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Journal for Technology and Science
Published by Antis Publisher
ISSN : -     EISSN : 30474337     DOI : https://doi.org/10.61796/ipteks.v1i1
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering
The Journal for Technology and Science published by Antis Publisher eISSN 3047-4337 is a scholarly journal that focuses on original research articles in natural science and technology relevant to industries and communities in developing countries. Released annually in March, August, and November, it is inclusive of scientists, researchers, educators, and scholars. The journals scope encompasses various topics addressing current challenges encountered by industries, governments, and communities in developing nations. With a commitment to advancing knowledge and fostering innovation, The Journal for Technology and Science welcomes submissions that contribute to the advancement of science, technology, and their applications in addressing societal needs and promoting sustainable development in emerging economies
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 82 Documents
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ANALYTICAL AND SIMULATED STUDY OF A BIPOLAR TRANSISTOR'S INPUT CHARACTERISTICS USING NI MULTISIM 14.2 PROGRAM Abdullah Z. Nuri
Journal for Technology and Science Vol. 3 No. 3 (2026): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v3i3.527

Abstract

Objective: This research presents a comprehensive study of the input characteristics of an NPN transistor in a common-emitter configuration using NI Multisim 14.2 at a constant output voltage across the transistor (VCE = 2 V). Method: The research methodology focuses on the increase in base current (Ig)   as a result of the voltage increase (VBE). The switching voltage, i.e., the critical operating point, is determined using the dynamic resistance equation. Results: The results obtained from NI Multisim 14.2 and the mathematical equations for a silicon diode that accepts or consumes a lower voltage (approximately 0.8 V) are crucial. Novelty: The most important conclusion of this research is that these calculations, relating to base current, emitter-base voltage, and a low dynamic resistance (7.58 Ω), form the basis for designing electronic circuits and the switching and amplification processes they undergo.
ANALYZE RISKS IN AWS/AZURE ENVIRONMENTS USING AI-DRIVEN MONITORING AND ANOMALY DETECTION Samira Alam Chowdhury; Mahbub Hasan; Fahmida Akter; Rabia Akter Sukhi; Abdul Rahman Mohammad
Journal for Technology and Science Vol. 1 No. 1 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i1.551

Abstract

Objective: In this research we explore the use of AI-driven monitoring and anomaly detection for analyzing risks in AWS and Azure environments, with an exploratory study on the Cloud Data Center Workload Dataset. The research will explore the following objectives: understanding the behavior of cloud workloads, detecting abnormal workload behavior, investigating the correlation between cloud monitoring parameters, and evaluating the risks of the operational environment of cloud infrastructure. Method: The methodology is based on an exploratory research approach with data preprocessing, exploratory data analysis, statistical analysis, and implementation of the monitoring framework using AI and R Studio. To interpret the behavior of the cloud, multiple visualizations were developed: workload distribution, resource utilization analysis, anomaly monitoring dashboard, correlation network, and operational risk dashboard. Results: The results indicate that changes in CPU usage, network traffic, memory consumption, processor temperature, or virtual machine activity affect the performance of the cloud and uncover potential points of vulnerability for operation. Moreover, with AI-powered monitoring one can easily detect abnormal patterns of resource consumption and continuously assess operational risk by combining the monitoring parameters into a composite risk index. In conclusion, the study demonstrates that AI support in monitoring bolsters cybersecurity management practices, enhances infrastructure visibility, aids in early detection of anomalies, and aids in proactive management of operational risks across AWS and Azure cloud platforms. Novelty: This study integrates AI-driven monitoring and anomaly detection with cloud workload analysis to evaluate operational risks across both AWS and Azure environments using the Cloud Data Center Workload Dataset.

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