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All Journal Jurnal Sistem Informasi dan Informatika (SIMIKA) Smatika Jurnal : STIKI Informatika Jurnal
Stefanus Eko Prasetyo
International University of Batam
Computer Science & IT Control & Systems Engineering

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The Cyber Threat Landscape in Indonesia: Attacks and Security System Analysis Hendi Sama; Stefanie; Stefanus Eko Prasetyo
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2200

Abstract

Cybersecurity in learning systems has become increasingly important as educational institutions rely more heavily on digital platforms such as learning management systems, online assessments, and cloud-based academic services. This rapid digital transformation exposes learning environments to sophisticated cyber threats that may disrupt academic activities and compromise sensitive information. However, many institutions still lack a clear understanding of how users perceive cyber risks and how these perceptions influence the effectiveness of cybersecurity systems. Currently, there is a significant research gap regarding empirical evidence that links user behavioral psychology with technical security outcomes in the Indonesian educational context. This study aims to empirically analyze the relationship between cyber awareness, perceived impact of cyber attacks, and perceived effectiveness of cybersecurity systems in digital learning environments. A quantitative research approach was applied using data collected from 402 respondents. The data were analyzed through descriptive statistics, correlation analysis, regression analysis, and group comparison tests to examine variable relationships and demographic differences. The findings indicate that cyber awareness significantly and positively predicts perceived system effectiveness (β = 0.501, p < 0.001), demonstrating that higher awareness levels enhance overall cybersecurity performance. Conversely, the perceived impact of cyber attacks does not show a significant effect on system effectiveness, suggesting that awareness is more influential than threat perception alone. Additional results reveal gender-based differences in cyber incident experiences, while awareness levels remain similar. The practical implications emphasize the importance of cybersecurity awareness programs, digital safety education, and proactive defense strategies to strengthen protection in learning systems and improve institutional cybersecurity readiness.
AN END-TO-END CRNN AND CTC APPROACH FOR OFFLINE HANDWRITTEN CHINESE TEXT RECOGNITION Stefanus Eko Prasetyo; Jeffrey; Haeruddin
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 9 No. 2 (2026): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v9i2.4519

Abstract

Offline handwritten Chinese text recognition remains a challenging problem due to the large number of character classes, complex character structures, and high variability in writing styles. This paper proposes an end-to-end offline handwritten Chinese text recognition system based on a CNN–BiLSTM–CTC architecture. A convolutional neural network (CNN) is used to extract spatial features from handwritten text images, while a bidirectional long short-term memory (BiLSTM) network captures contextual dependencies between characters in both forward and backward directions. The Connectionist Temporal Classification (CTC) framework is applied to enable segmentation-free training and decoding of character sequences directly from input images. Experiments conducted on the CASIA-HWDB 2.2 offline handwritten Chinese text dataset demonstrate that the proposed approach achieves a Character Error Rate (CER) of 16,85%, corresponding to an accuracy of 83,15%, confirming the effectiveness of the CNN–BiLSTM–CTC framework for offline handwritten Chinese text recognition.
Co-Authors Haeruddin Hendi Sama Jeffrey Stefanie