi Farizki Rafi Farizki Vo Hung Cuong