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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,114 Documents
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Development of an LSTM-Based Power Monitoring and Prediction System for Campus Electrical Facilities Using ESP32 and PM2120 Sholikhah, Evi Nafiatus; Oktavia Rizqi Kurniawan; Dimas Pristovani Riananda; Mustika Kurnia Mayangsari; Rohmad Hadi Handayani
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5030

Abstract

This study develops a data acquisition system for monitoring, detecting, and forecasting electrical energy consumption to support efficient energy management. Electrical parameters such as voltage, current, and power are measured using a PM2120 power meter via Modbus RTU RS485 and processed by an ESP32 microcontroller. The data are displayed in real-time through a Nextion Human-Machine Interface (HMI) and utilized as input for a Long Short-Term Memory (LSTM) model trained on historical consumption data. Safety features include LED indicators that activate when current reaches 80% of maximum capacity and a buzzer that signals threshold violations. Experimental results demonstrate high prediction accuracy, with RMSE values of 0.38 kW (5.32%) for phase R, 0.47 kW (7.55%) for phase S, and 0.28 kW (5.39%) for phase T. Transmission latency averages two to three seconds, while prediction computation is under 10 seconds. The system effectively reflects consumption trends, making it a reliable decision-support tool for enhancing energy efficiency in small- to medium-scale installations.
Why Generative AI Will Not Replace University Lecturers: A Human-Centred Perspective Murimo Bethel Mutanga; Revesai, Zvinodashe; Samuel Chikasha; Tarirai Chani
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5037

Abstract

The integration of artificial intelligence (AI) into higher education has prompted widespread speculation about the potential obsolescence of university lecturers. While AI systems demonstrate impressive capabilities in content delivery, assessment, and personalisation, this research critically examines the assumption that they can replace human educators. This issue is particularly complex, given that effective higher education involves not only the transmission of information but also the development of cognitive, emotional, ethical, and social aspects. Despite advances in AI technologies, current discourse often neglects the irreplaceable human functions that underpin transformative education. Addressing this gap, the study adopts a human-centred framework to investigate essential lecturer capabilities, limitations of AI systems, and the design of optimal human-AI collaboration. Using qualitative methods, including stakeholder interviews and comparative institutional analysis, the findings reveal ten educational domains where human capabilities remain indispensable, from emotional support and ethical mentorship to adaptive teaching and research integration. AI excels in routine, scalable tasks, yet lacks empathy, moral agency, and contextual understanding. Consequently, this research proposes a collaborative model in which AI enhances rather than replaces lecturers, thereby supporting educational quality and student development. The findings have significant implications for institutional policy, faculty development, and the ethical integration of AI in education, affirming the enduring and transformative role of human educators in the digital age.
Hybrid-Based Multi-Object Tracking for Football Sport Htun, Zin Mar; Theingi Myint
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5039

Abstract

Tracking is now popular in real world. Precise tracking of objects in real-time videos is a challenging task. With billions of fans, football is a rapidly expanding sport that has proven essential to many nations and their citizens in particular. None of the numerous great target tracking algorithms have surfaced in recent years primarily deep learning and correlation filtering that can track players in soccer game videos with high accuracy. In this paper, the proposed system is used You Only Look Once version 8-nano (YOLOv8n) for Multi-Object Detection (MOD) to get higher detection accuracy results. Moreover, this system is based on the hybrid method for tracking. The hybrid method is combined with stacked Long Short Term Memory (LSTM) and Fairness of Detection and Re-identification in Multipe Object Tracking (FairMOT). The experimental analysis shows that the proposed system is efficiently and better accuacy because the best detection results with YOLOv8n is 93% for precision, 91% for recall and 92% for mAP(50) with own dataset. After using the proposed system, the average of the Multi Object Tracking Accuracy (MOTA) is 80 % at IoU-Threshold 0.5, the average of the Multi Object Tracking Precision (MOTP) is 89% at IoU-Threshold 0.8 and the average of the final mAP is 96% at IoU- Threshold 0.5 by using hybrid method for tracking.
The Rise of Quantum Computing and Its Impact on Cybersecurity Vareta, Passmore; Muzenda, Hillary; Nyamupaguma, Tanyaradzwa; Dube, Yangekile; Ndlovu, Belinda
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5040

Abstract

As technology continues to evolve, cybersecurity measures tend to be vulnerable to the computational power of quantum computers. These computers perform calculations faster than classical computers. This ability to solve tasks within polynomial time threatens current cybersecurity practices through Shor's and Grover's algorithms. Classical computers rely on mathematical hardness assumptions and are vulnerable to quantum attacks. This paper scrutinizes the double effects of quantum computing on cybersecurity and its ability to support post-quantum resistant technologies. A systematic literature review (SLR) of 24 peer-reviewed articles (2021-2025) obtained from IEEE Xplore, SpringerLink, ACM, and Google Scholar was conducted, and the results identified three integral themes. Firstly, 80% of quantum computing threats studies analysed prove that Shor's algorithm can efficiently factorise large integers, rendering Rivest Shamir Alderman and Elliptic Curve Cryptography obsolete. Secondly, 65% of the studies show that Post-Quantum Cryptography (PQC) offers quantum-resilience in the foreseeable future. In comparison, 25% of Quantum Key Distribution (QKD) papers show practical barriers like signal loss and standardization delays. 15% of studies reveal the urgent need for regulatory and ethical concerns. Key results highlight the urgent need for hybrid cryptographic systems that combine quantum key distribution and post-quantum cryptography, as proposed by 40% of recent publications. 46% of studies show that Europe leads quantum cybersecurity research, driven by collaborative policy efforts. This study suggests practical recommendations for accelerated adoption of NIST-standardised PQC algorithms, investment in QKD infrastructure for critical sectors, and multidisciplinary collaboration to address technical, legal, and ethical gaps. This paper provides a roadmap for mitigating quantum threats and leveraging quantum technologies to transform cybersecurity resilience in the digital era.

Page 112 of 112 | Total Record : 1114

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