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All Journal Technologia: Jurnal Ilmiah Jurnal Pengembangan Sistem Informasi dan Informatika
Zain, M Syafrizal
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Computer Science & IT Social Sciences

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A GAN-Based Approach for Identifying Fake Accounts on Twitter Zain, M Syafrizal; Swengky, Better; Wisesa, Bradika Almandin; Putri, Vivin Mahat
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 1 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i1.2671

Abstract

The multiple security threats on the network make the need for robust security measures a major concern. The increasing presence of fake accounts and malicious actors on online platforms poses significant challenges, requiring sophisticated detection techniques to maintain network integrity. To address these issues, we propose a novel method for detecting fake accounts by leveraging Generative Adversarial Networks (GANs). By analyzing data extracted from platform APIs, our approach leverages the unique characteristics of GANs to improve the accuracy and efficiency of the detection process. In this study, we develop a GANs-based model specifically designed to detect fake accounts. The model is built through several key stages: first, we collect a comprehensive dataset, then perform data processing and preprocessing to make it suitable for machine learning applications. Next, the model is trained using various hyperparameters to optimize accuracy, thus learning the underlying patterns associated with fake accounts. After the training stage, the model is tested on previously unseen data to evaluate its generalization and performance in real-world scenarios. Experimental results show that our model achieves a threshold value of 0.0054779826. This value plays a crucial role in determining the accuracy of the detection system. The smaller the threshold value, the higher the model accuracy, as it shows a lower error rate in distinguishing between real and fake accounts. The ability of GANs-based models to adaptively learn from data during the training process contributes to high precision in detecting anomalies as well as minimizing false positives.
Prediksi Harga Saham Malindo Feedmill Tbk. (MAIN) Menggunakan Jaringan Saraf Tiruan Long Short-Term Memory (LSTM) Putri, Vivin Mahat; Zain, M Syafrizal; Darma, Satria Agus
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 6 No. 3 (2025): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v6i3.2789

Abstract

Stock price prediction presents a significant yet intricate challenge in financial forecasting, primarily due to volatile market dynamics and the nonlinear nature of data. This study investigates the efficacy of the Long Short-Term Memory (LSTM) model, a specialized Recurrent Neural Network (RNN), for forecasting the stock price of PT. Malindo Feedmill, Tbk., a publicly listed agribusiness firm on the Indonesia Stock Exchange. A five-year historical dataset of daily stock prices (open, high, low, close, volume) was utilized. Pre-processing involved data normalization, the application of a sliding window approach, and partitioning the data into training and testing subsets. The LSTM model was trained on sequential closing prices to effectively learn and model long-term dependencies inherent in stock price movements. The model's predictive performance was rigorously assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. Our results reveal that the LSTM model adeptly captures price trends, yielding a low MAPE of 3.47% on the test set. Comparative analysis against traditional models like linear regression confirms that LSTM provides superior accuracy and robustness, especially under volatile market conditions. This research highlights the significant potential of deep learning models in facilitating smarter investment decisions within the Indonesian agricultural sector. Subsequent work will aim to integrate sentiment analysis and macroeconomic indicators to further improve real-time predictive accuracy.
PENERAPAN METODE PROTOTYPE DALAM PENGEMBANGAN SISTEM PERPUSTAKAAN DIGITAL DI POLMAN BABEL affani, khairunnisa shabira; Rindri, Yang Agita; Zain, M Syafrizal
Technologia : Jurnal Ilmiah Vol 17, No 1 (2026): Technologia (Januari)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v17i1.20944

Abstract

Perkembangan teknologi digital telah membawa dampak besar terhadap inovasi di dunia pendidikan, salah satunya dalam penyediaan layanan perpustakaan. Di Polman Babel, penggunaan sistem perpustakaan digital masih menghadapi kendala karena belum terintegrasi secara menyeluruh dan belum mendukung pengelolaan informasi secara optimal. Untuk mengatasi hal tersebut, penelitian ini mengembangkan sistem perpustakaan digital menggunakan metode prototype, yang memungkinkan proses pengembangan dilakukan secara bertahap dengan melibatkan partisipasi pengguna pada setiap fase. Berdasarkan hasil black box testing, seluruh fungsi utama sistem berjalan sesuai rancangan, sedangkan hasil User Acceptance Testing (UAT) dari 31 responden menunjukkan tingkat penerimaan sebesar 88,4%. Dengan hasil tersebut, sistem perpustakaan digital ini dinyatakan layak digunakan serta berpotensi meningkatkan efisiensi dan akses layanan informasi di Polman Babel.
Co-Authors affani, khairunnisa shabira Better Swengky Bradika Almandin Wisesa Darma, Satria Agus Putri, Vivin Mahat Yang Agita Rindri