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All Journal G-Tech : Jurnal Teknologi Terapan
Mochamad Imamudin
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Impact of Preprocessing on Indonesian Extractive Summarization Using LexRank, TextRank, DivRank, and Cosine Similarity Andri Setiawan; Zainal Abidin; Mochamad Imamudin
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8306

Abstract

Extractive text summarization is a fundamental approach to tackle information overload, yet its quality is highly dependent on the pre-processing stage. Despite its crucial role, there is no consensus on the most optimal pre-processing scenario for the Indonesian language, which has a complex morphological structure. This study aims to fill this research gap by systematically analyzing the impact of seven pre-processing scenarios on four summarization methods: three graph-based methods (LexRank, TextRank, DivRank) and one topic-relevance method (Cosine Similarity against the title). Using a corpus of 3,000 Indonesian news articles and ROUGE evaluation metrics, the results show two key findings. First, the Cosine Similarity method significantly outperforms all graph-based methods, achieving the highest F1-Measure scores on ROUGE-1 (0.5073), ROUGE-2 (0.4018), and ROUGE-L (0.4574), which emphasizes the important role of the title in news texts. Second, a comprehensive pre-processing scenario involving Case Folding, Punctuation Removal, Tokenization, Normalization, Negation Handling, Stopword Removal and Stemming proves to be the most effective in improving the performance of all algorithms. These findings provide empirical evidence and practical recommendations that the combination of a title-relevancy approach with proper text normalization is the most effective strategy for optimizing extractive text summarization for the Indonesian language.
Probabilistic Forecasting of M≥5.0 Earthquakes in East Java: A 30-Day LSTM Approach Using Seismic Feature Data Nanang Yulianto; Totok Chamidy; Mochamad Imamudin; Suhartono Suhartono; Muhammad Ainul Yaqin
G-Tech: Jurnal Teknologi Terapan Vol 10 No 2 (2026): G-Tech, Vol. 10 No. 2 April 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i2.9504

Abstract

East Java is a seismically active region where short-term earthquake forecasting remains a critical yet challenging endeavor. While deterministic prediction is inherently unfeasible, probabilistic modeling offers a practical pathway for risk mitigation. This study develops a 30-day forward-window probabilistic forecasting model for M≥5.0 earthquakes in East Java using a Long Short-Term Memory (LSTM) network framed as a binary classification task. The model is trained on 25 years of seismic data (2001–2025) from BMKG Stasiun Geofisika Pasuruan. Twenty-five seismic features were rigorously selected through correlation analysis and data-leakage prevention protocols, while class imbalance was mitigated using adaptive loss weighting. The LSTM architecture was systematically optimized via sequential hyperparameter tuning and robust validation strategies. On a hold-out test set, the model achieved an AUC-ROC of 0.752, F1-score of 0.484, and recall of 0.673, indicating the model's capacity to detect impending seismic events with reasonable sensitivity. These results confirm that deep learning can effectively capture non-linear temporal patterns in seismic sequences. The primary contribution of this work is a validated, operationally ready probabilistic forecasting framework that can be integrated into regional earthquake monitoring systems, providing actionable lead time for disaster preparedness in East Java.
Co-Authors Ainul Yaqin Andri Setiawan Nanang Yulianto Suhartono Suhartono Totok Chamidy Zainal Abidin