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All Journal Inspiration: Jurnal Teknologi Informasi dan Komunikasi Signal and Image Processing Letters
Nugroho H, Yabes Dwi
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Topic Modelling of Disaster Based on Indonesia Tweet Using Latent Dirichlet Allocation Nuryono, Aninditya Anggari; Iswanto, Iswanto; Ma'arif, Alfian; Putra, Rizal Kusuma; Nugroho H, Yabes Dwi; Hakim, Muhammad Iman Nur
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.132

Abstract

Twitter (now X) is a critical social media platform for disseminating information during crises. This study models disaster-related topics from Indonesian-language tweets using Latent Dirichlet Allocation (LDA). From a dataset of 8,718 tweets collected from official sources like BMKG and BNPB, we performed several preprocessing steps, including case folding, stop word removal, stemming, and normalization of slang and abbreviations. The optimal number of topics was determined using coherence scores, with the model achieving a peak coherence value of approximately 0.57. Keywords such as “banjir”, “kecelakaan”, “tanah longsor,” and others were used to collect data from Twitter accounts like "BMKG" (Meteorology, Climatology, and Geophysical Agency) and "BNPB" (National Disaster Management Agency). The results revealed that the most frequently discussed topics with high coherence values were “angin topan” “topan”, “virus corona”, “kecelakaan”, “tenggelam”, “badai”, “angin puting.” A word cloud was used to visualize these disaster-related topics.
Lightweight Deep Learning Approach Using 1D-CNN and Attention for Sequential Credit Card Fraud Detection Nugroho H, Yabes Dwi; Rahmawati, Aulia; Araz, Rezty Amalia; Nuryono, Aninditya Anggari
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 1 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v15i1.115

Abstract

Fraudulent activity in credit card transactions continues to be a pressing concern in the financial industry, primarily because transaction data is highly complex and heavily skewed toward legitimate cases. To address this issue, the present study proposes a hybrid deep learning framework that merges the strengths of a one-dimensional convolutional neural network (1D-CNN) with the selective capabilities of an attention mechanism. The performance of this enhanced model was rigorously compared with a conventional 1D-CNN, employing widely recognized evaluation metrics such as accuracy, precision, recall, and the F1-score. The experimental outcomes demonstrate that introducing the attention layer substantially improves the network’s ability to recognize critical temporal dependencies in transaction sequences. As a result, the model achieved exceptional performance levels, with an accuracy of 98%, precision of 97%, recall of 98%, and an F1-score of 98%. These findings provide strong evidence of the superiority of the attention-based approach, highlighting its effectiveness in producing more reliable and resilient fraud detection systems. Beyond the algorithmic gains, the research contributes a practical foundation for real-time applications in financial security, enabling institutions to curtail potential losses, reinforce public confidence in digital payment services, and enhance the efficiency of day-to-day operations.
Co-Authors Alfian Ma’arif Araz, Rezty Amalia Aulia Rahmawati, Aulia Iswanto Iswanto Muhammad Iman Nur Hakim Nuryono, Aninditya Anggari Putra, Rizal Kusuma