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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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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 21 Documents
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Search results for , issue "Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science" : 21 Documents clear
Mitigating Phishing Attacks in Healthcare Institutions: A Need for Comprehensive Incidence Response Plan Nkrumah, Albert; Asante, George; Asiedu, William
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.v14i6.4764

Abstract

In recent years, the healthcare industry has witnessed a sharp increase in the number of security breaches, particularly phishing incidents, leading to the compromise of millions of sensitive patient records. The study aimed to explore Phishing Attacks in Healthcare. Specifically, the study seeks to investigate the prevalence of phishing attacks within community hospitals and develop comprehensive incident response plans that outline the steps to be taken in the event of phishing attacks. The study developed comprehensive and effective strategies for mitigating the risk of phishing attacks within Community Hospitals in Kumasi Metropolis. A quantitative research approach was adopted. The target population comprised IT professionals and healthcare administrators of community hospitals in Kumasi Metropolis. From the target population, a total of 9 hospitals were selected, where 97 respondents were used. Simple random and purposive sampling techniques were used in choosing the community hospitals and participants respectively. A structured self-administered questionnaire was utilized to gather the required data. The study revealed a high frequency of community hospital phishing attacks, with 57.7% encountering phishing attacks 1-2 times within 1-2 years, 6.4% experiencing a number of phishing incidents over 3-4 years, and 42.3% experiencing more than 5 phishing attacks within 1-2 years. The findings revealed that community hospitals frequently encounter several types of phishing attacks, including smishing, spear phishing, email phishing, clone phishing, vishing, and whaling attacks. The study concludes that implementing the ACSC Incident Matrix 2022 framework would be instrumental in helping hospitals effectively assess and manage cyber threats. It was recommended that CSA in collaboration with the MoC and Ghana Health Service, should launch national awareness campaigns focusing on the dangers of phishing attacks, particularly within the healthcare sector.
Multiclass Regression for Facial Beauty Prediction Based on Deep Learning Using SCUT-B 5500 Haji, Vaman; Abdulazeez, Adnan Mohsin
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.v14i6.5008

Abstract

FBP, that is, facial beauty prediction, is a fundamental procedure of how beautiful a face person perceives, just like human beings. The challenge focuses on systems that can assess facial features and provide ratings that align with human perceptions of attractiveness. In this paper, we investigate the usage of deep learning techniques using ResNet18 models for predicting beauty of a face using SCUT-B 5500 dataset and share our findings. In the last ten years machine recognition and scoring of attractiveness has developed into a new field through the use of artificial intelligence. We present our exploratory research on constructing a robust model based on a dataset containing 5500 annotated frontal images ranked according to perceived beauty. Multi-task transfer learning was employed to improve the model performance and address the issue of limited data. Our ResNet18 model had an impressive accuracy of over 91% on predicting beauty ratings. Furthermore, this study not only contributes to the field of facial beauty prediction, but it also has the potential to be implemented in multiple fields such as social networks, dating applications, personalized ads.
Hybrid Transfer Learning Model for Facial Attractiveness Prediction Hawar Bahzad Ahmad; Abdulazeez , Adnan Mohsin
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.v14i6.5018

Abstract

Prediction of facial attractiveness greatly depends on the subjective terminology applied according to the diverse cultural, social and psychological considerations. This task is important for applications in many fields, such as aesthetics, entertainment, wardrobe recommendations, etc., and requires accurate and robust models. Current methods predominantly adopt a single model, which is unable to learn the diverse attributes that can influence the quality of facial beauty. In order to overcome these challenges, this study proposes a hybrid transfer learning framework for feature extraction and prediction that combines ResNet50 and InceptionV3. In this methodology, Multi-task Cascaded Convolutional Networks (MTCNN) is used for accurate face detection and preprocessing, then features extraction is done using pretrained ResNet50 and InceptionV3 architectures. The features extracted are then normalized and fused together and passed through a dense classification layer with application of dropouts and regularization in order to make the model robust. The CelebA dataset was used to train the model, utilizing class weights to account for imbalanced data and callbacks to optimize performance. Test accuracy and F1 Score of the proposed model is found to be 83.58% and 0.8384 respectively, which shows good generalization on unseen data. The validation frames the performance of the hybrid framework which leverages the complementary strengths of multiple CNNs, and thus provides robust performance.
An Industry 5.0 Compliant Human-Robot Collaboration Digital Twin Framework for African Medium Scale Enterprises Fasina, Ebun-Oluwa Phillip; Sawyerr, Babatunde Alade; Akinola-Taiwo, Kayodele; Murainah, Abdul-Azeez; Ojiako, Chika Perpetua
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.v14i6.5023

Abstract

Industry 5.0 emphasizes human-robot collaboration, where Digital Twins (DTs) connect physical and digital operations for efficient, flexible work. Existing DT frameworks often focus on full-system autonomy or prediction, overlooking structured, task-level coordination between Human and Robot Digital Twins (HDT and RDT). This paper introduces a minimal, modular framework that enables shared task-based collaboration between HDT and RDT agents. Built on the Cross Domain Digital Twin (CDDT) design pattern, it supports real-time, role-specific interaction. The framework provides a scalable foundation for collaborative DT systems aligned with Industry 5.0, offering a practical base for future human–robot coordination research.
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.
Prototipe Sistem Keamanan Kunci Pintu Rumah Otomatis Dengan Pengenalan Wajah Berbasis IoT Niam Tamami; Achmad Rizky Ramadhani; Hary Oktavianto; Rifqi Nabila Zufar
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.v14i6.4998

Abstract

Dalam era digital yang semakin berkembang, teknologi telah mengubah sistem keamanan dengan menggantikan kunci pintu konvensional dengan kunci pintu pintar yang canggih. Teknologi seperti pengenalan wajah, sidik jari, dan sensor gerak memberikan tingkat keamanan yang lebih tinggi dibandingkan dengan kunci tradisional. Kunci pintu konvensional rentan terhadap risiko pembobolan, kehilangan, atau duplikasi oleh pelaku kejahatan. Sebagai solusi, penulis ingin menciptakan kunci pintu pintar yang dapat membuka pintu dengan pengenalan wajah, Personal Identification Number (PIN), dan melalui Telegram bot. Pengenalan wajah pada kunci pintu pintar memastikan bahwa hanya pemilik atau orang yang terdaftar yang dapat membuka pintu. Fitur tambahan berupa penggunaan PIN memberikan lapisan keamanan ekstra. Jika PIN yang salah dimasukkan sebanyak tiga kali, alarm akan berbunyi untuk memberikan peringatan. Selain itu, jika sistem pengenalan wajah tidak dapat mengenali wajah yang sedang dipindai, kamera akan mengambil gambar dan mengirimkannya ke Telegram bot di smartphone pengguna. Dengan penerapan kunci pintu pintar ini, diharapkan keamanan dan kemudahan akses yang lebih baik dibandingkan dengan kunci pintu konvensional. Hasil pengujian menunjukkan bahwa sistem ini dapat bekerja dengan akurasi pengenalan wajah sebesar 79% dan respon alarm bekerja secara real-time melalui integrasi Telegram bot.
Digital Forensic-Ready Voting Model Muyambo, Edmore; Baror, Stacey; Makura, Sheunesu
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.v14i6.5019

Abstract

The increasing digitalization of elections through internet-based voting (e-voting) systems introduces both opportunities for enhanced accessibility and threats to electoral integrity. Existing electronic voting systems often lack built-in forensic capabilities necessary to detect, preserve, and prove incidents of vote rigging or cyber manipulation. This paper proposes a Digital Forensic-Ready Voting Model (DFRVM) that integrates forensic-by-design principles, blockchain technology, and legal admissibility frameworks to ensure accountability, transparency, and verifiability in the electoral process. The model emphasizes proactive evidence collection, real-time monitoring, and tamper-evident audit trails to strengthen post-election dispute resolution.

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