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All Journal Journal of Business, Social and Technology Jurnal Ragam Pengabdian
Ivan Michael Siregar
Institut Teknologi Harapan Bangsa
Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Agnostic Multi-Source Retrieval-Augmented Generation for Documents and Database Question Answering Krisna Dwi Setya Adi; Ivan Michael Siregar
Jurnal Ragam Pengabdian Vol. 3 No. 2 (2026): Mei-Agustus, Sustainable Development Goals (SDGs): Multidisciplinary Perspectiv
Publisher : Lembaga Teewan Journal Solutions

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62710/qw0ytn73

Abstract

Key personnel turnover creates knowledge gaps in document-based service organizations, where information is distributed across technical specifications, operational databases, and team discussions. This study develops a multi-source Retrieval-Augmented Generation (RAG) based Question Answering (QA) system that automatically integrates heterogeneous knowledge sources through a unified source parameter. Using the Adapter Pattern, the system converts PDF/TXT documents and PostgreSQL tables into a common representation, builds a FAISS vector index, retrieves relevant context, and generates grounded answers with Gemini 2.5 Flash. Evaluation employs eight metrics and three composite scores: Knowledge Transfer Effectiveness (KTE), Multi-Source Retrieval Score (MSRS), and Answer Quality Index (AQI). Experiments were conducted on the BOND_SYS dataset using 25 Indonesian questions covering specification documents, an 8-table PostgreSQL database, and 908 developer discussion messages. Results show perfect retrieval performance (Precision@K = 1.000; MRR = 1.000) across all scenarios. The full hybrid configuration achieves the highest Overall score (0.373), while Scenario C records the highest MSRS (0.825). Scenario E obtains ROUGE-L = 0.181 and BLEU-1 = 0.196 using five manually curated reference answers. Two baseline comparisons further support this contribution: a zero-shot LLM without retrieval correctly answered only 8% of questions, while a BM25 keyword-search baseline, competitive on single-source scenarios, was outperformed on cross-referencing tasks, underscoring the added value of dense multi-source retrieval.  The findings demonstrate that integrating formal documents, structured databases, and discussion logs enhances knowledge transfer and question answering for organizational support and employee onboarding.
Online Reviews in SKU-level Demand Forecasting Using Global Cross-Learning LSTM Ivan Michael Siregar; Irfan Muhammad Fauzi
Journal of Business, Social and Technology Vol. 7 No. 4 (2026): Journal of Business, Social and Technology
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/bustechno.v7i4.755

Abstract

Background: Daily SKU-level demand forecasting is challenging in zero-inflated e-commerce data because absolute-error optimization can favor zero forecasts and fail to capture demand activation. Objective: This study evaluates a Global Cross-Learning LSTM that combines transaction history, structural sparsity markers, and online-review signals as behavioral proxies. Methods: Computational forecasting experiments used daily SKU-level transaction and review data from the Amazon and Olist datasets. Four ablation configurations (M0–M3) were compared with SES, Croston, and SBA using MASE, RMSSE, and AMSE. The LSTM used a 30-day lookback window, 50 units, the Adam optimizer, a batch size of 128, 20 epochs, and Huber loss. Paired differences across 22 entities were assessed using the Wilcoxon signed-rank test. Results: Across the full timeline, M0 obtained the lowest MASE (0.4243), reflecting the advantage of zero forecasts on inactive days. During activation windows, however, M3 reduced AMSE from 0.7769 to 0.6195 and produced a statistically significant improvement in RMSSE (p < 0.05). Global pooling also supported forecasting for cold-start and lumpy-demand items. Conclusion: The findings support Proxy Theory by indicating that review volume and valence can provide leading information before sufficient transaction data accumulate. Theoretically, the study links behavioral proxies with global representation learning for intermittent demand. Practically, the model can inform replenishment and safety-stock decisions by reducing underforecasting during demand activation. The novelty lies in jointly evaluating online-review proxies and global cross-learning at the SKU level under lifecycle-specific conditions.
Co-Authors Irfan Muhammad Fauzi Krisna Dwi Setya Adi