Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Information Systems and Informatics Journal of Applied Engineering and Technological Science (JAETS)
Bagus Priambodo
Mercu Buana University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Integrating Fibonacci Retracement To Improve Accuracy of Time Series Prediction of Gold Prices Bagus Priambodo; Ruci Meiyanti; Samidi Samidi; Gushelmi Gushelmi; Rabiah Abdul Kadir; Azlina Ahmad
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 2 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i2.6073

Abstract

The prediction of gold prices is crucial for investors and policymakers due to its significant impact on global financial markets. Machine learning and deep learning have been used for predicting gold prices on time series data. This study employs MLR, SVM and CNN LSTM with Fibonacci retracement levels to forecast gold prices based on time series data. The experiment results demonstrate that combining Fibonacci retracement with model prediction significantly enhances predictive performance compared to prediction without Fibonacci. The use of Fibonacci levels has resulted in a higher R² score and lower RMSE score showing that Fibonacci levels influence the accuracy of gold price predictions and strengthen the overall reliability of gold price forecasts. The findings underscore the potential of combining machine learning models with technical analysis tools in financial forecasting. Integrating the Fibonacci retracement level offers valuable insights for market participants, enabling more informed investment decisions and effective risk management strategies.
Benchmarking Embedding Models for POJK Regulatory Retrieval in Indonesian Insurance Documents Daeng Ahmad Nurdin; Bambang Jokonowo; Bagus Priambodo
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1781

Abstract

Indonesian insurance regulations are clause-dense and terminologically specific, making manual retrieval of applicable provisions difficult. This study presents a retrieval-only benchmark comparing embedding and lexical methods on POJK 26/2025 and POJK 36/2025. The corpus contains 181 context chunks and 181 synthetic Indonesian queries, each assigned one primary relevant chunk. Queries were generated in JSON mode using Gemma 4 E4B via Ollama (ollama:gemma4:e4b), with malformed outputs retried and heuristic fallbacks prohibited. Evaluation comprised a four-model local dense comparison and a separate native BGE-M3/BM25 ablation using exact exhaustive ranking. The local comparison reports MRR and Hit rates, while the native ablation additionally reports NDCG@10, MAP@10, bootstrap confidence intervals, paired tests, and rank bins. Qwen3 Embedding achieved the highest local MRR (0.8075). Under a prespecified descriptive composite rule combining MRR, Hit rates, and measured latency, BGE-M3 ranked first. In the native ablation, BGE-M3 combined retrieval achieved NDCG@10 of 0.8810. A practitioner-validated subset audit and artifact-backed qualitative analysis supplemented the quantitative results. Findings are limited to this two-document benchmark and do not evaluate answer generation, citation correctness, or end-to-end RAG faithfulness.
Co-Authors Azlina Ahmad Bambang Jokonowo Daeng Ahmad Nurdin Gushelmi Rabiah Abdul Kadir Ruci Meiyanti Samidi Samidi