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Ahmad Rifa'i
Universitas Semarang, Semarang
Computer Science & IT

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Analisis Komparasi Kinerja LSTM dan CNN dalam Deteksi Spam Email Berbasis Deep learning Maugy Al Kautsar; Galet Guntoro Setiaji; Ahmad Rifa'i
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.572

Abstract

Spam email remains a critical issue in digital communication due to its potential misuse in spreading false information and online fraud. This study aims to evaluate and compare the performance of two deep learning models Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for text-based spam email classification. The dataset used in this study was obtained from Kaggle and contains 5,572 labeled email entries categorized as spam and non-spam. The preprocessing stage included labeling, cleaning, lowercasing (casefolding), tokenization, stopword removal, and stemming. The data was split into training and testing sets with a 70:30 ratio. Both models were trained using the same configuration and evaluated using accuracy, loss, confusion matrix, and F1-score metrics. The results indicate that the LSTM model achieved the highest accuracy of 98.72% with a loss value of 0.0377, outperforming the CNN model, which achieved 87.78% accuracy and a loss of 0.3659. Based on these findings, LSTM demonstrated superior performance in detecting spam emails using text-based input. This research is expected to serve as a reference for developing more accurate and effective spam detection systems in the future.
Peningkatan Transparansi Tata Kelola Keuangan Desa melalui Pengembangan Sistem Berbasis Web dengan Menerapkan Model Agile Septiano Cepeda Da Costa; Galet Guntoro Setiaji; Ahmad Rifa'i
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.821

Abstract

This research discusses village financial management as a primary aspect in supporting village progress and community welfare. Transparent and effective budget management is crucial for ensuring that village funds are utilized optimally for development programs. However, several obstacles persist, including delays in financial reporting and limited access to information for residents. The purpose of this research is to design and develop a web-based village financial information system using the Laravel framework and an Agile methodology to improve transparency, data accessibility, and the effectiveness of real-time financial reporting. The Agile method was selected for its iterative and collaborative development process, which is responsive to user needs. This approach allows both residents and village officials to participate actively in the development process, ensuring the resulting system serves as a viable solution to existing problems. Functional testing results using the Black Box method indicate that the system facilitates easier financial reporting, provides accurate data presentation, and supports accountability in village fund management. This system is expected to increase community participation in budget oversight and serve as a technological solution for the digitalization of village financial management. Ultimately, this system aims to make village budget management transparent, fast, and accessible to all residents at any time, thereby fostering community trust and engagement in village development.
Co-Authors Galet Guntoro Setiaji Maugy Al Kautsar Septiano Cepeda Da Costa