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SISTEM KETERTELUSURAN ELEKTRONIK UNTUK PENGUATAN PERTANIAN ORGANIK PARTICIPATORY GUARANTEE SYSTEM [ELECTRONIC TRACEABILITY SYSTEM FOR STRENGTHENING ORGANIC FARMING PARTICIPATORY GUARANTEE SYSTEM] Priantoro, Akhmad Unggul; Prihartono, Agung; Dragon, Bullion
Al-Ihtiram: Multidisciplinary Journal of Counseling and Social Research Vol 3, No 1 (2024): Mei
Publisher : Perkumpulan Ahli Bimbingan dan Konseling Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59027/al-ihtiram.v3i1.780

Abstract

The Participatory Guarantee System (PGS) is a community-based effort to ensure the quality of organic agriculture produces, supporting organic farmers as an alternative to organic certification by Organic Certification Bodies (LSO), which is very expensive and time-consuming. PGS is an internationally recognized practice by IFOAM (International Federation of Organic Agriculture Movements) to provide quality assurance to consumers of organic agricultural produces. In practice, PGS relies on manual record-keeping and trust among the community members, with no strict sanctions against fraudulent activities. This makes PGS vulnerable to deviations that could harm all stakeholders. Therefore, an effective and efficient electronic traceability system is needed to support the development of PGS-based organic agriculture. This study proposes a design for an electronic traceability system that meets the needs of PGS.
GRAPH RAG UNTUK MEMAHAMI PERATURAN TENTANG PAJAK KENDARAAN BERMOTOR DI PROVINSI BANTEN [GRAPH RAG TO UNDERSTAND THE REGULATION ON MOTOR VEHICLE TAX IN BANTEN PROVINCE] Prihartono, Agung; Priantoro, Akhmad Unggul
Al-Ihtiram: Multidisciplinary Journal of Counseling and Social Research Vol 4, No 1 (2025): Mei
Publisher : Perkumpulan Ahli Bimbingan dan Konseling Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59027/al-ihtiram.v4i1.1049

Abstract

Local regulation documents (Perda) often have a complex structure and intricate language, making them difficult for the general public to understand. Large Language Models (LLM) offer the potential to simplify this information but carry the risk of generating inaccurate or "hallucinated" information. This research proposes and tests the Graph Retrieval-Augmented Generation (Graph-RAG) method as a solution to build an accurate and explainable question-answering system. Using the Provincial Regulation of Banten Number 1 of 2024 on Regional Taxes and Levies as a case study, we built a knowledge graph mapping key entities related to Motor Vehicle Tax (PKB)—such as rates, taxpayers, and exemptions—along with their relationships. The results of the trial show that the Graph-RAG system can answer specific questions about the base rate of PKB, progressive rates, and excluded objects with 100% accuracy, while also providing direct citations to the relevant articles and paragraphs in the regulation. This method has proven effective in presenting complex regulatory information reliably and verifiably, demonstrating its potential as a tool to assist public administration and citizen information services.