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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
 102   103   104   105   106 
Five-Factor Authentication System with a Track and Trace Capability for Online Banking Platforms Moepi, Glen; Mathonsi, Topside E.
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4830

Abstract

Online banking is a rapidly growing customer service platform, but increasing cyber threats require constant security improvements. This study developed an enhanced Multi-Factor Authentication (MFA) scheme with track-and-trace capabilities to mitigate risks. The proposed system includes five authentication modalities: username, password, PIN, OTP, biometrics (fingerprint or facial scan), registered smart devices, and a time-locked user location. A major feature is its ability to detect suspicious activities and send alerts via secretly obtained photos and location triangulation. Using design science methodology, three prototype schemes were developed and compared with First National Bank (FNB) and Standard Bank (STD) security systems. Evaluated with Datadog and AppDynamics APM tools, the best prototype achieved 80% security, slightly below FNB and STD’s 90%. It matched them in resource efficiency and outperformed them in response time, averaging 500 milliseconds compared to FNB’s 700 ms and STD’s 1000 ms.
Machine Learning and Explainable AI for Parkinson’s Disease Prediction: A Systematic Review Ndlovu, Belinda; Maguraushe, Kudakwashe; Mabikwa, Otis
The Indonesian Journal of Computer Science Vol. 14 No. 2 (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.v14i2.4837

Abstract

Parkinson's disease is a movement disorder within the nervous system that impacts millions of people across the world. The standard diagnostic methods usually miss early subtle signs of disease which has driven research into Machine Learning (ML) and Explainable Artificial Intelligence (XAI) to develop better predictive models. Following PRISMA guidelines we analyzed 13 studies found in IEEE Xplore, PubMed and ACM concerning different ML methodologies for Parkinson’s disease prediction. Deep learning models using vocal and motor data achieve good accuracy but require more clinical trust and transparency due to their opaque "black-box" nature. SHAP and LIME act as XAI solutions that address transparency issues in model predictions by delivering understandable information regarding model outputs to all users. Current solutions show progress. However, there are multiple complications, including limited and unbalanced datasets alongside accuracy-explainability trade-offs which underline the need for extensive datasets, multidisciplinary teamwork and practical validation.
Analisis Statis dan Dinamis Ransomware: Babuk dan Lockbit 3.0 Kukuh Iskandar Rizqi
The Indonesian Journal of Computer Science Vol. 14 No. 2 (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.v14i2.4839

Abstract

Ransomware remains a significant cybersecurity threat, targeting both private and public sectors with increasing sophistication. This study analyzes Babuk and Lockbit 3.0 ransomware through static and dynamic methods to uncover their technical characteristics and runtime behaviors. Static analysis reveals differences in structural complexity, with Babuk employing a simpler architecture while Lockbit 3.0 incorporates advanced features such as additional sections and dynamic functionality. Dynamic analysis highlights distinct operational strategies, including encryption patterns and registry modifications for persistence and obfuscation. These findings provide critical insights into ransomware behavior, serving as a foundation for developing AI and ML-based detection systems to identify and mitigate evolving threats effectively.
Viseme Morphing and Text-to-Speech Integration for Indonesian News Broadcasting Ardiana, Mirza
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4841

Abstract

Advancements in multimedia technology and artificial intelligence have driven innovation in digital broadcasting, including virtual newsreaders. This study proposes a text-to-speech-based lip-sync animation system specifically for the Indonesian language to improve synchronization between lip movements and speech. The primary challenge in developing this system lies in generating realistic lip animations that correspond with the phonetic structure of Indonesian. The system workflow involves text input, syllable parsing using the Finite State Automata (FSA) method, viseme conversion (viseme morphing), and web-based animation output. Test results show a viseme duration accuracy of 98.5%, voice-lip movement synchronization of 94.26%, and a Mean Opinion Score (MOS) of 77.12%, indicating that the system is reasonably feasible for implementation. Despite minor delays, the system demonstrates strong potential for further development through the integration of Natural Language Processing (NLP) and deep learning, which could improve viseme mapping accuracy and enhance system flexibility across various digital broadcasting platforms.
DESAIN DAN PENGEMBANGAN SECURE INTEGRATION MODEL PADA INTEGRASI LAYANAN MELALUI MINI PROGRAM: STUDI KASUS MOBILE BANKING PT XYZ Khols, Ghiant Masua; Setiadi Yazid
The Indonesian Journal of Computer Science Vol. 14 No. 2 (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.v14i2.4843

Abstract

This research develops a Secure Integration Model based on OAuth 2.0, AES-256 encryption, and Unique Code tokenization to provide a secure integration between PT XYZ's mobile banking and its partners through the Mini Program. The primary objective of this study is to enable seamless login and customer order payments using virtual accounts with a high level of security. The model is designed to ensure that only authorized entities can access sensitive data through robust authentication and authorization mechanisms. The development of this model serves as an essential solution to provide a secure service integration for accessing customer data and processing payments through virtual accounts, which is a critical requirement in the integration between PT XYZ and its partners. Additionally, this model addresses security challenges related to customer data protection and mitigates the risks of cyberattacks such as data theft, transaction manipulation, and credential misuse. Greybox penetration testing is applied to identify potential vulnerabilities in the API Gateway, token authentication, and inter-system communication. The testing results demonstrate that the integration using this model has low vulnerability and meets established security standards. Its implementation is expected to improve the security, efficiency, and scalability of PT XYZ's digital services.
Fusion-based Intelligent Congestion Management algorithm for on-road traffic in smart cities Tshilongamulenzhe, Ndivhuho; P. Du Plessis, Deon; Mathonsi, Topside E.
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4845

Abstract

The concept of Wireless Sensor Networks (WSNs) has garnered significant global attention due to their wide range of applications. According to the IEEE 802.11 standard, WSNs are wireless networks consisting of sensor nodes (SNs) that interconnect via wireless communication links. These SNs are capable of sensing, processing, and wirelessly transmitting data, even in challenging environments. WSNs are primarily utilized for communication in various domains, including smart cities, healthcare, residential areas, and military applications. However, the deployment of WSNs in environments such as smart cities come with some challenges, particularly traffic congestion. Traffic congestion in smart cities, particularly during peak hours, is often caused by a high volume of vehicles traveling at the same time, resulting in delays, accidents, and inefficiencies under various weather conditions, including sunny and rainy days. Therefore, this paper proposes a novel algorithm called the Fusion-based Intelligent Congestion Management (FICM) algorithm, developed through the integration of the Navigation Reference Spatial Data (NRSD) algorithm and Fusion-based Multimodal Abnormal Detection (FMAD) algorithm. The objective of FICM is to mitigate on-road traffic congestion within smart cities effectively. The algorithm’s performance was evaluated using Network Simulator 3 (NS-3) by comparing its effectiveness with the NRSD and FMAD algorithms. Under sunny weather conditions, the NS-3 simulation results revealed that the FICM algorithm achieved an average False Alarm Rate (FAR) of 0.83%, a Mean Time to Detection (MTTD) of 76.0%, and a Detection Rate (DR) of 84.3%, outperforming both the NRSD and FMAD algorithms. Similarly, under rainy weather conditions, the FICM algorithm demonstrated an average FAR of 14.09%, an MTTD of 57.03%, and a DR of 78.04%, surpassing the performance of the NRSD and FMAD algorithms within the smart city environment.
Improvement of the buffered storage assignment technique for the packets switch node Al-janabi, Adel
The Indonesian Journal of Computer Science Vol. 14 No. 2 (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.v14i2.4846

Abstract

Consider the packets switch node's buffer, which is shared by numerous output communications lines. Spreading buffered memory across numerous users minimizes the total amount of storage required to fulfill latencies constraints and the possibility of packets loss. despite this, there can be an issue with assigning buffers memory amongst various users given that specific users who have consumed most overall that memory could limit or restrict accessibility to communications connections for others users, considerably reducing the overall efficiency of the switch node. These are several buffers storage allocation strategies, among which, known as SMA (Share in Minimum Assignment), are being studied in this research to decrease the expenses related to packets denial and postponement, as well as the functioning of the drives and lines of communication. The switch nodes are modelled using a multithreaded queue system with parallel devices of the kind, a memory buffer sharing accordingly to the SMA scheming, and a set amount of memory spaces designated to every device. A mathematical description of the problems of optimising the SMA schemes in regard to the amount of publically available buffer placements is presented with the goal to reduce losses to the system caused by applications disapproval, application queuing latency, and buffers and device operation. The hypothesis about the bounds of the field that contains the global optimal point is proven. A variety of arguments are also provided as a result of the theorem regarding the location of the globally optimal of the function of objective for different switch nodes types and specific instances of SMA.
Identifikasi CSF dalam Implementasi Business Intelligence di Organisasi Nirlaba: Studi Kasus di Indonesia Hakim, Mohammad Luqmanul; Sensuse, Dana Indra; Ni Wayan Trisnawaty
The Indonesian Journal of Computer Science Vol. 14 No. 2 (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.v14i2.4848

Abstract

This study aims to identify and analyze the critical success factors (CSFs) for implementing business intelligence (BI) in nonprofit organizations in Indonesia. As the world's most generous country for seven consecutive years, Indonesia shows significant potential in its nonprofit sector, particularly in managing Zakat, Infaq, Sadaqah, and Waqf (ZISWAF). This research employs a systematic literature review (SLR) and a case study on a nonprofit organization adopting a BI system. The study identifies five categories of critical success factors (CSFs): organizational, technological, data management, human resource, and process factors. The findings aim to contribute theoretically and practically to digital transformation strategies in the nonprofit sector while guiding decision-makers in implementing effective and sustainable BI systems.
Vision-based Obstacle Detection and Motor Speed Control for Autonomous Driving Systems Aye Nilar Win; Zin Mar Lwin; Tin Tin Hla
The Indonesian Journal of Computer Science Vol. 14 No. 2 (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.v14i2.4849

Abstract

Autonomous driving systems rely on robust perception and control mechanisms to navigate safely in dynamic environments. This study presents a vision-based approach for obstacle detection and motor speed control using MobileNet SSD object detection model. The system utilizes a camera module to capture real-time video frames, which are processed to detect and classify obstacles. Based on the detected objects' position and distance, an adaptive motor speed control algorithm adjusts the vehicle's velocity to ensure collision avoidance and smooth navigation. The implementation is tested on a Raspberry Pi-based platform with an integrated motor control system, utilizing PWM signals for speed regulation. MobileNet SSD offers a lightweight, faster alternative for real time inference. Experimental results demonstrate the system’s effectiveness in detecting obstacles and dynamically adjusting speed in response to environmental conditions. This approaches enhance autonomous vehicle safety and efficiency, making it suitable for real-world applications in self-driving technologies.
Drone Detection and Identification Using SDR: Analysis of DJI Mini 2 Drone ID Signals Thi Thi Khaine; May Su Hlaing; Tin Tin Hla
The Indonesian Journal of Computer Science Vol. 14 No. 2 (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.v14i2.4850

Abstract

The increasing adoption of Unmanned Aerial Vehicles (UAVs) for both commercial and recreational purposes has raised significant security and privacy concerns. DJI OcuSync 2.0, a proprietary communication protocol used in DJI drones, enables high-definition video transmission and telemetry over dual-frequency bands (2.4 GHz and 5.8 GHz). Detecting and identifying OcuSync signals in a crowded RF environment is crucial for effective drone monitoring and threat mitigation. This study presents an SDR-based detection system utilizing the USRP B210 with a 50 MHz sampling rate to capture OcuSync signals. Signal analysis is performed using Short-Time Fourier Transform (STFT) and Welch’s method for estimating Power Spectral Density (PSD). A Non-Parametric Amplitude Quantization Method (NPAQM) is implemented for dynamic threshold estimation to improve detection sensitivity. The system is tested under varying Signal-to-Noise Ratio (SNR) conditions, demonstrating high detection accuracy and robustness against interference. The proposed system provides a reliable framework for real-time OcuSync signal identification and can be adapted for broader UAV detection applications.

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