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All Journal Journal of Computer Science Application and Engineering Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK)
Anwar, Ican
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other

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Machine Learning Approaches for Detection of SQL Injection Attacks Anwar, Ican
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 1 (2025): JAFOTIK - February
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i1.50

Abstract

This study addresses the escalating cybersecurity challenges posed by SQL injection attacks in web applications and databases. This study aims to explore and evaluate the effectiveness of machine learning techniques in detecting SQL injection attacks, providing insights into the current state of research. The research involves collecting a relevant dataset of normal and malicious SQL queries, training and testing machine learning models (Support Vector Machines, Deep Neural Networks, and Random Forest). The Deep Neural Networks model stand out with the highest accuracy 0.95 and recall 0.98, indicating its robust capability to correctly classify instances of SQL Injection Attacks. The study contributes valuable insights into the current landscape of machine learning applications for SQL injection detection, providing a foundation for further exploration and analysis in this critical cybersecurity domain.
Shaping the Future of Agriculture with Intelligent Systems Anwar, Ican
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 2 (2025): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v3i2.72

Abstract

This study explores the implementation of intelligent systems in agriculture as a solution to longstanding challenges such as inefficient resource use, disease management, and low productivity. By integrating technologies like Artificial Intelligence (AI), the Internet of Things (IoT), computer vision, and robotics, intelligent systems enable precision farming that optimizes water usage, enhances crop monitoring, automates labor-intensive tasks, and improves overall decision-making. Real-world applications such as CropX and NetBeat for smart irrigation, Plantix and Nuru for disease detection, and John Deere’s autonomous tractors for automated fieldwork demonstrate the tangible benefits of these innovations. Additionally, tools like Moocall and Ida offer real-time livestock health monitoring, while platforms such as AgriPredict and aWhere provide data-driven decision support to farmers globally. A sample block diagram of a smart irrigation system, supported by a simplified calculation, illustrates the practical operation and measurable benefits of such systems. The study emphasizes the potential of intelligent agriculture not only to boost productivity and sustainability but also to make advanced tools more accessible to small and medium-scale farmers. Future advancements should aim to enhance integration, affordability, and ease of use, ultimately supporting the transition to more resilient and efficient agricultural practices in the face of growing global food demands.
Bridging Educational Inequalities with Future AI in Advancing SDG 4 Anwar, Ican
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 3 No. 2 (2025): JAFOTIK - August
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v3i2.82

Abstract

Educational inequality remains a persistent challenge in many developing contexts, where limited resources, large class sizes, and high dropout rates prevent students from achieving their full potential. This study aims to explore how future applications of Artificial Intelligence (AI) can bridge these gaps and support the achievement of Sustainable Development Goal 4 (SDG 4) on quality education. The research adopts a mixed-methods approach, combining case study analysis of AI-driven initiatives with scenario-based calculations of potential benefits in time, cost, and student reach. By examining areas such as AI tutoring, automated grading, predictive dropout interventions, and personalized learning, the study highlights both the opportunities and limitations of AI in education. The contribution of this work lies in proposing a practical framework that illustrates how AI can reduce disparities, optimize resource use, and enhance inclusivity, ultimately offering a pathway toward more equitable and sustainable education systems.
From Traditional to Intelligent Agriculture: A Vision for the Future Anwar, Ican
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 4 No. 1 (2026): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v4i1.94

Abstract

The transition from traditional agriculture to intelligent, data-driven farming systems is increasingly critical for addressing challenges related to climate change, resource limitations, and food security. This study presents a comprehensive framework for intelligent agriculture by integrating Internet of Things technologies, machine learning techniques, and decision support systems to enhance agricultural productivity and sustainability. The proposed approach follows a structured methodology involving data acquisition, preprocessing, feature selection, intelligent modeling, and performance evaluation. Experimental results indicate that intelligent agriculture improves water-use efficiency by approximately 28%, reduces fertilizer usage by 22%, and enhances crop yield prediction accuracy from 62% to 88% when compared with traditional farming practices. Early pest and disease detection capabilities are improved by nearly 35%, enabling timely intervention and reduced crop losses. These findings demonstrate that intelligent agriculture significantly outperforms conventional methods while promoting sustainable resource management. Despite challenges related to infrastructure and adoption, the study confirms that intelligent agriculture represents a promising and resilient solution for future agricultural systems.
The Impact of Intelligent Agriculture on Sustainability and Food Security Anwar, Ican
Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) Vol. 4 No. 1 (2026): JAFOTIK - February
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/jafotik.v4i1.98

Abstract

Agricultural systems face increasing challenges related to resource depletion, climate variability, and food insecurity, requiring innovative and sustainable solutions. This study examines the impact of intelligent agriculture on sustainability performance and food security outcomes. A quantitative comparative design was employed, involving several farms categorized into intelligent agriculture adopters and conventional farmers. Data were analyzed using a sliding time-window approach, Structural Equation Modeling (SEM), and predictive machine learning techniques to evaluate direct and mediated effects. The findings reveal that intelligent agriculture significantly improves yield (33% increase), reduces water and fertilizer use (approximately 25%), and decreases production variability by more than 50%. Sustainability performance strongly mediates the relationship between intelligent agriculture and food security, resulting in a 27% improvement in the Food Security Index. These results confirm that intelligent agriculture enhances long-term agricultural resilience and resource efficiency, providing empirical support for policies promoting digital farming technologies to achieve sustainable food systems.
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