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International Journal Science and Technology (IJST)
Published by Asosiasi Dosen Muda Indonesia
ISSN : 28287223     EISSN : 28287045     DOI : https://doi.org/10.56127/ijst.v1i2
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
International Journal Science and Technology (IJST) is a scientific journal that presents original articles about research knowledge and information or the latest research and development applications in the field of technology. The scope of the IJST Journal covers the fields of Informatics, Mechanical Engineering, Electrical Engineering, Information Systems and Industrial Engineering. This journal is a means of publication and a place to share research and development work in the field of technology.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 132 Documents
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Ethical and Responsible AI in the Age of Adversarial Diffusion Models: Challenges, Risks, and Mitigation Strategies Tejaskumar Pujari; Anshul Goel; Deepak Kejriwal
International Journal Science and Technology Vol. 1 No. 3 (2022): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v1i3.1963

Abstract

The rapid pace of diffusion models in generative AI has completely restructured many fields, particularly with respect to image synthesis, video generation, and creative data enhancement. However, promising developments remain tinged with ethical questions in view of diffusion-based model dual-use. By misusing these models, purveyors could think up deepfaked videos, unpredictable forms of misinformation, instead outing cyber warfare-related attacks over the Internet, therefore aggravating societal vulnerabilities. This paper explores and analyzes these potential ethical risks and adversarial threats of diffusion-based artificial intelligence technologies. We lay out the basis for good AI-the notion of fair, accountable, transparent, and robust (FATR) systems-discussing efforts underway to mitigate these ethical risks through watermarking, model alignment, and regulatory mechanisms. Thus, from the dialogue with ethical viewpoints, also touching upon cybersecurity, military policy, or governance, we present a conceptual model to encapsulate probable ethical considerations in the development and deployment of diffusion models. Human-centered values need to be advanced by a proactive convergent bonding among researchers, decision-makers, and civil society players during the strengthening of a tributary of generative AI's power.
Adversarial AI in Social Engineering Attacks: Large- Scale Detection and Automated Counter measures Anil Kumar Pakina; Deepak Kejriwal; Tejaskumar Dattatray Pujari
International Journal Science and Technology Vol. 4 No. 1 (2025): March: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i1.1964

Abstract

Social engineering attacks using AI-generated deepfake information leverage rare cybersecurity threat hunting. Conventional phishing detection and fraud prevention systems are failing to catch detection errors due to AI-generated social engineering in email, voice, and video content. To mitigate the increased risk of AI-driven social engineering attacks, a new multi-modal AI defense framework, incorporating Transfer Learning through pre-trained language models, deep fake sound analysis, and behavior-analysis systems capable of pinpointing AI generated social engineering attack, is presented. Benefiting from the utilization of state-of-the-art deepfake voice recognition systems and behavior anomaly detector system (BADS) base for cash withdrawals, the discoverers show that the defense mechanism achieves unprecedented detection accuracy with the least incidence of false positives. This brings about the necessity for fraud prevention augmenting AI measures and provision of automated protection mitigating adversarial social engineering within the enterprise security and financial transaction systems.
Ensuring Responsible AI: The Role of Supervised Fine-Tuning (SFT) in Upholding Integrity and Privacy Regulations Tejaskumar Pujari; Anshul Goel; Ashwin Sharma
International Journal Science and Technology Vol. 3 No. 3 (2024): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v3i3.1968

Abstract

AI is increasingly used in high-stakes fields such as healthcare, finance, education, and public governance, requiring systems that uphold fairness, accountability, transparency, and privacy. This paper highlights the critical role of Supervised Fine-Tuning (SFT) in aligning large AI models with ethical principles and regulatory frameworks like the GDPR and EU AI Act. The interdisciplinary approach combines regulatory analysis, technical research, and case studies. It proposes integrating privacy-preserving techniques—differential privacy, secure multiparty computation, and federated learning—with SFT during deployment. The research also advocates incorporating Human-in-the-Loop (HITL) and Explainable AI (XAI) to ensure ongoing oversight and interpretability. SFT is positioned not only as a technical method but as a core enabler of responsible AI governance and public trust.
Augmented Reality in E-Commerce: A Conceptual Exploration of Product Uncertainty and Customer Engagement Hari Setiabudi Husni; Michelle Alicia Lynch; William Leo Walangitan; Aaron Kennedy
International Journal Science and Technology Vol. 4 No. 1 (2025): March: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.2077

Abstract

The growth of e-commerce has redefined consumer expectations, yet it continues to grapple with key limitations such as product uncertainty and insufficient customer engagement. Augmented Reality (AR) has emerged as a promising technological innovation, offering immersive product visualization that enhances consumer interaction and confidence in online shopping. This study provides a conceptual exploration of AR’s role in mitigating product-related uncertainty while simultaneously promoting deeper customer engagement within e-commerce platforms. Employing a systematic literature-based approach, the study synthesizes findings from peer-reviewed research across marketing, human-computer interaction, and information systems. The analysis identifies how AR reduces different dimensions of uncertainty—such as appearance, fit, and functional ambiguity—and outlines the psychological mechanisms, including trust formation and perceived interactivity, that mediate user responses. Furthermore, the study highlights the conditions under which AR fosters cognitive and emotional engagement, with implications for interface design and strategic technology adoption. This conceptual synthesis offers both theoretical insights and practical guidelines for e-commerce platforms considering AR integration. It also proposes a research agenda to empirically validate the conceptual model and expand understanding of AR’s long-term impact on consumer behavior.
Predicting Defensive Formation Effectiveness in Football Using Random Forest and LSTM Models Nurdiyanto Yusuf
International Journal Science and Technology Vol. 4 No. 3 (2025): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i3.2271

Abstract

Defensive strategies are fundamental to football success, yet the evaluation of formation effectiveness often remains subjective. This study proposes a data-driven approach to predict the most effective defensive formations by integrating machine learning models. Using tracking-derived features from 150 professional European matches (2018–2023), Random Forest (RF) and Long Short-Term Memory (LSTM) models were applied to assess defensive outcomes. The results indicate that the 5-3-2 formation consistently achieved the highest predicted defensive success across direct, wing, and central attacks, followed by 4-4-2, while the 4-3-3 formation exhibited the weakest defensive stability. RF identified key static features such as line height, block width, and compactness, while LSTM captured temporal dynamics of coordinated player movements, yielding superior predictive performance. This study concludes that combining interpretable ensemble models with sequence-based neural networks offers a robust framework for tactical analysis. The findings provide actionable insights for coaches and analysts, supporting evidence-based decision-making in optimizing defensive strategies in modern football.
Testing Process of Density and Porosity of Aluminium 7075 and Aluminium 6061 Ariyanto, Ariyanto; Ahmad Risa Harfit; Ahcmad Fauzan; Tati Noviati; Bagus Rianto
International Journal Science and Technology Vol. 4 No. 2 (2025): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.1993

Abstract

Abstract: Aluminium is a lightweight metal with good corrosion resistance and electrical conductivity properties. Testing the density and porosity of aluminiumis important for its development in various applications, including household appliances, aircraft industry, automotive, ships, and construction. The purpose of this research is to determine the density and porosity values of aluminium 7075 and aluminium 6061 samples. Experimental methods, such as the Archimedes method and the Fujitsu FSR A 300g x 0.001g analytical balance, are used to collect data and measure the mass density of the test specimens. The results show that aluminium 6061 has a higher density measurement value (3.302 g/cm3) compared to aluminium 7075 (2.144 g/cm3). The theoretical density calculation results also indicate that aluminium 7075 has a higher value (3.26 g/cm3) compared to aluminium 6061 (3.18 g/cm3). Additionally, aluminium 7075 has a higher porosity calculation value (34.2%) compared to aluminium 6061 (27.6%).
Analysis of Land Use Patterns in the Kampung Minang Tourism Village, Nagari Sumpur, Based on Geographic Information Systems (GIS) Azis, Tengku Abdillah; Sitompul, Delon A.L; Gultom, Maria Joito; Wijaya, Cindy Melissa; Syahfitri, Wiji; Tumiar Sidauruk
International Journal Science and Technology Vol. 4 No. 2 (2025): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.2107

Abstract

The method used is descriptive quantitative, with primary data collected through field observations and interviews, and secondary data obtained from drone imagery and spatial planning documents. The analysis involved land classification, thematic mapping, and Nearest Neighbor Analysis (NNA). The results show that land use is dominated by sapodilla plantations (48.89%) and rice fields (33.86%), reflecting the village’s agrarian character. Regional facilities are evenly distributed, supporting educational, health, and cultural functions. The NNA indicates a clustered settlement pattern, with a Nearest Neighbor Ratio of 0.329755 and a z-score of -28.470036, signifying concentrated residential areas due to geographical factors and accessibility. These findings emphasize the importance of spatial planning in sustainable tourism village development.
Development of a Dual-Leaf Automatic Door Prototype Based on Arduino using HC-SR04 Ultrasonic Sensor and Servo Motor Syaeful Ilman; Arifin, Tri Nur; Wahyuningsih, erfiana
International Journal Science and Technology Vol. 4 No. 2 (2025): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.2133

Abstract

This research aims to design and implement a two-leaf automatic door sytem that opens towards the inside by utilizing an arduino microcontroller, HC-SR04 ultrasonic sensor and servo motor. The test results show that the system can work responsively and stably with a good level of detection accuracy and servo movement symmetry. This system has potential to be implemented on a small scale in residential homes, laboratories, or other semi-automatic public spased.
Dynamics of Organic Open Space : Transformation Functions and Activities on the Nunbaun Sabu Coast , Kupang City Messakh, Jeni; Yohanes A. Luwu
International Journal Science and Technology Vol. 4 No. 2 (2025): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.2146

Abstract

Public open spaces (POS) in coastal areas often develop organically without formal planning, yet play a critical role in the social life of urban communities. This study examines the transformation of the organic public open space at Nunbaun Sabu Beach, Kupang City, Indonesia. The aim of this research is to identify the factors driving the functional transformation of this coastal space and the patterns of social activities occurring within it. A qualitative approach was adopted, using case study methodology, including field observations and in-depth interviews. The findings indicate that the space has shifted from parking lot for churchgoers to a public social space for recreation and social interactions. The transformation is largely driven by the construction of a coastal defense wall (500 meters long), which provided a sense of safety and enhanced the area’s attractiveness for social activities. The beautiful sunset view further entices visitors to engage in activities such as resting, gathering, and enjoying the natural scenery. This study also emphasizes how physical infrastructure, such as the coastal defense wall and the sunset view, plays a critical role in the transformation of this organic public open space, as framed by Michel de Certeau’s theory of space manipulation and Lefebvre’s theory of the production of space. The findings offer valuable insights for urban planning that is more responsive to organically developed public spaces.
Brain Tumor Detection using Deep Learning Sudha, Ms. K; Latha Maheswari, T.; M, Harish; Chandini, Shaik; MS, Jishnu
International Journal Science and Technology Vol. 4 No. 2 (2025): July: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i2.2147

Abstract

Brain tumor detection using deep learning has emerged as a crucial approach to improving early diagnosis and treatment planning. This project presents a novel hybrid deep learning model based on the ShuffleNet architecture to enhance the accuracy and efficiency of brain tumor detection from medical images. Traditional machine learning (ML) models rely on hand- crafted features, which are often time-consuming and less effective. Deep learning, on the other hand, automates feature extraction, improving detection accuracy and reliability. The proposed system leverages the ShuffleNet framework, known for its lightweight and high-performance characteristics, making it well-suited for real- time applications. To further enhance the model’s capability, we modified ShuffleNet by removing its last five layers and replacing them with 15 newly designed layers that increase expressiveness and feature extraction capacity. Additionally, we integrated a leaky ReLU activation function in the feature map to mitigate the vanishing gradient problem and improve model generalization. These enhancements result in superior feature representation and higher classification accuracy for brain tumor pathology detection. The dataset used for model training comprises MRI scans labeled with different tumor types. Preprocessing techniques such as normalization, augmentation, and contrast enhancement are applied to ensure robust training. The modified ShuffleNet model demonstrates higher precision, recall, and F1-score compared to traditional CNN-based models, while maintaining computational efficiency. This system can be deployed in real-time clinical settings to assist radiologists in early tumor detection, reducing human error and enhancing diagnostic speed. The integration of deep learning into medical imaging represents a significant step toward automated, accurate, and efficient brain tumor detection, ultimately improving patient outcomes.

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