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All Journal JPM (Jurnal Pemberdayaan Masyarakat) Indonesian Journal of Data and Science
Abdul Rachman Manga'
Universitas Muslim Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Implementasi Sistem Layanan Mandiri untuk Efisiensi Administrasi Desa Biji Nangka Kabupaten Sinjai Purnawansyah; Rahma Puspitasari; Abdul Rachman Manga'; Herdianti Darwis; Sitti Nurhalimah
Jurnal Pemberdayaan Masyarakat Vol 11 No 1 (2026): Mei
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jpm.v11i1.13200

Abstract

This community service program aims to improve administrative efficiency in Biji Nangka Village, which previously used manual processes and was prone to delays, inconsistencies, and the risk of archive loss. This activity implemented a website-based self-service system and provided training to village officials on the use of key features such as digital letter management, automatic numbering, and electronic archive storage. A total of 17 participants participated in the training and all successfully operated the system. Evaluation results showed that the time to create letters was reduced from 10–15 minutes to 3–5 minutes. Furthermore, the results of the pre-test and post-test comparison showed a 9.412% increase in participant understanding, indicating the effectiveness of the training in improving the digital competence of village officials. Overall, this program has had a positive impact on improving the quality of administrative services and supporting the realization of digital-based village governance.
A Comparative Study of LSTM and CNN Models in SQL Injection Attack Detection Abdul Rachman Manga'; Wahyu Kadri Rahmat Suat Suat; Huzain Azis
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.460

Abstract

This research focuses on evaluating and comparing the performance of two deep learning architectures, namely Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), for detecting SQL Injection (SQLi) attacks on text-based query data. SQL Injection remains one of the most critical cybersecurity threats due to its ability to exploit vulnerabilities in user input validation and compromise the integrity, confidentiality, and availability of information systems. In this study, LSTM and CNN models were trained using a publicly available SQL Injection dataset containing malicious and benign SQL queries. Three train-test split scenarios were evaluated, namely 70:30, 80:20, and 90:10, while maintaining identical preprocessing procedures and experimental configurations. Text preprocessing was performed using tokenization, sequence transformation, padding, and embedding representation to convert SQL queries into numerical input suitable for deep learning models. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that the LSTM model achieved the best performance with an accuracy of 98.90% using the 80:20 train-test split scenario, while the CNN model achieved an accuracy of 97.90% under the same scenario. Although CNN is effective in extracting local textual patterns, LSTM demonstrated better capability in capturing sequential dependencies within SQL query structures. These findings indicate that deep learning approaches, particularly LSTM-based models, provide an effective and reliable solution for SQL Injection detection and support the development of adaptive cybersecurity systems
Co-Authors Herdianti Darwis Huzain Azis Purnawansyah Purnawansyah Rahma Puspitasari Sitti Nurhalimah Wahyu Kadri Rahmat Suat Suat