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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 1,170 Documents
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The Machine Learning Techniques to Detect Social Engineering Attacks in Text-Based Communications: A Systematic Review Thomas Maseko; Michael Moeti; Mokganya, Karabo
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.4982

Abstract

In the digital age, social engineering-type attacks have posed a significant threat in the cybersecurity space. The act of Social Engineering attacks is the act of leveraging psychological manipulation to deceive individuals into divulging confidential information or performing harmful actions. Detecting Social Engineering is challenging due to the emergence of artificial intelligence and contextual subtleties. Therefore, to improve on cybersecurity posture, this systematic review explores (ML) machine learning techniques designed to identify social engineering attacks in text-based communications, by analysing the performance, methodologies, and limitations of ML techniques. Machine learning techniques in the detection of social engineering attacks have progressed from simple lexical classifiers to sophisticated deep contextual models. The engine of this paper is an empirical systematic review that identifies trends, gaps, and strengths in current literature. Paucity speaking on literature, PRIMSA is used for systematically surveying articles aligned with the detection of social engineering attacks using ML techniques. The ability of machine learning techniques to effectively identify various forms of SE attacks and adapt to emerging threats makes ML a great tool in combating Social Engineering attacks. As the volume of digital communication persistently grows at an unprecedented rate, so does the potential of criminals to exploit these channels. The researchers concluded with a recommendation for a comprehensive survey of ML techniques for detecting social engineering attacks in text-based communications.
Adoption of Artificial Intelligence in Competitive Intelligence: A Systematic Literature Review Maune, Alexander
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5035

Abstract

The accelerating convergence of Artificial Intelligence (AI) and Competitive Intelligence (CI) represents a significant transformation in how organisations gather, analyse, and interpret strategic information. As global markets become increasingly dynamic and data-driven, understanding how AI enhances CI processes is critical for both scholars and practitioners. This study conducts a systematic literature review (SLR) using the PRISMA methodology to synthesise and critically evaluate existing research on the integration of AI into CI. Drawing from peer-reviewed articles published between 2000 and 2025, the review identifies four dominant thematic clusters: AI-enabled data acquisition and mining, predictive analytics and machine learning in market forecasting, natural language processing in sentiment and competitor analysis, and ethical, organisational, and interpretative challenges in AI-driven intelligence. Findings reveal that while AI enhances the accuracy, speed, and depth of intelligence analysis, the literature remains fragmented across disciplines, with limited empirical validation and theoretical coherence. Notably, few studies address the human–AI interface, data governance, and contextual applicability in emerging economies. The paper presents an integrative conceptual framework that links AI capabilities with the CI cycle, highlighting avenues for future research, including ethical AI governance, explainable intelligence models, and applications in small and medium-sized enterprises (SMEs). The synthesis underscores that AI does not replace human intelligence but rather augments it—transforming CI into a more anticipatory, adaptive, and strategic function.
Unified Deep and Machine Learning Hybrid Models for Alzheimer’s and Mild Cognitive Impairment Detection Khan, Abdullah; Dzati Athiar Ramli; Saima Anwar Lashari
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5068

Abstract

Alzheimer’s disease (AD), the most common form of dementia, is characterized by progressive neurodegeneration, leading to memory loss and cognitive decline. Recent studies have reported annual conversion rates from amnestic Mild Cognitive Impairment (MCI) to probable AD. With the advent of Magnetic Resonance Imaging (MRI)-based analysis, advancements in machine learning (ML), particularly deep convolutional neural networks (CNNs), have transformed the diagnostic landscape of AD. However, earlier approaches often struggled to accurately distinguish between different MCI stages. To address this limitation, a deep neural network (DNN) model was developed, employing an enhanced artificial neural network (ANN) architecture to classify individuals into three categories: mild Alzheimer’s dementia, MCI, and normal cognition. The proposed DNN model, trained on a Kaggle dataset, achieved an exceptional accuracy of 0.99. In comparison, conventional classifiers such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) achieved accuracies of 0.97, 0.96, 0.96, and 0.96, respectively. Meanwhile, K-Nearest Neighbors (KNN) attained 0.83, Random Forest (RF) achieved 0.95, and Logistic Regression (LR) reached 0.93. Hybrid models combining DNN with SVM and DT (DNN-SVM and DNN-DT) yielded accuracies of 0.79 and 0.64, respectively. These findings highlight the importance of selecting models that balance interpretability with computational efficiency. Overall, this study provides valuable insights into the strengths and limitations of various classification techniques, enabling informed decisions for different datasets and clinical objectives in Alzheimer’s disease diagnosis.
Design of an IoT-Based Snack Vending Machine with QR Code Payment, Automatic Stock Monitoring, and QoS Evaluation Berlina Putri Agustine; I Gede Puja Astawa; Anang Budikarso
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5073

Abstract

The rapid advancement of the Internet of Things (IoT) has enabled the development of smart vending systems with advanced automation and monitoring capabilities. This study details the design and implementation of an IoT-based snack vending machine that incorporates QR code payment, automated stock monitoring, and network Quality of Service (QoS) assessment. The system employs embedded devices and wireless communication to manage transactions, monitor inventory levels in real time, and enable remote supervision. QR code–enabled digital payment is adopted to enhance transaction efficiency and user convenience, while quality of service metrics are evaluated to assess network performance and system reliability. Experimental findings indicate that the system operates reliably, delivers precise stock information, and maintains stable network performance under standard operating conditions. These results suggest that the proposed solution is both practical and effective for smart vending applications and is adaptable to other IoT-based automated service systems.
Meteorological Drought Forecast using Deep Learning and Ensemble Machine Learning: A systematic review literature Phiri, Reatlegile; Esiefarienrhe, Bukohwo Michael; Obagbuwa, Ibidun Christiana
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5077

Abstract

Meteorological drought is commonly defined as a prolonged deficiency in precipitation relative to the climatological norm for a given region. However, limitations in robustly quantifying and monitoring drought severity continue to impede decision-making across multiple sectors. Conventional tools, have exhibited substantial limitations in terms of accuracy, spatial–temporal resolution, and generalizability. This paper presents a systematic literature review (SLR) focusing on emerging applications of machine learning (ML) and deep learning (DL) to prediction and monitoring meteorological drought, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. An initial pool of 79 peer-reviewed articles published between 2021 and 2025 were identified. The review process examined the articles based on predefined inclusion and exclusion criteria, 19 studies were ultimately retained for detailed analysis. Quality assessment scores for these studies ranged from 71.4% to 100%. The review highlights the increasing use of hybrid ML and DL models, which combine modeling paradigms, as an effective strategy to improve drought forecasting performance, exhibit strong predictive capabilities and offer a compelling alternative to traditional single-model approaches.
A Survey of UAVs Detection: From Single UAV to Swarm Abdenneji, Montassar; Tijeni Delleji; Feten Slimeni
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5085

Abstract

During the First World War, the world experienced the innovation of what is called drone, as a tool of espionage. Today we talk about drone swarm, where multiple drones work together to achieve a common goal or task. The use of this type of aircraft has undergone a great evolution and has become a source of problems for countries’ security, which incites researchers to look for solutions against drones. There are several detection methods such as acoustic, radar, visual, and thermal detection. Also, there is radio frequency (RF) detection based on the RF communication links of the drone system. All these methods of drone detection facilitate the mission of the fight against drones. This review presents a comprehensive study of single/multi-UAV (swarm) architectures and offers a critical analysis of detection methods, transitioning from a single to multi-drone context. The paper also highlights several potential research directions providing essential datasets related to the detection techniques. In addition to the theoretical literature review, this work paves the way toward the development of practical counter-UAV systems.
Factors Influencing the Adoption of Multi Factor Authentication in the Public Sector: A Case Study of Indonesia National Single Window Agency Purnomo, Dencaswo; Ghaisani, Amanda; Sensuse, Dana Indra; Lusa, Sofian; Ramlan, Nurcholis; Indrawati, Nur
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5091

Abstract

This study aims to examine the factors that influence the intention and actual use of Multi-Factor Authentication (MFA) in the National Single Window Agency (LNSW). The research model integrates the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) with the addition of the Perceived Security (PS) construct. Data were collected from employees and vendor teams at the LNSW and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The results show that Perceived Ease of Use (PEOU) and Social Influence (SI) have a positive and significant effect on Behavioral Intention (BI). In addition, Perceived Security (PS) does not have a direct effect on Behavioral Intention, but it has a significant positive effect on Perceived Usefulness (PU). Other findings show that Behavioral Intention (BI) is a strong predictor of Actual Usage (AU) of MFA. These results confirm the relevance of the TAM and UTAUT models in explaining the adoption of security technology in the public sector, and emphasize the importance of ease of use and organizational influence in encouraging the adoption of MFA
Factors influencing Users’ Perception on Yangon Bus Service and Origin-Destination Estimation for Bus Trips in Yangon City Khin, May Thu Zar; Kyaw , Nyan Myint; Aye, Moe Thet Thet
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5092

Abstract

Urban public transportation in Yangon faces challenges such as overcrowding, irregular service, and mismatched supply and demand, affecting passenger satisfaction. This study evaluates the Yangon Bus Service (YBS) by combining users’ perception analysis with Origin–Destination (O–D) estimation. Surveys measured passenger satisfaction with service attributes, and O–D matrices assessed travel patterns across the city. Stepwise multiple linear regression showed that comfort (0.107), bus route (0.106), and bus stop cleanliness (0.432) positively influence satisfaction, while bus fare (-0.117) has a negative effect. Travel activity is concentrated in Yangon's central areas, reflecting the city’s monocentric structure and generating high daily bus demand. The findings provide insights to improve YBS by identifying factors affecting satisfaction and travel demand, guiding service enhancements, optimizing operations, and increasing ridership.
A Hybrid Real-Time Hand Gesture Recognition System Using Haar Cascade Detection, HOG Features, and Lightweight CNN Diler N Abdulqader; Othman, Pawan Shivan; Suhail M. Abdulrahman
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5102

Abstract

Hand gesture recognition (HGR) enables natural and contactless interaction between humans and intelligent systems. This paper proposes a real-time gesture recognition framework based on a hybrid architecture combining classical computer vision techniques with deep learning. The system integrates fast hand localization using MediaPipe-based region-of-interest extraction, Histogram of Oriented Gradients (HOG) feature encoding, and a lightweight convolutional neural network (CNN) for gesture classification, followed by temporal stabilization to improve prediction consistency across video frames. A dataset containing 900 gesture images (open, fist, and peace) was automatically collected using a webcam-based acquisition module and divided into training and validation subsets using an 85/15 split with data augmentation. Experimental evaluation includes quantitative performance analysis, ablation studies, and real-time testing. The proposed framework achieves 96.8% accuracy, 96.5% precision, 96.2% recall, and 96.3% F1-score, while maintaining real-time processing speed of approximately 28 FPS.
Kombinasi Kompresi Huffman Coding dan Teknik Least Significant Bit untuk Efisiensi dan Keamanan Penyembunyian Data Puspita Sari, Riska Amelia; Nurul Latifah, Iin Nurul Latifah; Elviana, Maisie; Febriawan, Dimas
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): 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.v15i2.5055

Abstract

The rapid advancement of digital technology requires stronger data protection mechanisms for both transmitted and stored information. One approach to enhance data confidentiality and efficiency is the combination of compression and data hiding techniques. This study aims to analyze the application of Huffman compression and Least Significant Bit (LSB) steganography for embedding text messages into digital images without developing a dedicated application. The text data were first compressed using the Huffman algorithm to reduce their size, and the resulting bitstream was embedded into an image using the LSB technique. The results indicate that Huffman compression successfully reduced data size by more than 80%, allowing the embedding process to be more efficient. The LSB method also demonstrated its ability to conceal messages without introducing noticeable visual distortion in the cover image. The combination of these methods provides an efficient data-hiding process while maintaining the visual quality of the media. This study highlights that compression and steganography techniques can serve as a simple yet effective approach for digital data protection.

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