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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA
Arya Adhyaksa Waskita
Universitas Pamulang, Tangerang Selatan
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Perbandingan Gated Recurrent Unit dan Time Series Transformer untuk Prediksi Kabut Menggunakan Sliding Window Chandra Dwi Pratomo; Agung Budi Susanto; Arya Adhyaksa Waskita
TIN: Terapan Informatika Nusantara Vol 7 No 2 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i2.10687

Abstract

Fog is one of the most hazardous weather phenomena for aviation operations. Dense fog can reduce visibility to below 1,000 meters, potentially causing flight delays, cancellations, and even aviation incidents. To date, fog prediction, particularly at Budiarto Airport, still relies on manual analysis by weather forecasters, making it prone to subjectivity and delays in information delivery. This study proposes and compares two deep learning architectures: the Gated Recurrent Unit (GRU) as an efficient recurrent model, and Time Series Transformer (TST) based on self-attention as a state-of-the-art model for METAR (Meteorological Aerodrome Report) data-based fog event prediction. The METAR data is initially processed using a sliding window technique before becoming a ready-to-use dataset. The dataset comprises 153,838 METAR records from the Budiarto–Curug Meteorological Station spanning from September 2015 to February 2026, which were processed through a METAR code parsing pipeline, BMKG rule-based median imputation, Min-Max normalization, and the construction of a 9-1 sliding window dataset. Experimental results on the test data demonstrate that TST 9-1 delivers the best performance with a Root Mean Squared Error (RMSE) of 0.452427 and a three class classification accuracy (No Fog, Light Fog, Dense Fog) of 88.21%, significantly outperforming GRU 9-1, which achieved an RMSE of 0.883981 and an accuracy of 72.97%. The main novelty of this research lies in the comparative study of GRU and TST architectures for METAR based fog prediction at airports, combined with a sliding window technique and the conversion of visibility regression into a multi class classification of fog events. This research contributes a fog prediction modeling framework capable of processing time-series data sequentially and more effectively, which can serve as a foundation for the development of an accurate, automated early warning system for fog events in airport environments.
Optimasi Akurasi Jawaban Aplikasi Chatbot Layanan Pelanggan dengan Metode RAGRetrieval-Augmented Generation Dhaman Dhaman; Sajarwo Anggai; Arya Adhyaksa Waskita
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.8048

Abstract

This research addresses the issue of low answer accuracy in chatbot systems based on Large Language Models (LLMs) when responding to questions derived from customer service documents. To overcome this problem, the Retrieval-Augmented Generation (RAG) method is applied to improve the quality of responses by adding relevant context from external documents. Three LLM models used in this study are LLaMA3.1 8B, LLaMA3.2 1B, and LLaMA3.2 3B from Meta AI. Evaluation is conducted using automatic ROUGE metrics (ROUGE-1, ROUGE-2, and ROUGE-L) and manual human evaluation assessing accuracy, relevance, and hallucination. This research contributes to the development of more reliable question-answering systems based on LLMs enhanced with external contextual documents related to customer service information. The results show a significant improvement across all models after applying the RAG method. ROUGE F1-scores increased consistently, with Llama3.1:8b showing the highest gain (from 0.12 to 0.58 on ROUGE-1). Human evaluation also confirmed improvements in accuracy (up to +2.73 points) and reductions in hallucination (up to −2.63 points). These improvements were evident not only in larger models but also in smaller ones, indicating that the benefits of RAG are not dependent on model size. In conclusion, RAG is highly effective in enhancing the accuracy and reliability of chatbot responses, especially in document-based question-answering scenarios. By leveraging contextual information from external documents, the system produces more factual, relevant, and hallucination-free responses. RAG has proven to be an effective approach for enhancing the response quality of LLM, including those with smaller parameter sizes.
Co-Authors Agung Budi Susanto Chandra Dwi Pratomo Dhaman Dhaman Sajarwo Anggai