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HENGKI TAMANDO
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hengki_tamando@yahoo.com
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jbst@iocscience.org
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INDONESIA
Journal Basic Science and Technology
Published by Institute of Computer Science
ISSN : 20898185     EISSN : 28081498     DOI : https://doi.org/10.35335/jbst
Core Subject : Science, Engineering,
Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology Mechanical Engineering
This journal is devoted to identifying, mapping, understanding, and interpreting new trends and patterns in the development of science & technology especially in developing countries in this world. The journal endeavors to highlight science & technology development from different perspectives. The aim is to promote broader dissemination of the results of scholarly endeavors into a broader subject of knowledge and practices and to establish an effective means of communication among academic and research institutions, policymakers, government agencies, and persons concerned with the complex issue of science & technology development
Arjuna Subject : Umum - Umum
Articles 5 Documents
 1 
Search results for , issue "Vol 14 No 2 (2025): June: Basic Science and Technology" : 5 Documents clear
Robust Salt Body Segmentation in Seismic Datasets Using a Multi-Scale Deep Neural Framework sunkara, santhi priya; Sharma, Deepti
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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Abstract

Accurate segmentation of salt bodies in seismic images is a critical task in subsurface exploration, as salt structures often act as traps for hydrocarbons. Traditional manual and rule-based methods are time-consuming and prone to inaccuracies due to the complex morphology and low contrast of salt boundaries. In this study, we propose a robust multi-scale deep neural network framework designed to enhance salt body segmentation in seismic datasets. The framework leverages a multi-scale encoder-decoder architecture integrated with Atrous Spatial Pyramid Pooling (ASPP) and attention mechanisms to effectively capture both global context and fine-grained structural details. Evaluated on the publicly available TGS Salt Identification Challenge dataset, the proposed model outperforms several state-of-the-art baselines in terms of Intersection over Union (IoU), Dice coefficient, and overall segmentation accuracy. The results demonstrate the framework’s effectiveness in accurately delineating salt regions, even in the presence of noisy or ambiguous seismic data, offering a reliable tool for aiding geophysical interpretation and exploration.
Vulnerability Analysis and Mitigation Strategies of DDoS Attacks on Cloud Infrastructure Sihotang, Hengki Tamando; Alrasyid, Wildan; Delano, Aldrich; Jacob, Halburt; Rizky, Galih Prakoso
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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Abstract

As cloud computing becomes increasingly central to modern digital operations, it has also become a primary target for Distributed Denial of Service (DDoS) attacks. This research investigates the major vulnerabilities within cloud infrastructure that are commonly exploited by DDoS attackers and evaluates the effectiveness of various mitigation strategies. The study employs a mixed-methods approach, combining vulnerability assessment, simulated attack scenarios, and comparative performance analysis of traditional and advanced defense mechanisms, including rate limiting, Intrusion Detection Systems (IDS), Software-Defined Networking (SDN), and machine learning-based anomaly detection. The findings reveal that key weaknesses in cloud systems such as shared resource models, unsecured APIs, and auto-scaling configurations can be leveraged to disrupt services or cause economic damage. The comparative evaluation highlights the limitations of conventional tools in handling sophisticated or large-scale attacks, while also showcasing the superior adaptability of SDN and AI-driven techniques under dynamic threat conditions. This research contributes to the field of cloud security by offering a comprehensive understanding of DDoS threat vectors, identifying effective defense combinations, and providing practical recommendations for strengthening the security posture of cloud systems. The study emphasizes the importance of proactive, layered, and intelligent defense frameworks to enhance the resilience of cloud-based infrastructures against evolving DDoS threats.
AI-Based Sentiment Analysis of Social Media to Detect Public Opinion on Government Policies Rizky, Galih Prakoso; Alrasyid, Wildan
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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Abstract

In the digital age, social media has become a powerful platform for public expression and discourse, offering governments a real-time window into citizen sentiment. This research explores the application of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) techniques, to analyze public sentiment on social media in response to government policies. Using data primarily sourced from Twitter, the study applies a BERT-based sentiment analysis model to classify public reactions into positive, negative, and neutral categories. The model achieved high performance with an accuracy of 89.2%, precision of 88.6%, and recall of 87.9%, outperforming traditional classifiers. Sentiment was analyzed across three key policy areas: fuel subsidy removal, education curriculum reform, and COVID-19 vaccination programs. Results indicate significant variations in public sentiment based on policy type, timing, and inferred demographic factors. A real-time sentiment analysis dashboard was developed to support policymakers in monitoring public opinion trends and improving communication strategies. This study demonstrates the potential of AI-driven sentiment analysis as a tool for enhancing data-informed governance, public engagement, and policy responsiveness.
Enhancing Ransomware Detection and Investigation through Digital Forensic Machine Learning Analysis Fadhil, Dzulfiqar; Taufiqurrahman, Taufiqurrahman
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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Abstract

Ransomware has become one of the most pervasive and damaging forms of cyber threats, targeting individuals, organizations, and critical infrastructures. Traditional digital forensic methods, while effective, are often limited by the speed and scale required to analyze modern ransomware attacks. This research explores the integration of machine learning techniques into digital forensic analysis to enhance the detection, classification, and investigation of ransomware. Using a controlled virtual environment, ransomware samples were executed and monitored to extract forensic artifacts from system logs, memory, and network activity. Features such as file entropy, API call behavior, and command-and-control (C2) communication patterns were analyzed. Machine learning models, particularly Random Forest and Convolutional Neural Networks (CNNs), were trained to identify ransomware behaviors with high accuracy. The Random Forest model achieved a detection accuracy of 96.4%, with strong precision and recall scores. The study also developed an automated forensic framework capable of real-time incident response and evidence extraction. Compared to previous research, this study offers improved generalization to unknown ransomware variants and faster forensic processing. The findings highlight the potential of digital forensic machine analysis as a robust solution for modern ransomware defense and investigation.
Effect of Process Parameter Variations in TIG Welding on Joint Strength Ishaan, Rohan; Siddhartha, Aswin
Journal Basic Science and Technology Vol 14 No 2 (2025): June: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

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Abstract

This research investigates the effect of process parameter variations in Tungsten Inert Gas (TIG) welding on joint strength, focusing on the key parameters that influence weld quality and mechanical properties. The study explores the relationship between welding current, welding speed, arc length, shielding gas flow rate, and electrode type, and their impact on the tensile strength, fatigue resistance, and overall integrity of welded joints. Through a series of controlled experiments, various combinations of these parameters were tested to identify optimal settings for achieving the strongest, most reliable welds. The results indicate that welding current and speed have the most significant influence on joint strength, with proper shielding gas flow and arc length playing critical roles in minimizing defects and ensuring a stable weld pool. The research also highlights the importance of adjusting welding parameters based on material type and application to achieve maximum joint strength while improving efficiency and reducing material waste. The findings provide valuable insights for improving welding practices, optimizing process parameters, and enhancing the quality and durability of welded structures in industrial applications. This study contributes to the development of more effective welding procedures, offering practical solutions for industries relying on high-quality welds for structural integrity and safety.

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