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All Journal Insyst : Journal of Intelligent System and Computation Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Samuel Situmeang
Information System Study Program, Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Toba, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Studi dan Analisis Hyperparameter Tuning IndoBERT Dalam Pendeteksian Berita Palsu Anugerah Simanjuntak; Rosni Lumbantoruan; Kartika Sianipar; Rut Gultom; Mario Simaremare; Samuel Situmeang; Erwin Panggabean
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 13 No 1: Februari 2024
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v13i1.8532

Abstract

The rapid advancement of communication technology has transformed how information is shared, but it has also brought concerns about the proliferation of false information. A recent report by the Ministry of Communication and Informatics in Indonesia revealed that around 800,000 websites were involved in spreading false information, underscoring the seriousness of the problem. To combat this issue, researchers have focused on developing techniques to detect and combat fake news. This research centers on using IndoBERT-base-p1 for fake news detection and aims to enhance its performance through three methods to tune the hyperparameter value of the model namely: Bayesian optimization, grid search, and random search. After comparing the outcomes of the three hyperparameter tuning methods, Bayesian Optimization emerged as the most effective approach. Achieving a precision of 88.79%, recall of 94.5%, and F1-score of 91.56% for the “fake” label, Bayesian Optimization outperformed the other hyperparameter tuning methods as well as the model using the fine-tuning hyperparameter value. These findings emphasize the importance of hyperparameter tuning in improving the accuracy of fake news detection models. Utilizing Bayesian Optimization and optimizing the specified hyperparameters, the model demonstrated superior performance in accurately identifying instances of fake news, providing a valuable tool in the ongoing battle against disinformation in the digital realm.
Automated Data Extraction from Aircraft Fuel Invoices Using PaddleOCR Hutapea, Reinaldy; Harwanto, Vanessa; Situmeang, Samuel
Intelligent System and Computation Vol 7 No 1 (2025): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v7i1.427

Abstract

This study presents an automated data extraction system for aircraft fuel invoice documents using PaddleOCR, a deep learning-based optical character recognition (OCR) technology. The system is designed to address the challenges of extracting information from complex and unstructured document formats, which traditionally require extensive manual processing. To enhance performance, the system incorporates image pre-processing techniques and artificial intelligence-based validation methods, ensuring higher accuracy in recognizing aviation-specific details such as flight identifiers and fuel data. Evaluation of the system demonstrates notable improvements in both time efficiency and accuracy. On average, documents can be processed in under 60 seconds with high recognition rates for clean, standard-quality inputs. While performance decreases with noisy or small-text documents, results indicate that accuracy can be further improved through deep learning-based denoising and training with aviation-specific datasets. The system also proves scalable, successfully handling up to 640 documents without compromising performance, suggesting its feasibility for large-scale industrial deployment. Beyond technical efficiency, the system delivers tangible economic benefits by reducing operational costs, minimizing transaction discrepancies, and enabling staff to focus on higher-value strategic tasks. Furthermore, it establishes a foundation for future enhancements, including integration with ERP systems, multilingual OCR support, and handwriting recognition. Overall, this research highlights the potential of PaddleOCR-based automation to significantly transform document management in the aviation industry and offers promising opportunities for adoption across other data-intensive sectors.
Co-Authors Anugerah Simanjuntak Erwin Panggabean Harwanto, Vanessa Hutapea, Reinaldy Kartika Sianipar Mario Simaremare Rosni Lumbantoruan Rut Gultom